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How to Write a Research Paper: the LEAP approach (+cheat sheet)

In this article I will show you how to write a research paper using the four LEAP writing steps. The LEAP academic writing approach is a step-by-step method for turning research results into a published paper .

The LEAP writing approach has been the cornerstone of the 70 + research papers that I have authored and the 3700+ citations these paper have accumulated within 9 years since the completion of my PhD. I hope the LEAP approach will help you just as much as it has helped me to make an real, tangible impact with my research.

What is the LEAP research paper writing approach?

I designed the LEAP writing approach not only for merely writing the papers. My goal with the writing system was to show young scientists how to first think about research results and then how to efficiently write each section of the research paper.

In other words, you will see how to write a research paper by first analyzing the results and then building a logical, persuasive arguments. In this way, instead of being afraid of writing research paper, you will be able to rely on the paper writing process to help you with what is the most demanding task in getting published – thinking.

The four research paper writing steps according to the LEAP approach:

I will show each of these steps in detail. And you will be able to download the LEAP cheat sheet for using with every paper you write.

But before I tell you how to efficiently write a research paper, I want to show you what is the problem with the way scientists typically write a research paper and why the LEAP approach is more efficient.

How scientists typically write a research paper (and why it isn’t efficient)

Writing a research paper can be tough, especially for a young scientist. Your reasoning needs to be persuasive and thorough enough to convince readers of your arguments. The description has to be derived from research evidence, from prior art, and from your own judgment. This is a tough feat to accomplish.

The figure below shows the sequence of the different parts of a typical research paper. Depending on the scientific journal, some sections might be merged or nonexistent, but the general outline of a research paper will remain very similar.

Here is the problem: Most people make the mistake of writing in this same sequence.

While the structure of scientific articles is designed to help the reader follow the research, it does little to help the scientist write the paper. This is because the layout of research articles starts with the broad (introduction) and narrows down to the specifics (results). See in the figure below how the research paper is structured in terms of the breath of information that each section entails.

How to write a research paper according to the LEAP approach

For a scientist, it is much easier to start writing a research paper with laying out the facts in the narrow sections (i.e. results), step back to describe them (i.e. write the discussion), and step back again to explain the broader picture in the introduction.

For example, it might feel intimidating to start writing a research paper by explaining your research’s global significance in the introduction, while it is easy to plot the figures in the results. When plotting the results, there is not much room for wiggle: the results are what they are.

Starting to write a research papers from the results is also more fun because you finally get to see and understand the complete picture of the research that you have worked on.

Most importantly, following the LEAP approach will help you first make sense of the results yourself and then clearly communicate them to the readers. That is because the sequence of writing allows you to slowly understand the meaning of the results and then develop arguments for presenting to your readers.

I have personally been able to write and submit a research article in three short days using this method.

Step 1: Lay Out the Facts

You have worked long hours on a research project that has produced results and are no doubt curious to determine what they exactly mean. There is no better way to do this than by preparing figures, graphics and tables. This is what the first LEAP step is focused on – diving into the results.

How to p repare charts and tables for a research paper

Your first task is to try out different ways of visually demonstrating the research results. In many fields, the central items of a journal paper will be charts that are based on the data generated during research. In other fields, these might be conceptual diagrams, microscopy images, schematics and a number of other types of scientific graphics which should visually communicate the research study and its results to the readers. If you have reasonably small number of data points, data tables might be useful as well.

Tips for preparing charts and tables

• Try multiple chart types but in the finished paper only use the one that best conveys the message you want to present to the readers
• Follow the eight chart design progressions for selecting and refining a data chart for your paper: https://peerrecognized.com/chart-progressions
• Prepare scientific graphics and visualizations for your paper using the scientific graphic design cheat sheet: https://peerrecognized.com/tools-for-creating-scientific-illustrations/

How to describe the results of your research

Now that you have your data charts, graphics and tables laid out in front of you – describe what you see in them. Seek to answer the question: What have I found?  Your statements should progress in a logical sequence and be backed by the visual information. Since, at this point, you are simply explaining what everyone should be able to see for themselves, you can use a declarative tone: The figure X demonstrates that…

Tips for describing the research results :

• Answer the question: “ What have I found? “
• Use declarative tone since you are simply describing observations

Step 2: Explain the results

The core aspect of your research paper is not actually the results; it is the explanation of their meaning. In the second LEAP step, you will do some heavy lifting by guiding the readers through the results using logic backed by previous scientific research.

How to define the Message of a research paper

To define the central message of your research paper, imagine how you would explain your research to a colleague in 20 seconds . If you succeed in effectively communicating your paper’s message, a reader should be able to recount your findings in a similarly concise way even a year after reading it. This clarity will increase the chances that someone uses the knowledge you generated, which in turn raises the likelihood of citations to your research paper. 

Tips for defining the paper’s central message :

• Write the paper’s core message in a single sentence or two bullet points
• Write the core message in the header of the research paper manuscript

How to write the Discussion section of a research paper

In the discussion section you have to demonstrate why your research paper is worthy of publishing. In other words, you must now answer the all-important So what? question . How well you do so will ultimately define the success of your research paper.

Here are three steps to get started with writing the discussion section:

• Write bullet points of the things that convey the central message of the research article (these may evolve into subheadings later on).
• Make a list with the arguments or observations that support each idea.
• Finally, expand on each point to make full sentences and paragraphs.

Tips for writing the discussion section:

• What is the meaning of the results?
• Was the hypothesis confirmed?
• Write bullet points that support the core message
• List logical arguments for each bullet point, group them into sections
• Instead of repeating research timeline, use a presentation sequence that best supports your logic
• Convert arguments to full paragraphs; be confident but do not overhype
• Refer to both supportive and contradicting research papers for maximum credibility

How to write the Conclusions of a research paper

Since some readers might just skim through your research paper and turn directly to the conclusions, it is a good idea to make conclusion a standalone piece. In the first few sentences of the conclusions, briefly summarize the methodology and try to avoid using abbreviations (if you do, explain what they mean).

After this introduction, summarize the findings from the discussion section. Either paragraph style or bullet-point style conclusions can be used. I prefer the bullet-point style because it clearly separates the different conclusions and provides an easy-to-digest overview for the casual browser. It also forces me to be more succinct.

Tips for writing the conclusion section :

• Summarize the key findings, starting with the most important one
• Make conclusions standalone (short summary, avoid abbreviations)
• Add an optional take-home message and suggest future research in the last paragraph

How to refine the Objective of a research paper

The objective is a short, clear statement defining the paper’s research goals. It can be included either in the final paragraph of the introduction, or as a separate subsection after the introduction. Avoid writing long paragraphs with in-depth reasoning, references, and explanation of methodology since these belong in other sections. The paper’s objective can often be written in a single crisp sentence.

Tips for writing the objective section :

• The objective should ask the question that is answered by the central message of the research paper
• The research objective should be clear long before writing a paper. At this point, you are simply refining it to make sure it is addressed in the body of the paper.

How to write the Methodology section of your research paper

When writing the methodology section, aim for a depth of explanation that will allow readers to reproduce the study . This means that if you are using a novel method, you will have to describe it thoroughly. If, on the other hand, you applied a standardized method, or used an approach from another paper, it will be enough to briefly describe it with reference to the detailed original source.

Remember to also detail the research population, mention how you ensured representative sampling, and elaborate on what statistical methods you used to analyze the results.

Tips for writing the methodology section :

• Include enough detail to allow reproducing the research
• Provide references if the methods are known
• Create a methodology flow chart to add clarity
• Describe the research population, sampling methodology, statistical methods for result analysis
• Describe what methodology, test methods, materials, and sample groups were used in the research.

Step 3: Advertize the research

Step 3 of the LEAP writing approach is designed to entice the casual browser into reading your research paper. This advertising can be done with an informative title, an intriguing abstract, as well as a thorough explanation of the underlying need for doing the research within the introduction.

How to write the Introduction of a research paper

The introduction section should leave no doubt in the mind of the reader that what you are doing is important and that this work could push scientific knowledge forward. To do this convincingly, you will need to have a good knowledge of what is state-of-the-art in your field. You also need be able to see the bigger picture in order to demonstrate the potential impacts of your research work.

Think of the introduction as a funnel, going from wide to narrow, as shown in the figure below:

• Start with a brief context to explain what do we already know,
• Follow with the motivation for the research study and explain why should we care about it,
• Explain the research gap you are going to bridge within this research paper,
• Describe the approach you will take to solve the problem.

Tips for writing the introduction section :

• Follow the Context – Motivation – Research gap – Approach funnel for writing the introduction
• Explain how others tried and how you plan to solve the research problem
• Do a thorough literature review before writing the introduction
• Start writing the introduction by using your own words, then add references from the literature

How to prepare the Abstract of a research paper

The abstract acts as your paper’s elevator pitch and is therefore best written only after the main text is finished. In this one short paragraph you must convince someone to take on the time-consuming task of reading your whole research article. So, make the paper easy to read, intriguing, and self-explanatory; avoid jargon and abbreviations.

How to structure the abstract of a research paper:

• The abstract is a single paragraph that follows this structure:
• Problem: why did we research this
• Methodology: typically starts with the words “Here we…” that signal the start of own contribution.
• Results: what we found from the research.
• Conclusions: show why are the findings important

How to compose a research paper Title

The title is the ultimate summary of a research paper. It must therefore entice someone looking for information to click on a link to it and continue reading the article. A title is also used for indexing purposes in scientific databases, so a representative and optimized title will play large role in determining if your research paper appears in search results at all.

Tips for coming up with a research paper title:

• Capture curiosity of potential readers using a clear and descriptive title
• Include broad terms that are often searched
• Add details that uniquely identify the researched subject of your research paper
• Avoid jargon and abbreviations
• Use keywords as title extension (instead of duplicating the words) to increase the chance of appearing in search results

How to prepare Highlights and Graphical Abstract

Highlights are three to five short bullet-point style statements that convey the core findings of the research paper. Notice that the focus is on the findings, not on the process of getting there.

A graphical abstract placed next to the textual abstract visually summarizes the entire research paper in a single, easy-to-follow figure. I show how to create a graphical abstract in my book Research Data Visualization and Scientific Graphics.

Tips for preparing highlights and graphical abstract:

• In highlights show core findings of the research paper (instead of what you did in the study).
• In graphical abstract show take-home message or methodology of the research paper. Learn more about creating a graphical abstract in this article.

Step 4: Prepare for submission

Sometimes it seems that nuclear fusion will stop on the star closest to us (read: the sun will stop to shine) before a submitted manuscript is published in a scientific journal. The publication process routinely takes a long time, and after submitting the manuscript you have very little control over what happens. To increase the chances of a quick publication, you must do your homework before submitting the manuscript. In the fourth LEAP step, you make sure that your research paper is published in the most appropriate journal as quickly and painlessly as possible.

How to select a scientific Journal for your research paper

The best way to find a journal for your research paper is it to review which journals you used while preparing your manuscript. This source listing should provide some assurance that your own research paper, once published, will be among similar articles and, thus, among your field’s trusted sources.

After this initial selection of hand-full of scientific journals, consider the following six parameters for selecting the most appropriate journal for your research paper (read this article to review each step in detail):

• Scope and publishing history
• Ranking and Recognition
• Publishing time
• Acceptance rate
• Content requirements
• Access and Fees

How to select a journal for your research paper:

• Use the six parameters to select the most appropriate scientific journal for your research paper
• Use the following tools for journal selection: https://peerrecognized.com/journals
• Follow the journal’s “Authors guide” formatting requirements

How to Edit you manuscript

No one can write a finished research paper on their first attempt. Before submitting, make sure to take a break from your work for a couple of days, or even weeks. Try not to think about the manuscript during this time. Once it has faded from your memory, it is time to return and edit. The pause will allow you to read the manuscript from a fresh perspective and make edits as necessary.

I have summarized the most useful research paper editing tools in this article.

Tips for editing a research paper:

• Take time away from the research paper to forget about it; then returning to edit,
• Start by editing the content: structure, headings, paragraphs, logic, figures
• Continue by editing the grammar and language; perform a thorough language check using academic writing tools
• Read the entire paper out loud and correct what sounds weird

How to write a compelling Cover Letter for your paper

Begin the cover letter by stating the paper’s title and the type of paper you are submitting (review paper, research paper, short communication). Next, concisely explain why your study was performed, what was done, and what the key findings are. State why the results are important and what impact they might have in the field. Make sure you mention how your approach and findings relate to the scope of the journal in order to show why the article would be of interest to the journal’s readers.

I wrote a separate article that explains what to include in a cover letter here. You can also download a cover letter template from the article.

Tips for writing a cover letter:

• Explain how the findings of your research relate to journal’s scope
• Tell what impact the research results will have
• Show why the research paper will interest the journal’s audience
• Add any legal statements as required in journal’s guide for authors

How to Answer the Reviewers

Reviewers will often ask for new experiments, extended discussion, additional details on the experimental setup, and so forth. In principle, your primary winning tactic will be to agree with the reviewers and follow their suggestions whenever possible. After all, you must earn their blessing in order to get your paper published.

Be sure to answer each review query and stick to the point. In the response to the reviewers document write exactly where in the paper you have made any changes. In the paper itself, highlight the changes using a different color. This way the reviewers are less likely to re-read the entire article and suggest new edits.

In cases when you don’t agree with the reviewers, it makes sense to answer more thoroughly. Reviewers are scientifically minded people and so, with enough logical and supported argument, they will eventually be willing to see things your way.

Tips for answering the reviewers:

• Agree with most review comments, but if you don’t, thoroughly explain why
• Highlight changes in the manuscript
• Do not take the comments personally and cool down before answering

The LEAP research paper writing cheat sheet

Imagine that you are back in grad school and preparing to take an exam on the topic: “How to write a research paper”. As an exemplary student, you would, most naturally, create a cheat sheet summarizing the subject… Well, I did it for you.

This one-page summary of the LEAP research paper writing technique will remind you of the key research paper writing steps. Print it out and stick it to a wall in your office so that you can review it whenever you are writing a new research paper.

Now that we have gone through the four LEAP research paper writing steps, I hope you have a good idea of how to write a research paper. It can be an enjoyable process and once you get the hang of it, the four LEAP writing steps should even help you think about and interpret the research results. This process should enable you to write a well-structured, concise, and compelling research paper.

Have fund with writing your next research paper. I hope it will turn out great!

Learn writing papers that get cited

The LEAP writing approach is a blueprint for writing research papers. But to be efficient and write papers that get cited, you need more than that.

My name is Martins Zaumanis and in my interactive course Research Paper Writing Masterclass I will show you how to  visualize  your research results,  frame a message  that convinces your readers, and write  each section  of the paper. Step-by-step.

And of course – you will learn to respond the infamous  Reviewer No.2.

Hey! My name is Martins Zaumanis and I am a materials scientist in Switzerland ( Google Scholar ). As the first person in my family with a PhD, I have first-hand experience of the challenges starting scientists face in academia. With this blog, I want to help young researchers succeed in academia. I call the blog “Peer Recognized”, because peer recognition is what lifts academic careers and pushes science forward.

Besides this blog, I have written the Peer Recognized book series and created the Peer Recognized Academy offering interactive online courses.

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• How to write a research paper

Last updated

11 January 2024

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With proper planning, knowledge, and framework, completing a research paper can be a fulfilling and exciting experience. 

Though it might initially sound slightly intimidating, this guide will help you embrace the challenge. 

By documenting your findings, you can inspire others and make a difference in your field. Here's how you can make your research paper unique and comprehensive.

• What is a research paper?

Research papers allow you to demonstrate your knowledge and understanding of a particular topic. These papers are usually lengthier and more detailed than typical essays, requiring deeper insight into the chosen topic.

To write a research paper, you must first choose a topic that interests you and is relevant to the field of study. Once you’ve selected your topic, gathering as many relevant resources as possible, including books, scholarly articles, credible websites, and other academic materials, is essential. You must then read and analyze these sources, summarizing their key points and identifying gaps in the current research.

You can formulate your ideas and opinions once you thoroughly understand the existing research. To get there might involve conducting original research, gathering data, or analyzing existing data sets. It could also involve presenting an original argument or interpretation of the existing research.

Writing a successful research paper involves presenting your findings clearly and engagingly, which might involve using charts, graphs, or other visual aids to present your data and using concise language to explain your findings. You must also ensure your paper adheres to relevant academic formatting guidelines, including proper citations and references.

Overall, writing a research paper requires a significant amount of time, effort, and attention to detail. However, it is also an enriching experience that allows you to delve deeply into a subject that interests you and contribute to the existing body of knowledge in your chosen field.

• How long should a research paper be?

Research papers are deep dives into a topic. Therefore, they tend to be longer pieces of work than essays or opinion pieces. 

However, a suitable length depends on the complexity of the topic and your level of expertise. For instance, are you a first-year college student or an experienced professional? 

Also, remember that the best research papers provide valuable information for the benefit of others. Therefore, the quality of information matters most, not necessarily the length. Being concise is valuable.

Following these best practice steps will help keep your process simple and productive:

1. Gaining a deep understanding of any expectations

Before diving into your intended topic or beginning the research phase, take some time to orient yourself. Suppose there’s a specific topic assigned to you. In that case, it’s essential to deeply understand the question and organize your planning and approach in response. Pay attention to the key requirements and ensure you align your writing accordingly. 

This preparation step entails

Deeply understanding the task or assignment

Being clear about the expected format and length

Familiarizing yourself with the citation and referencing requirements 

Understanding any defined limits for your research contribution

Where applicable, speaking to your professor or research supervisor for further clarification

2. Choose your research topic

Select a research topic that aligns with both your interests and available resources. Ideally, focus on a field where you possess significant experience and analytical skills. In crafting your research paper, it's crucial to go beyond summarizing existing data and contribute fresh insights to the chosen area.

Consider narrowing your focus to a specific aspect of the topic. For example, if exploring the link between technology and mental health, delve into how social media use during the pandemic impacts the well-being of college students. Conducting interviews and surveys with students could provide firsthand data and unique perspectives, adding substantial value to the existing knowledge.

When finalizing your topic, adhere to legal and ethical norms in the relevant area (this ensures the integrity of your research, protects participants' rights, upholds intellectual property standards, and ensures transparency and accountability). Following these principles not only maintains the credibility of your work but also builds trust within your academic or professional community.

For instance, in writing about medical research, consider legal and ethical norms , including patient confidentiality laws and informed consent requirements. Similarly, if analyzing user data on social media platforms, be mindful of data privacy regulations, ensuring compliance with laws governing personal information collection and use. Aligning with legal and ethical standards not only avoids potential issues but also underscores the responsible conduct of your research.

3. Gather preliminary research

Once you’ve landed on your topic, it’s time to explore it further. You’ll want to discover more about available resources and existing research relevant to your assignment at this stage. 

This exploratory phase is vital as you may discover issues with your original idea or realize you have insufficient resources to explore the topic effectively. This key bit of groundwork allows you to redirect your research topic in a different, more feasible, or more relevant direction if necessary. 

Spending ample time at this stage ensures you gather everything you need, learn as much as you can about the topic, and discover gaps where the topic has yet to be sufficiently covered, offering an opportunity to research it further. 

4. Define your research question

To produce a well-structured and focused paper, it is imperative to formulate a clear and precise research question that will guide your work. Your research question must be informed by the existing literature and tailored to the scope and objectives of your project. By refining your focus, you can produce a thoughtful and engaging paper that effectively communicates your ideas to your readers.

5. Write a thesis statement

A thesis statement is a one-to-two-sentence summary of your research paper's main argument or direction. It serves as an overall guide to summarize the overall intent of the research paper for you and anyone wanting to know more about the research.

A strong thesis statement is:

Concise and clear: Explain your case in simple sentences (avoid covering multiple ideas). It might help to think of this section as an elevator pitch.

Specific: Ensure that there is no ambiguity in your statement and that your summary covers the points argued in the paper.

Debatable: A thesis statement puts forward a specific argument––it is not merely a statement but a debatable point that can be analyzed and discussed.

Here are three thesis statement examples from different disciplines:

Psychology thesis example: "We're studying adults aged 25-40 to see if taking short breaks for mindfulness can help with stress. Our goal is to find practical ways to manage anxiety better."

Environmental science thesis example: "This research paper looks into how having more city parks might make the air cleaner and keep people healthier. I want to find out if more green spaces means breathing fewer carcinogens in big cities."

UX research thesis example: "This study focuses on improving mobile banking for older adults using ethnographic research, eye-tracking analysis, and interactive prototyping. We investigate the usefulness of eye-tracking analysis with older individuals, aiming to spark debate and offer fresh perspectives on UX design and digital inclusivity for the aging population."

6. Conduct in-depth research

A research paper doesn’t just include research that you’ve uncovered from other papers and studies but your fresh insights, too. You will seek to become an expert on your topic––understanding the nuances in the current leading theories. You will analyze existing research and add your thinking and discoveries.  It's crucial to conduct well-designed research that is rigorous, robust, and based on reliable sources. Suppose a research paper lacks evidence or is biased. In that case, it won't benefit the academic community or the general public. Therefore, examining the topic thoroughly and furthering its understanding through high-quality research is essential. That usually means conducting new research. Depending on the area under investigation, you may conduct surveys, interviews, diary studies , or observational research to uncover new insights or bolster current claims.

7. Determine supporting evidence

Not every piece of research you’ve discovered will be relevant to your research paper. It’s important to categorize the most meaningful evidence to include alongside your discoveries. It's important to include evidence that doesn't support your claims to avoid exclusion bias and ensure a fair research paper.

8. Write a research paper outline

Before diving in and writing the whole paper, start with an outline. It will help you to see if more research is needed, and it will provide a framework by which to write a more compelling paper. Your supervisor may even request an outline to approve before beginning to write the first draft of the full paper. An outline will include your topic, thesis statement, key headings, short summaries of the research, and your arguments.

9. Write your first draft

Once you feel confident about your outline and sources, it’s time to write your first draft. While penning a long piece of content can be intimidating, if you’ve laid the groundwork, you will have a structure to help you move steadily through each section. To keep up motivation and inspiration, it’s often best to keep the pace quick. Stopping for long periods can interrupt your flow and make jumping back in harder than writing when things are fresh in your mind.

10. Cite your sources correctly

It's always a good practice to give credit where it's due, and the same goes for citing any works that have influenced your paper. Building your arguments on credible references adds value and authenticity to your research. In the formatting guidelines section, you’ll find an overview of different citation styles (MLA, CMOS, or APA), which will help you meet any publishing or academic requirements and strengthen your paper's credibility. It is essential to follow the guidelines provided by your school or the publication you are submitting to ensure the accuracy and relevance of your citations.

11. Ensure your work is original

It is crucial to ensure the originality of your paper, as plagiarism can lead to serious consequences. To avoid plagiarism, you should use proper paraphrasing and quoting techniques. Paraphrasing is rewriting a text in your own words while maintaining the original meaning. Quoting involves directly citing the source. Giving credit to the original author or source is essential whenever you borrow their ideas or words. You can also use plagiarism detection tools such as Scribbr or Grammarly to check the originality of your paper. These tools compare your draft writing to a vast database of online sources. If you find any accidental plagiarism, you should correct it immediately by rephrasing or citing the source.

12. Revise, edit, and proofread

One of the essential qualities of excellent writers is their ability to understand the importance of editing and proofreading. Even though it's tempting to call it a day once you've finished your writing, editing your work can significantly improve its quality. It's natural to overlook the weaker areas when you've just finished writing a paper. Therefore, it's best to take a break of a day or two, or even up to a week, to refresh your mind. This way, you can return to your work with a new perspective. After some breathing room, you can spot any inconsistencies, spelling and grammar errors, typos, or missing citations and correct them. 

• The best research paper format 

The format of your research paper should align with the requirements set forth by your college, school, or target publication. 

There is no one “best” format, per se. Depending on the stated requirements, you may need to include the following elements:

Title page: The title page of a research paper typically includes the title, author's name, and institutional affiliation and may include additional information such as a course name or instructor's name. 

Table of contents: Include a table of contents to make it easy for readers to find specific sections of your paper.

Abstract: The abstract is a summary of the purpose of the paper.

Methods : In this section, describe the research methods used. This may include collecting data , conducting interviews, or doing field research .

Results: Summarize the conclusions you drew from your research in this section.

Discussion: In this section, discuss the implications of your research . Be sure to mention any significant limitations to your approach and suggest areas for further research.

Tables, charts, and illustrations: Use tables, charts, and illustrations to help convey your research findings and make them easier to understand.

Works cited or reference page: Include a works cited or reference page to give credit to the sources that you used to conduct your research.

Bibliography: Provide a list of all the sources you consulted while conducting your research.

Dedication and acknowledgments : Optionally, you may include a dedication and acknowledgments section to thank individuals who helped you with your research.

• General style and formatting guidelines

Formatting your research paper means you can submit it to your college, journal, or other publications in compliance with their criteria.

Research papers tend to follow the American Psychological Association (APA), Modern Language Association (MLA), or Chicago Manual of Style (CMOS) guidelines.

Here’s how each style guide is typically used:

Chicago Manual of Style (CMOS):

CMOS is a versatile style guide used for various types of writing. It's known for its flexibility and use in the humanities. CMOS provides guidelines for citations, formatting, and overall writing style. It allows for both footnotes and in-text citations, giving writers options based on their preferences or publication requirements.

American Psychological Association (APA):

APA is common in the social sciences. It’s hailed for its clarity and emphasis on precision. It has specific rules for citing sources, creating references, and formatting papers. APA style uses in-text citations with an accompanying reference list. It's designed to convey information efficiently and is widely used in academic and scientific writing.

Modern Language Association (MLA):

MLA is widely used in the humanities, especially literature and language studies. It emphasizes the author-page format for in-text citations and provides guidelines for creating a "Works Cited" page. MLA is known for its focus on the author's name and the literary works cited. It’s frequently used in disciplines that prioritize literary analysis and critical thinking.

To confirm you're using the latest style guide, check the official website or publisher's site for updates, consult academic resources, and verify the guide's publication date. Online platforms and educational resources may also provide summaries and alerts about any revisions or additions to the style guide.

Citing sources

When working on your research paper, it's important to cite the sources you used properly. Your citation style will guide you through this process. Generally, there are three parts to citing sources in your research paper: 

First, provide a brief citation in the body of your essay. This is also known as a parenthetical or in-text citation. 

Second, include a full citation in the Reference list at the end of your paper. Different types of citations include in-text citations, footnotes, and reference lists. 

In-text citations include the author's surname and the date of the citation. 

Footnotes appear at the bottom of each page of your research paper. They may also be summarized within a reference list at the end of the paper. 

A reference list includes all of the research used within the paper at the end of the document. It should include the author, date, paper title, and publisher listed in the order that aligns with your citation style.

10 research paper writing tips:

Following some best practices is essential to writing a research paper that contributes to your field of study and creates a positive impact.

These tactics will help you structure your argument effectively and ensure your work benefits others:

Clear and precise language:  Ensure your language is unambiguous. Use academic language appropriately, but keep it simple. Also, provide clear takeaways for your audience.

Effective idea separation:  Organize the vast amount of information and sources in your paper with paragraphs and titles. Create easily digestible sections for your readers to navigate through.

Compelling intro:  Craft an engaging introduction that captures your reader's interest. Hook your audience and motivate them to continue reading.

Thorough revision and editing:  Take the time to review and edit your paper comprehensively. Use tools like Grammarly to detect and correct small, overlooked errors.

Thesis precision:  Develop a clear and concise thesis statement that guides your paper. Ensure that your thesis aligns with your research's overall purpose and contribution.

Logical flow of ideas:  Maintain a logical progression throughout the paper. Use transitions effectively to connect different sections and maintain coherence.

Critical evaluation of sources:  Evaluate and critically assess the relevance and reliability of your sources. Ensure that your research is based on credible and up-to-date information.

Thematic consistency:  Maintain a consistent theme throughout the paper. Ensure that all sections contribute cohesively to the overall argument.

Relevant supporting evidence:  Provide concise and relevant evidence to support your arguments. Avoid unnecessary details that may distract from the main points.

Embrace counterarguments:  Acknowledge and address opposing views to strengthen your position. Show that you have considered alternative arguments in your field.

7 research tips 

If you want your paper to not only be well-written but also contribute to the progress of human knowledge, consider these tips to take your paper to the next level:

Selecting the appropriate topic: The topic you select should align with your area of expertise, comply with the requirements of your project, and have sufficient resources for a comprehensive investigation.

Use academic databases: Academic databases such as PubMed, Google Scholar, and JSTOR offer a wealth of research papers that can help you discover everything you need to know about your chosen topic.

Critically evaluate sources: It is important not to accept research findings at face value. Instead, it is crucial to critically analyze the information to avoid jumping to conclusions or overlooking important details. A well-written research paper requires a critical analysis with thorough reasoning to support claims.

Diversify your sources: Expand your research horizons by exploring a variety of sources beyond the standard databases. Utilize books, conference proceedings, and interviews to gather diverse perspectives and enrich your understanding of the topic.

Take detailed notes: Detailed note-taking is crucial during research and can help you form the outline and body of your paper.

Stay up on trends: Keep abreast of the latest developments in your field by regularly checking for recent publications. Subscribe to newsletters, follow relevant journals, and attend conferences to stay informed about emerging trends and advancements. 

Engage in peer review: Seek feedback from peers or mentors to ensure the rigor and validity of your research . Peer review helps identify potential weaknesses in your methodology and strengthens the overall credibility of your findings.

• The real-world impact of research papers

Writing a research paper is more than an academic or business exercise. The experience provides an opportunity to explore a subject in-depth, broaden one's understanding, and arrive at meaningful conclusions. With careful planning, dedication, and hard work, writing a research paper can be a fulfilling and enriching experience contributing to advancing knowledge.

How do I publish my research paper? 

Many academics wish to publish their research papers. While challenging, your paper might get traction if it covers new and well-written information. To publish your research paper, find a target publication, thoroughly read their guidelines, format your paper accordingly, and send it to them per their instructions. You may need to include a cover letter, too. After submission, your paper may be peer-reviewed by experts to assess its legitimacy, quality, originality, and methodology. Following review, you will be informed by the publication whether they have accepted or rejected your paper. 

What is a good opening sentence for a research paper? 

Beginning your research paper with a compelling introduction can ensure readers are interested in going further. A relevant quote, a compelling statistic, or a bold argument can start the paper and hook your reader. Remember, though, that the most important aspect of a research paper is the quality of the information––not necessarily your ability to storytell, so ensure anything you write aligns with your goals.

Research paper vs. a research proposal—what’s the difference?

While some may confuse research papers and proposals, they are different documents. 

A research proposal comes before a research paper. It is a detailed document that outlines an intended area of exploration. It includes the research topic, methodology, timeline, sources, and potential conclusions. Research proposals are often required when seeking approval to conduct research. 

A research paper is a summary of research findings. A research paper follows a structured format to present those findings and construct an argument or conclusion.

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• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

• 6. The Methodology
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Applying Critical Thinking
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Quantitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Generative AI and Writing
• USC Libraries Tutorials and Other Guides
• Bibliography

The methods section describes actions taken to investigate a research problem and the rationale for the application of specific procedures or techniques used to identify, select, process, and analyze information applied to understanding the problem, thereby, allowing the reader to critically evaluate a study’s overall validity and reliability. The methodology section of a research paper answers two main questions: How was the data collected or generated? And, how was it analyzed? The writing should be direct and precise and always written in the past tense.

Kallet, Richard H. "How to Write the Methods Section of a Research Paper." Respiratory Care 49 (October 2004): 1229-1232.

Importance of a Good Methodology Section

You must explain how you obtained and analyzed your results for the following reasons:

• Readers need to know how the data was obtained because the method you chose affects the results and, by extension, how you interpreted their significance in the discussion section of your paper.
• Methodology is crucial for any branch of scholarship because an unreliable method produces unreliable results and, as a consequence, undermines the value of your analysis of the findings.
• In most cases, there are a variety of different methods you can choose to investigate a research problem. The methodology section of your paper should clearly articulate the reasons why you have chosen a particular procedure or technique.
• The reader wants to know that the data was collected or generated in a way that is consistent with accepted practice in the field of study. For example, if you are using a multiple choice questionnaire, readers need to know that it offered your respondents a reasonable range of answers to choose from.
• The method must be appropriate to fulfilling the overall aims of the study. For example, you need to ensure that you have a large enough sample size to be able to generalize and make recommendations based upon the findings.
• The methodology should discuss the problems that were anticipated and the steps you took to prevent them from occurring. For any problems that do arise, you must describe the ways in which they were minimized or why these problems do not impact in any meaningful way your interpretation of the findings.
• In the social and behavioral sciences, it is important to always provide sufficient information to allow other researchers to adopt or replicate your methodology. This information is particularly important when a new method has been developed or an innovative use of an existing method is utilized.

Bem, Daryl J. Writing the Empirical Journal Article. Psychology Writing Center. University of Washington; Denscombe, Martyn. The Good Research Guide: For Small-Scale Social Research Projects . 5th edition. Buckingham, UK: Open University Press, 2014; Lunenburg, Frederick C. Writing a Successful Thesis or Dissertation: Tips and Strategies for Students in the Social and Behavioral Sciences . Thousand Oaks, CA: Corwin Press, 2008.

Structure and Writing Style

I.  Groups of Research Methods

There are two main groups of research methods in the social sciences:

• The e mpirical-analytical group approaches the study of social sciences in a similar manner that researchers study the natural sciences . This type of research focuses on objective knowledge, research questions that can be answered yes or no, and operational definitions of variables to be measured. The empirical-analytical group employs deductive reasoning that uses existing theory as a foundation for formulating hypotheses that need to be tested. This approach is focused on explanation.
• The i nterpretative group of methods is focused on understanding phenomenon in a comprehensive, holistic way . Interpretive methods focus on analytically disclosing the meaning-making practices of human subjects [the why, how, or by what means people do what they do], while showing how those practices arrange so that it can be used to generate observable outcomes. Interpretive methods allow you to recognize your connection to the phenomena under investigation. However, the interpretative group requires careful examination of variables because it focuses more on subjective knowledge.

II.  Content

The introduction to your methodology section should begin by restating the research problem and underlying assumptions underpinning your study. This is followed by situating the methods you used to gather, analyze, and process information within the overall “tradition” of your field of study and within the particular research design you have chosen to study the problem. If the method you choose lies outside of the tradition of your field [i.e., your review of the literature demonstrates that the method is not commonly used], provide a justification for how your choice of methods specifically addresses the research problem in ways that have not been utilized in prior studies.

The remainder of your methodology section should describe the following:

• Decisions made in selecting the data you have analyzed or, in the case of qualitative research, the subjects and research setting you have examined,
• Tools and methods used to identify and collect information, and how you identified relevant variables,
• The ways in which you processed the data and the procedures you used to analyze that data, and
• The specific research tools or strategies that you utilized to study the underlying hypothesis and research questions.

In addition, an effectively written methodology section should:

• Introduce the overall methodological approach for investigating your research problem . Is your study qualitative or quantitative or a combination of both (mixed method)? Are you going to take a special approach, such as action research, or a more neutral stance?
• Indicate how the approach fits the overall research design . Your methods for gathering data should have a clear connection to your research problem. In other words, make sure that your methods will actually address the problem. One of the most common deficiencies found in research papers is that the proposed methodology is not suitable to achieving the stated objective of your paper.
• Describe the specific methods of data collection you are going to use , such as, surveys, interviews, questionnaires, observation, archival research. If you are analyzing existing data, such as a data set or archival documents, describe how it was originally created or gathered and by whom. Also be sure to explain how older data is still relevant to investigating the current research problem.
• Explain how you intend to analyze your results . Will you use statistical analysis? Will you use specific theoretical perspectives to help you analyze a text or explain observed behaviors? Describe how you plan to obtain an accurate assessment of relationships, patterns, trends, distributions, and possible contradictions found in the data.
• Provide background and a rationale for methodologies that are unfamiliar for your readers . Very often in the social sciences, research problems and the methods for investigating them require more explanation/rationale than widely accepted rules governing the natural and physical sciences. Be clear and concise in your explanation.
• Provide a justification for subject selection and sampling procedure . For instance, if you propose to conduct interviews, how do you intend to select the sample population? If you are analyzing texts, which texts have you chosen, and why? If you are using statistics, why is this set of data being used? If other data sources exist, explain why the data you chose is most appropriate to addressing the research problem.
• Provide a justification for case study selection . A common method of analyzing research problems in the social sciences is to analyze specific cases. These can be a person, place, event, phenomenon, or other type of subject of analysis that are either examined as a singular topic of in-depth investigation or multiple topics of investigation studied for the purpose of comparing or contrasting findings. In either method, you should explain why a case or cases were chosen and how they specifically relate to the research problem.
• Describe potential limitations . Are there any practical limitations that could affect your data collection? How will you attempt to control for potential confounding variables and errors? If your methodology may lead to problems you can anticipate, state this openly and show why pursuing this methodology outweighs the risk of these problems cropping up.

NOTE:   Once you have written all of the elements of the methods section, subsequent revisions should focus on how to present those elements as clearly and as logically as possibly. The description of how you prepared to study the research problem, how you gathered the data, and the protocol for analyzing the data should be organized chronologically. For clarity, when a large amount of detail must be presented, information should be presented in sub-sections according to topic. If necessary, consider using appendices for raw data.

ANOTHER NOTE: If you are conducting a qualitative analysis of a research problem , the methodology section generally requires a more elaborate description of the methods used as well as an explanation of the processes applied to gathering and analyzing of data than is generally required for studies using quantitative methods. Because you are the primary instrument for generating the data [e.g., through interviews or observations], the process for collecting that data has a significantly greater impact on producing the findings. Therefore, qualitative research requires a more detailed description of the methods used.

YET ANOTHER NOTE:   If your study involves interviews, observations, or other qualitative techniques involving human subjects , you may be required to obtain approval from the university's Office for the Protection of Research Subjects before beginning your research. This is not a common procedure for most undergraduate level student research assignments. However, i f your professor states you need approval, you must include a statement in your methods section that you received official endorsement and adequate informed consent from the office and that there was a clear assessment and minimization of risks to participants and to the university. This statement informs the reader that your study was conducted in an ethical and responsible manner. In some cases, the approval notice is included as an appendix to your paper.

III.  Problems to Avoid

Irrelevant Detail The methodology section of your paper should be thorough but concise. Do not provide any background information that does not directly help the reader understand why a particular method was chosen, how the data was gathered or obtained, and how the data was analyzed in relation to the research problem [note: analyzed, not interpreted! Save how you interpreted the findings for the discussion section]. With this in mind, the page length of your methods section will generally be less than any other section of your paper except the conclusion.

Unnecessary Explanation of Basic Procedures Remember that you are not writing a how-to guide about a particular method. You should make the assumption that readers possess a basic understanding of how to investigate the research problem on their own and, therefore, you do not have to go into great detail about specific methodological procedures. The focus should be on how you applied a method , not on the mechanics of doing a method. An exception to this rule is if you select an unconventional methodological approach; if this is the case, be sure to explain why this approach was chosen and how it enhances the overall process of discovery.

Problem Blindness It is almost a given that you will encounter problems when collecting or generating your data, or, gaps will exist in existing data or archival materials. Do not ignore these problems or pretend they did not occur. Often, documenting how you overcame obstacles can form an interesting part of the methodology. It demonstrates to the reader that you can provide a cogent rationale for the decisions you made to minimize the impact of any problems that arose.

Literature Review Just as the literature review section of your paper provides an overview of sources you have examined while researching a particular topic, the methodology section should cite any sources that informed your choice and application of a particular method [i.e., the choice of a survey should include any citations to the works you used to help construct the survey].

It’s More than Sources of Information! A description of a research study's method should not be confused with a description of the sources of information. Such a list of sources is useful in and of itself, especially if it is accompanied by an explanation about the selection and use of the sources. The description of the project's methodology complements a list of sources in that it sets forth the organization and interpretation of information emanating from those sources.

Azevedo, L.F. et al. "How to Write a Scientific Paper: Writing the Methods Section." Revista Portuguesa de Pneumologia 17 (2011): 232-238; Blair Lorrie. “Choosing a Methodology.” In Writing a Graduate Thesis or Dissertation , Teaching Writing Series. (Rotterdam: Sense Publishers 2016), pp. 49-72; Butin, Dan W. The Education Dissertation A Guide for Practitioner Scholars . Thousand Oaks, CA: Corwin, 2010; Carter, Susan. Structuring Your Research Thesis . New York: Palgrave Macmillan, 2012; Kallet, Richard H. “How to Write the Methods Section of a Research Paper.” Respiratory Care 49 (October 2004):1229-1232; Lunenburg, Frederick C. Writing a Successful Thesis or Dissertation: Tips and Strategies for Students in the Social and Behavioral Sciences . Thousand Oaks, CA: Corwin Press, 2008. Methods Section. The Writer’s Handbook. Writing Center. University of Wisconsin, Madison; Rudestam, Kjell Erik and Rae R. Newton. “The Method Chapter: Describing Your Research Plan.” In Surviving Your Dissertation: A Comprehensive Guide to Content and Process . (Thousand Oaks, Sage Publications, 2015), pp. 87-115; What is Interpretive Research. Institute of Public and International Affairs, University of Utah; Writing the Experimental Report: Methods, Results, and Discussion. The Writing Lab and The OWL. Purdue University; Methods and Materials. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College.

Writing Tip

Statistical Designs and Tests? Do Not Fear Them!

Don't avoid using a quantitative approach to analyzing your research problem just because you fear the idea of applying statistical designs and tests. A qualitative approach, such as conducting interviews or content analysis of archival texts, can yield exciting new insights about a research problem, but it should not be undertaken simply because you have a disdain for running a simple regression. A well designed quantitative research study can often be accomplished in very clear and direct ways, whereas, a similar study of a qualitative nature usually requires considerable time to analyze large volumes of data and a tremendous burden to create new paths for analysis where previously no path associated with your research problem had existed.

To locate data and statistics, GO HERE .

Another Writing Tip

Knowing the Relationship Between Theories and Methods

There can be multiple meaning associated with the term "theories" and the term "methods" in social sciences research. A helpful way to delineate between them is to understand "theories" as representing different ways of characterizing the social world when you research it and "methods" as representing different ways of generating and analyzing data about that social world. Framed in this way, all empirical social sciences research involves theories and methods, whether they are stated explicitly or not. However, while theories and methods are often related, it is important that, as a researcher, you deliberately separate them in order to avoid your theories playing a disproportionate role in shaping what outcomes your chosen methods produce.

Introspectively engage in an ongoing dialectic between the application of theories and methods to help enable you to use the outcomes from your methods to interrogate and develop new theories, or ways of framing conceptually the research problem. This is how scholarship grows and branches out into new intellectual territory.

Reynolds, R. Larry. Ways of Knowing. Alternative Microeconomics . Part 1, Chapter 3. Boise State University; The Theory-Method Relationship. S-Cool Revision. United Kingdom.

Yet Another Writing Tip

Methods and the Methodology

Do not confuse the terms "methods" and "methodology." As Schneider notes, a method refers to the technical steps taken to do research . Descriptions of methods usually include defining and stating why you have chosen specific techniques to investigate a research problem, followed by an outline of the procedures you used to systematically select, gather, and process the data [remember to always save the interpretation of data for the discussion section of your paper].

The methodology refers to a discussion of the underlying reasoning why particular methods were used . This discussion includes describing the theoretical concepts that inform the choice of methods to be applied, placing the choice of methods within the more general nature of academic work, and reviewing its relevance to examining the research problem. The methodology section also includes a thorough review of the methods other scholars have used to study the topic.

Bryman, Alan. "Of Methods and Methodology." Qualitative Research in Organizations and Management: An International Journal 3 (2008): 159-168; Schneider, Florian. “What's in a Methodology: The Difference between Method, Methodology, and Theory…and How to Get the Balance Right?” PoliticsEastAsia.com. Chinese Department, University of Leiden, Netherlands.

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• Last Updated: Sep 4, 2024 9:40 AM
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Advanced Research Methods

Writing the research paper.

• What Is Research?
• Library Research
• Writing a Research Proposal

Before Writing the Paper

Methods, thesis and hypothesis, clarity, precision and academic expression, format your paper, typical problems, a few suggestions, avoid plagiarism.

• Presenting the Research Paper

Find a topic.

• Try to find a subject that really interests you.
• While you explore the topic, narrow or broaden your target and focus on something that gives the most promising results.
• Don't choose a huge subject if you have to write a 3 page long paper, and broaden your topic sufficiently if you have to submit at least 25 pages.
• Consult your class instructor (and your classmates) about the topic.

Explore the topic.

• Find primary and secondary sources in the library.
• Read and critically analyse them.
• Take notes.
• Compile surveys, collect data, gather materials for quantitative analysis (if these are good methods to investigate the topic more deeply).
• Come up with new ideas about the topic. Try to formulate your ideas in a few sentences.
• Review your notes and other materials and enrich the outline.
• Try to estimate how long the individual parts will be.
• Do others understand what you want to say?
• Do they accept it as new knowledge or relevant and important for a paper?
• Do they agree that your thoughts will result in a successful paper?
• Qualitative: gives answers on questions (how, why, when, who, what, etc.) by investigating an issue
• Quantitative:requires data and the analysis of data as well
• the essence, the point of the research paper in one or two sentences.
• a statement that can be proved or disproved.
• Be specific.
• Avoid ambiguity.
• Use predominantly the active voice, not the passive.
• Deal with one issue in one paragraph.
• Be accurate.
• Double-check your data, references, citations and statements.

Academic Expression

• Don't use familiar style or colloquial/slang expressions.
• Write in full sentences.
• Check the meaning of the words if you don't know exactly what they mean.
• Avoid metaphors.
• Almost the rough content of every paragraph.
• The order of the various topics in your paper.
• On the basis of the outline, start writing a part by planning the content, and then write it down.
• Put a visible mark (which you will later delete) where you need to quote a source, and write in the citation when you finish writing that part or a bigger part.
• Does the text make sense?
• Could you explain what you wanted?
• Did you write good sentences?
• Is there something missing?
• Check the spelling.
• Complete the citations, bring them in standard format.

Use the guidelines that your instructor requires (MLA, Chicago, APA, Turabian, etc.).

• Adjust margins, spacing, paragraph indentation, place of page numbers, etc.
• Standardize the bibliography or footnotes according to the guidelines.

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(Based on English Composition 2 from Illinois Valley Community College):

• Weak organization
• Poor support and development of ideas
• Weak use of secondary sources
• Excessive errors
• Stylistic weakness

When collecting materials, selecting research topic, and writing the paper:

• Be systematic and organized (e.g. keep your bibliography neat and organized; write your notes in a neat way, so that you can find them later on.
• Use your critical thinking ability when you read.
• Write down your thoughts (so that you can reconstruct them later).
• Stop when you have a really good idea and think about whether you could enlarge it to a whole research paper. If yes, take much longer notes.
• When you write down a quotation or summarize somebody else's thoughts in your notes or in the paper, cite the source (i.e. write down the author, title, publication place, year, page number).
• If you quote or summarize a thought from the internet, cite the internet source.
• Write an outline that is detailed enough to remind you about the content.
• Read your paper for yourself or, preferably, somebody else. 
• When you finish writing, check the spelling;
• Use the citation form (MLA, Chicago, or other) that your instructor requires and use it everywhere.

Plagiarism : somebody else's words or ideas presented without citation by an author

• Cite your source every time when you quote a part of somebody's work.
• Cite your source  every time when you summarize a thought from somebody's work.
• Cite your source  every time when you use a source (quote or summarize) from the Internet.

Consult the Citing Sources research guide for further details.

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• Last Updated: Aug 22, 2024 3:43 PM
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Checklist: Dissertation Proposal

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Examples: Edited Papers

Need editing and proofreading services, research methodology guide: writing tips, types, & examples.

• Tags: Academic Research , Research

No dissertation or research paper is complete without the research methodology section. Since this is the chapter where you explain how you carried out your research, this is where all the meat is! Here’s where you clearly lay out the steps you have taken to test your hypothesis or research problem.

Through this blog, we’ll unravel the complexities and meaning of research methodology in academic writing , from its fundamental principles and ethics to the diverse types of research methodology in use today. Alongside offering research methodology examples, we aim to guide you on how to write research methodology, ensuring your research endeavors are both impactful and impeccably grounded!

Ensure your research methodology is foolproof. Learn more

Let’s first take a closer look at a simple research methodology definition:

Defining what is research methodology

Research methodology is the set of procedures and techniques used to collect, analyze, and interpret data to understand and solve a research problem. Methodology in research not only includes the design and methods but also the basic principles that guide the choice of specific methods.

Grasping the concept of methodology in research is essential for students and scholars, as it demonstrates the thorough and structured method used to explore a hypothesis or research question. Understanding the definition of methodology in research aids in identifying the methods used to collect data. Be it through any type of research method approach, ensuring adherence to the proper research paper format is crucial.

Now let’s explore some research methodology types:

Types of research methodology

1. qualitative research methodology.

Qualitative research methodology is aimed at understanding concepts, thoughts, or experiences. This approach is descriptive and is often utilized to gather in-depth insights into people’s attitudes, behaviors, or cultures. Qualitative research methodology involves methods like interviews, focus groups, and observation. The strength of this methodology lies in its ability to provide contextual richness.

2. Quantitative research methodology

Quantitative research methodology, on the other hand, is focused on quantifying the problem by generating numerical data or data that can be transformed into usable statistics. It uses measurable data to formulate facts and uncover patterns in research. Quantitative research methodology typically involves surveys, experiments, or statistical analysis. This methodology is appreciated for its ability to produce objective results that are generalizable to a larger population.

3. Mixed-Methods research methodology

Mixed-methods research combines both qualitative and quantitative research methodologies to provide a more comprehensive understanding of the research problem. This approach leverages the strengths of both methodologies to provide a deeper insight into the research question of a research paper .

Research methodology vs. research methods

The research methodology or design is the overall strategy and rationale that you used to carry out the research. Whereas, research methods are the specific tools and processes you use to gather and understand the data you need to test your hypothesis.

Research methodology examples and application

To further understand research methodology, let’s explore some examples of research methodology:

a. Qualitative research methodology example: A study exploring the impact of author branding on author popularity might utilize in-depth interviews to gather personal experiences and perspectives.

b. Quantitative research methodology example: A research project investigating the effects of a book promotion technique on book sales could employ a statistical analysis of profit margins and sales before and after the implementation of the method.

c. Mixed-Methods research methodology example: A study examining the relationship between social media use and academic performance might combine both qualitative and quantitative approaches. It could include surveys to quantitatively assess the frequency of social media usage and its correlation with grades, alongside focus groups or interviews to qualitatively explore students’ perceptions and experiences regarding how social media affects their study habits and academic engagement.

These examples highlight the meaning of methodology in research and how it guides the research process, from data collection to analysis, ensuring the study’s objectives are met efficiently.

Importance of methodology in research papers

When it comes to writing your study, the methodology in research papers or a dissertation plays a pivotal role. A well-crafted methodology section of a research paper or thesis not only enhances the credibility of your research but also provides a roadmap for others to replicate or build upon your work.

How to structure the research methods chapter

Wondering how to write the research methodology section? Follow these steps to create a strong methods chapter:

Step 1: Explain your research methodology

At the start of a research paper , you would have provided the background of your research and stated your hypothesis or research problem. In this section, you will elaborate on your research strategy. 

Begin by restating your research question and proceed to explain what type of research you opted for to test it. Depending on your research, here are some questions you can consider: 

a. Did you use qualitative or quantitative data to test the hypothesis? 

b. Did you perform an experiment where you collected data or are you writing a dissertation that is descriptive/theoretical without data collection? 

c. Did you use primary data that you collected or analyze secondary research data or existing data as part of your study? 

These questions will help you establish the rationale for your study on a broader level, which you will follow by elaborating on the specific methods you used to collect and understand your data. 

Step 2: Explain the methods you used to test your hypothesis 

Now that you have told your reader what type of research you’ve undertaken for the dissertation, it’s time to dig into specifics. State what specific methods you used and explain the conditions and variables involved. Explain what the theoretical framework behind the method was, what samples you used for testing it, and what tools and materials you used to collect the data. 

Step 3: Explain how you analyzed the results

Once you have explained the data collection process, explain how you analyzed and studied the data. Here, your focus is simply to explain the methods of analysis rather than the results of the study. 

Here are some questions you can answer at this stage: 

a. What tools or software did you use to analyze your results? 

b. What parameters or variables did you consider while understanding and studying the data you’ve collected? 

c. Was your analysis based on a theoretical framework? 

Your mode of analysis will change depending on whether you used a quantitative or qualitative research methodology in your study. If you’re working within the hard sciences or physical sciences, you are likely to use a quantitative research methodology (relying on numbers and hard data). If you’re doing a qualitative study, in the social sciences or humanities, your analysis may rely on understanding language and socio-political contexts around your topic. This is why it’s important to establish what kind of study you’re undertaking at the onset. 

Step 4: Defend your choice of methodology 

Now that you have gone through your research process in detail, you’ll also have to make a case for it. Justify your choice of methodology and methods, explaining why it is the best choice for your research question. This is especially important if you have chosen an unconventional approach or you’ve simply chosen to study an existing research problem from a different perspective. Compare it with other methodologies, especially ones attempted by previous researchers, and discuss what contributions using your methodology makes.  

Step 5: Discuss the obstacles you encountered and how you overcame them

No matter how thorough a methodology is, it doesn’t come without its hurdles. This is a natural part of scientific research that is important to document so that your peers and future researchers are aware of it. Writing in a research paper about this aspect of your research process also tells your evaluator that you have actively worked to overcome the pitfalls that came your way and you have refined the research process. 

Tips to write an effective methodology chapter

1. Remember who you are writing for. Keeping sight of the reader/evaluator will help you know what to elaborate on and what information they are already likely to have. You’re condensing months’ work of research in just a few pages, so you should omit basic definitions and information about general phenomena people already know.

2. Do not give an overly elaborate explanation of every single condition in your study. 

3. Skip details and findings irrelevant to the results.

4. Cite references that back your claim and choice of methodology. 

5. Consistently emphasize the relationship between your research question and the methodology you adopted to study it. 

To sum it up, what is methodology in research? It’s the blueprint of your research, essential for ensuring that your study is systematic, rigorous, and credible. Whether your focus is on qualitative research methodology, quantitative research methodology, or a combination of both, understanding and clearly defining your methodology is key to the success of your research.

Once you write the research methodology and complete writing the entire research paper, the next step is to edit your paper. As experts in research paper editing and proofreading services , we’d love to help you perfect your paper!

Here are some other articles that you might find useful: 

• Essential Research Tips for Essay Writing
• How to Write a Lab Report: Examples from Academic Editors
• The Essential Types of Editing Every Writer Needs to Know
• Editing and Proofreading Academic Papers: A Short Guide
• The Top 10 Editing and Proofreading Services of 2023

Frequently Asked Questions

What does research methodology mean, what types of research methodologies are there, what is qualitative research methodology, how to determine sample size in research methodology, what is action research methodology.

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Research Methods | Definition, Types, Examples

Research methods are specific procedures for collecting and analysing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

• Qualitative vs quantitative : Will your data take the form of words or numbers?
• Primary vs secondary : Will you collect original data yourself, or will you use data that have already been collected by someone else?
• Descriptive vs experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyse the data .

• For quantitative data, you can use statistical analysis methods to test relationships between variables.
• For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Table of contents

Methods for collecting data, examples of data collection methods, methods for analysing data, examples of data analysis methods, frequently asked questions about methodology.

Data are the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .

You can also take a mixed methods approach, where you use both qualitative and quantitative research methods.

Primary vs secondary data

Primary data are any original information that you collect for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary data are information that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data. But if you want to synthesise existing knowledge, analyse historical trends, or identify patterns on a large scale, secondary data might be a better choice.

Descriptive vs experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.

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Your data analysis methods will depend on the type of data you collect and how you prepare them for analysis.

Data can often be analysed both quantitatively and qualitatively. For example, survey responses could be analysed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that were collected:

• From open-ended survey and interview questions, literature reviews, case studies, and other sources that use text rather than numbers.
• Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions.

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that were collected either:

• During an experiment.
• Using probability sampling methods .

Because the data are collected and analysed in a statistically valid way, the results of quantitative analysis can be easily standardised and shared among researchers.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyse data (e.g. experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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How to Write a Research Paper

Use the links below to jump directly to any section of this guide:

Research Paper Fundamentals

How to choose a topic or question, how to create a working hypothesis or thesis, common research paper methodologies, how to gather and organize evidence , how to write an outline for your research paper, how to write a rough draft, how to revise your draft, how to produce a final draft, resources for teachers .

It is not fair to say that no one writes anymore. Just about everyone writes text messages, brief emails, or social media posts every single day. Yet, most people don't have a lot of practice with the formal, organized writing required for a good academic research paper. This guide contains links to a variety of resources that can help demystify the process. Some of these resources are intended for teachers; they contain exercises, activities, and teaching strategies. Other resources are intended for direct use by students who are struggling to write papers, or are looking for tips to make the process go more smoothly.

The resources in this section are designed to help students understand the different types of research papers, the general research process, and how to manage their time. Below, you'll find links from university writing centers, the trusted Purdue Online Writing Lab, and more.

What is an Academic Research Paper?

"Genre and the Research Paper" (Purdue OWL)

There are different types of research papers. Different types of scholarly questions will lend themselves to one format or another. This is a brief introduction to the two main genres of research paper: analytic and argumentative. 

"7 Most Popular Types of Research Papers" (Personal-writer.com)

This resource discusses formats that high school students commonly encounter, such as the compare and contrast essay and the definitional essay. Please note that the inclusion of this link is not an endorsement of this company's paid service.

How to Prepare and Plan Out Writing a Research Paper

Teachers can give their students a step-by-step guide like these to help them understand the different steps of the research paper process. These guides can be combined with the time management tools in the next subsection to help students come up with customized calendars for completing their papers.

"Ten Steps for Writing Research Papers" (American University)  

This resource from American University is a comprehensive guide to the research paper writing process, and includes examples of proper research questions and thesis topics.

"Steps in Writing a Research Paper" (SUNY Empire State College)

This guide breaks the research paper process into 11 steps. Each "step" links to a separate page, which describes the work entailed in completing it.

How to Manage Time Effectively

The links below will help students determine how much time is necessary to complete a paper. If your sources are not available online or at your local library, you'll need to leave extra time for the Interlibrary Loan process. Remember that, even if you do not need to consult secondary sources, you'll still need to leave yourself ample time to organize your thoughts.

"Research Paper Planner: Timeline" (Baylor University)

This interactive resource from Baylor University creates a suggested writing schedule based on how much time a student has to work on the assignment.

"Research Paper Planner" (UCLA)

UCLA's library offers this step-by-step guide to the research paper writing process, which also includes a suggested planning calendar.

There's a reason teachers spend a long time talking about choosing a good topic. Without a good topic and a well-formulated research question, it is almost impossible to write a clear and organized paper. The resources below will help you generate ideas and formulate precise questions.

"How to Select a Research Topic" (Univ. of Michigan-Flint)

This resource is designed for college students who are struggling to come up with an appropriate topic. A student who uses this resource and still feels unsure about his or her topic should consult the course instructor for further personalized assistance.

"25 Interesting Research Paper Topics to Get You Started" (Kibin)

This resource, which is probably most appropriate for high school students, provides a list of specific topics to help get students started. It is broken into subsections, such as "paper topics on local issues."

"Writing a Good Research Question" (Grand Canyon University)

This introduction to research questions includes some embedded videos, as well as links to scholarly articles on research questions. This resource would be most appropriate for teachers who are planning lessons on research paper fundamentals.

"How to Write a Research Question the Right Way" (Kibin)

This student-focused resource provides more detail on writing research questions. The language is accessible, and there are embedded videos and examples of good and bad questions.

It is important to have a rough hypothesis or thesis in mind at the beginning of the research process. People who have a sense of what they want to say will have an easier time sorting through scholarly sources and other information. The key, of course, is not to become too wedded to the draft hypothesis or thesis. Just about every working thesis gets changed during the research process.

CrashCourse Video: "Sociology Research Methods" (YouTube)

Although this video is tailored to sociology students, it is applicable to students in a variety of social science disciplines. This video does a good job demonstrating the connection between the brainstorming that goes into selecting a research question and the formulation of a working hypothesis.

"How to Write a Thesis Statement for an Analytical Essay" (YouTube)

Students writing analytical essays will not develop the same type of working hypothesis as students who are writing research papers in other disciplines. For these students, developing the working thesis may happen as a part of the rough draft (see the relevant section below). 

"Research Hypothesis" (Oakland Univ.)

This resource provides some examples of hypotheses in social science disciplines like Political Science and Criminal Justice. These sample hypotheses may also be useful for students in other soft social sciences and humanities disciplines like History.

When grading a research paper, instructors look for a consistent methodology. This section will help you understand different methodological approaches used in research papers. Students will get the most out of these resources if they use them to help prepare for conversations with teachers or discussions in class.

"Types of Research Designs" (USC)

A "research design," used for complex papers, is related to the paper's method. This resource contains introductions to a variety of popular research designs in the social sciences. Although it is not the most intuitive site to read, the information here is very valuable. 

"Major Research Methods" (YouTube)

Although this video is a bit on the dry side, it provides a comprehensive overview of the major research methodologies in a format that might be more accessible to students who have struggled with textbooks or other written resources.

"Humanities Research Strategies" (USC)

This is a portal where students can learn about four methodological approaches for humanities papers: Historical Methodologies, Textual Criticism, Conceptual Analysis, and the Synoptic method.

"Selected Major Social Science Research Methods: Overview" (National Academies Press)

This appendix from the book  Using Science as Evidence in Public Policy , printed by National Academies Press, introduces some methods used in social science papers.

"Organizing Your Social Sciences Research Paper: 6. The Methodology" (USC)

This resource from the University of Southern California's library contains tips for writing a methodology section in a research paper.

How to Determine the Best Methodology for You

Anyone who is new to writing research papers should be sure to select a method in consultation with their instructor. These resources can be used to help prepare for that discussion. They may also be used on their own by more advanced students.

"Choosing Appropriate Research Methodologies" (Palgrave Study Skills)

This friendly and approachable resource from Palgrave Macmillan can be used by students who are just starting to think about appropriate methodologies.

"How to Choose Your Research Methods" (NFER (UK))

This is another approachable resource students can use to help narrow down the most appropriate methods for their research projects.

The resources in this section introduce the process of gathering scholarly sources and collecting evidence. You'll find a range of material here, from introductory guides to advanced explications best suited to college students. Please consult the LitCharts  How to Do Academic Research guide for a more comprehensive list of resources devoted to finding scholarly literature.

Google Scholar

Students who have access to library websites with detailed research guides should start there, but people who do not have access to those resources can begin their search for secondary literature here.

"Gathering Appropriate Information" (Texas Gateway)

This resource from the Texas Gateway for online resources introduces students to the research process, and contains interactive exercises. The level of complexity is suitable for middle school, high school, and introductory college classrooms.

"An Overview of Quantitative and Qualitative Data Collection Methods" (NSF)

This PDF from the National Science Foundation goes into detail about best practices and pitfalls in data collection across multiple types of methodologies.

"Social Science Methods for Data Collection and Analysis" (Swiss FIT)

This resource is appropriate for advanced undergraduates or teachers looking to create lessons on research design and data collection. It covers techniques for gathering data via interviews, observations, and other methods.

"Collecting Data by In-depth Interviewing" (Leeds Univ.)

This resource contains enough information about conducting interviews to make it useful for teachers who want to create a lesson plan, but is also accessible enough for college juniors or seniors to make use of it on their own.

There is no "one size fits all" outlining technique. Some students might devote all their energy and attention to the outline in order to avoid the paper. Other students may benefit from being made to sit down and organize their thoughts into a lengthy sentence outline. The resources in this section include strategies and templates for multiple types of outlines. 

"Topic vs. Sentence Outlines" (UC Berkeley)

This resource introduces two basic approaches to outlining: the shorter topic-based approach, and the longer, more detailed sentence-based approach. This resource also contains videos on how to develop paper paragraphs from the sentence-based outline.

"Types of Outlines and Samples" (Purdue OWL)

The Purdue Online Writing Lab's guide is a slightly less detailed discussion of different types of outlines. It contains several sample outlines.

"Writing An Outline" (Austin C.C.)

This resource from a community college contains sample outlines from an American history class that students can use as models.

"How to Structure an Outline for a College Paper" (YouTube)

This brief (sub-2 minute) video from the ExpertVillage YouTube channel provides a model of outline writing for students who are struggling with the idea.

"Outlining" (Harvard)

This is a good resource to consult after completing a draft outline. It offers suggestions for making sure your outline avoids things like unnecessary repetition.

As with outlines, rough drafts can take on many different forms. These resources introduce teachers and students to the various approaches to writing a rough draft. This section also includes resources that will help you cite your sources appropriately according to the MLA, Chicago, and APA style manuals.

"Creating a Rough Draft for a Research Paper" (Univ. of Minnesota)

This resource is useful for teachers in particular, as it provides some suggested exercises to help students with writing a basic rough draft. 

Rough Draft Assignment (Duke of Definition)

This sample assignment, with a brief list of tips, was developed by a high school teacher who runs a very successful and well-reviewed page of educational resources.

"Creating the First Draft of Your Research Paper" (Concordia Univ.)

This resource will be helpful for perfectionists or procrastinators, as it opens by discussing the problem of avoiding writing. It also provides a short list of suggestions meant to get students writing.

Using Proper Citations

There is no such thing as a rough draft of a scholarly citation. These links to the three major citation guides will ensure that your citations follow the correct format. Please consult the LitCharts How to Cite Your Sources guide for more resources.

Chicago Manual of Style Citation Guide

Some call  The Chicago Manual of Style , which was first published in 1906, "the editors' Bible." The manual is now in its 17th edition, and is popular in the social sciences, historical journals, and some other fields in the humanities.

APA Citation Guide

According to the American Psychological Association, this guide was developed to aid reading comprehension, clarity of communication, and to reduce bias in language in the social and behavioral sciences. Its first full edition was published in 1952, and it is now in its sixth edition.

MLA Citation Guide

The Modern Language Association style is used most commonly within the liberal arts and humanities. The  MLA Style Manual and Guide to Scholarly Publishing  was first published in 1985 and (as of 2008) is in its third edition.

Any professional scholar will tell you that the best research papers are made in the revision stage. No matter how strong your research question or working thesis, it is not possible to write a truly outstanding paper without devoting energy to revision. These resources provide examples of revision exercises for the classroom, as well as tips for students working independently.

"The Art of Revision" (Univ. of Arizona)

This resource provides a wealth of information and suggestions for both students and teachers. There is a list of suggested exercises that teachers might use in class, along with a revision checklist that is useful for teachers and students alike.

"Script for Workshop on Revision" (Vanderbilt University)

Vanderbilt's guide for leading a 50-minute revision workshop can serve as a model for teachers who wish to guide students through the revision process during classtime. 

"Revising Your Paper" (Univ. of Washington)

This detailed handout was designed for students who are beginning the revision process. It discusses different approaches and methods for revision, and also includes a detailed list of things students should look for while they revise.

"Revising Drafts" (UNC Writing Center)

This resource is designed for students and suggests things to look for during the revision process. It provides steps for the process and has a FAQ for students who have questions about why it is important to revise.

Conferencing with Writing Tutors and Instructors

No writer is so good that he or she can't benefit from meeting with instructors or peer tutors. These resources from university writing, learning, and communication centers provide suggestions for how to get the most out of these one-on-one meetings.

"Getting Feedback" (UNC Writing Center)

This very helpful resource talks about how to ask for feedback during the entire writing process. It contains possible questions that students might ask when developing an outline, during the revision process, and after the final draft has been graded.

"Prepare for Your Tutoring Session" (Otis College of Art and Design)

This guide from a university's student learning center contains a lot of helpful tips for getting the most out of working with a writing tutor.

"The Importance of Asking Your Professor" (Univ. of Waterloo)

This article from the university's Writing and Communication Centre's blog contains some suggestions for how and when to get help from professors and Teaching Assistants.

Once you've revised your first draft, you're well on your way to handing in a polished paper. These resources—each of them produced by writing professionals at colleges and universities—outline the steps required in order to produce a final draft. You'll find proofreading tips and checklists in text and video form.

"Developing a Final Draft of a Research Paper" (Univ. of Minnesota)

While this resource contains suggestions for revision, it also features a couple of helpful checklists for the last stages of completing a final draft.

Basic Final Draft Tips and Checklist (Univ. of Maryland-University College)

This short and accessible resource, part of UMUC's very thorough online guide to writing and research, contains a very basic checklist for students who are getting ready to turn in their final drafts.

Final Draft Checklist (Everett C.C.)

This is another accessible final draft checklist, appropriate for both high school and college students. It suggests reading your essay aloud at least once.

"How to Proofread Your Final Draft" (YouTube)

This video (approximately 5 minutes), produced by Eastern Washington University, gives students tips on proofreading final drafts.

"Proofreading Tips" (Georgia Southern-Armstrong)

This guide will help students learn how to spot common errors in their papers. It suggests focusing on content and editing for grammar and mechanics.

This final set of resources is intended specifically for high school and college instructors. It provides links to unit plans and classroom exercises that can help improve students' research and writing skills. You'll find resources that give an overview of the process, along with activities that focus on how to begin and how to carry out research. 

"Research Paper Complete Resources Pack" (Teachers Pay Teachers)

This packet of assignments, rubrics, and other resources is designed for high school students. The resources in this packet are aligned to Common Core standards.

"Research Paper—Complete Unit" (Teachers Pay Teachers)

This packet of assignments, notes, PowerPoints, and other resources has a 4/4 rating with over 700 ratings. It is designed for high school teachers, but might also be useful to college instructors who work with freshmen.

"Teaching Students to Write Good Papers" (Yale)

This resource from Yale's Center for Teaching and Learning is designed for college instructors, and it includes links to appropriate activities and exercises.

"Research Paper Writing: An Overview" (CUNY Brooklyn)

CUNY Brooklyn offers this complete lesson plan for introducing students to research papers. It includes an accompanying set of PowerPoint slides.

"Lesson Plan: How to Begin Writing a Research Paper" (San Jose State Univ.)

This lesson plan is designed for students in the health sciences, so teachers will have to modify it for their own needs. It includes a breakdown of the brainstorming, topic selection, and research question process. 

"Quantitative Techniques for Social Science Research" (Univ. of Pittsburgh)

This is a set of PowerPoint slides that can be used to introduce students to a variety of quantitative methods used in the social sciences.

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How to Write the Methods Section of a Research Paper

Writing a research paper is both an art and a skill, and knowing how to write the methods section of a research paper is the first crucial step in mastering scientific writing. If, like the majority of early career researchers, you believe that the methods section is the simplest to write and needs little in the way of careful consideration or thought, this article will help you understand it is not 1 .

We have all probably asked our supervisors, coworkers, or search engines “ how to write a methods section of a research paper ” at some point in our scientific careers, so you are not alone if that’s how you ended up here.  Even for seasoned researchers, selecting what to include in the methods section from a wealth of experimental information can occasionally be a source of distress and perplexity.   

Additionally, journal specifications, in some cases, may make it more of a requirement rather than a choice to provide a selective yet descriptive account of the experimental procedure. Hence, knowing these nuances of how to write the methods section of a research paper is critical to its success. The methods section of the research paper is not supposed to be a detailed heavy, dull section that some researchers tend to write; rather, it should be the central component of the study that justifies the validity and reliability of the research.

Are you still unsure of how the methods section of a research paper forms the basis of every investigation? Consider the last article you read but ignore the methods section and concentrate on the other parts of the paper . Now think whether you could repeat the study and be sure of the credibility of the findings despite knowing the literature review and even having the data in front of you. You have the answer!   

Having established the importance of the methods section , the next question is how to write the methods section of a research paper that unifies the overall study. The purpose of the methods section , which was earlier called as Materials and Methods , is to describe how the authors went about answering the “research question” at hand. Here, the objective is to tell a coherent story that gives a detailed account of how the study was conducted, the rationale behind specific experimental procedures, the experimental setup, objects (variables) involved, the research protocol employed, tools utilized to measure, calculations and measurements, and the analysis of the collected data 2 .

In this article, we will take a deep dive into this topic and provide a detailed overview of how to write the methods section of a research paper . For the sake of clarity, we have separated the subject into various sections with corresponding subheadings.  

Table of Contents

What is the methods section of a research paper ?  

The methods section is a fundamental section of any paper since it typically discusses the ‘ what ’, ‘ how ’, ‘ which ’, and ‘ why ’ of the study, which is necessary to arrive at the final conclusions. In a research article, the introduction, which serves to set the foundation for comprehending the background and results is usually followed by the methods section, which precedes the result and discussion sections. The methods section must explicitly state what was done, how it was done, which equipment, tools and techniques were utilized, how were the measurements/calculations taken, and why specific research protocols, software, and analytical methods were employed.  

Why is the methods section important?  

The primary goal of the methods section is to provide pertinent details about the experimental approach so that the reader may put the results in perspective and, if necessary, replicate the findings 3 .  This section offers readers the chance to evaluate the reliability and validity of any study. In short, it also serves as the study’s blueprint, assisting researchers who might be unsure about any other portion in establishing the study’s context and validity. The methods plays a rather crucial role in determining the fate of the article; an incomplete and unreliable methods section can frequently result in early rejections and may lead to numerous rounds of modifications during the publication process. This means that the reviewers also often use methods section to assess the reliability and validity of the research protocol and the data analysis employed to address the research topic. In other words, the purpose of the methods section is to demonstrate the research acumen and subject-matter expertise of the author(s) in their field.  

Structure of methods section of a research paper  

Similar to the research paper, the methods section also follows a defined structure; this may be dictated by the guidelines of a specific journal or can be presented in a chronological or thematic manner based on the study type. When writing the methods section , authors should keep in mind that they are telling a story about how the research was conducted. They should only report relevant information to avoid confusing the reader and include details that would aid in connecting various aspects of the entire research activity together. It is generally advisable to present experiments in the order in which they were conducted. This facilitates the logical flow of the research and allows readers to follow the progression of the study design.   

It is also essential to clearly state the rationale behind each experiment and how the findings of earlier experiments informed the design or interpretation of later experiments. This allows the readers to understand the overall purpose of the study design and the significance of each experiment within that context. However, depending on the particular research question and method, it may make sense to present information in a different order; therefore, authors must select the best structure and strategy for their individual studies.   

In cases where there is a lot of information, divide the sections into subheadings to cover the pertinent details. If the journal guidelines pose restrictions on the word limit , additional important information can be supplied in the supplementary files. A simple rule of thumb for sectioning the method section is to begin by explaining the methodological approach ( what was done ), describing the data collection methods ( how it was done ), providing the analysis method ( how the data was analyzed ), and explaining the rationale for choosing the methodological strategy. This is described in detail in the upcoming sections.    

How to write the methods section of a research paper  

Contrary to widespread assumption, the methods section of a research paper should be prepared once the study is complete to prevent missing any key parameter. Hence, please make sure that all relevant experiments are done before you start writing a methods section . The next step for authors is to look up any applicable academic style manuals or journal-specific standards to ensure that the methods section is formatted correctly. The methods section of a research paper typically constitutes materials and methods; while writing this section, authors usually arrange the information under each category.

The materials category describes the samples, materials, treatments, and instruments, while experimental design, sample preparation, data collection, and data analysis are a part of the method category. According to the nature of the study, authors should include additional subsections within the methods section, such as ethical considerations like the declaration of Helsinki (for studies involving human subjects), demographic information of the participants, and any other crucial information that can affect the output of the study. Simply put, the methods section has two major components: content and format. Here is an easy checklist for you to consider if you are struggling with how to write the methods section of a research paper .   

• Explain the research design, subjects, and sample details  
• Include information on inclusion and exclusion criteria  
• Mention ethical or any other permission required for the study  
• Include information about materials, experimental setup, tools, and software  
• Add details of data collection and analysis methods  
• Incorporate how research biases were avoided or confounding variables were controlled  
• Evaluate and justify the experimental procedure selected to address the research question  
• Provide precise and clear details of each experiment  
• Flowcharts, infographics, or tables can be used to present complex information     
• Use past tense to show that the experiments have been done   
• Follow academic style guides (such as APA or MLA ) to structure the content  
• Citations should be included as per standard protocols in the field  

Now that you know how to write the methods section of a research paper , let’s address another challenge researchers face while writing the methods section —what to include in the methods section .  How much information is too much is not always obvious when it comes to trying to include data in the methods section of a paper. In the next section, we examine this issue and explore potential solutions.   

What to include in the methods section of a research paper  

The technical nature of the methods section occasionally makes it harder to present the information clearly and concisely while staying within the study context. Many young researchers tend to veer off subject significantly, and they frequently commit the sin of becoming bogged down in itty bitty details, making the text harder to read and impairing its overall flow. However, the best way to write the methods section is to start with crucial components of the experiments. If you have trouble deciding which elements are essential, think about leaving out those that would make it more challenging to comprehend the context or replicate the results. The top-down approach helps to ensure all relevant information is incorporated and vital information is not lost in technicalities. Next, remember to add details that are significant to assess the validity and reliability of the study. Here is a simple checklist for you to follow ( bonus tip: you can also make a checklist for your own study to avoid missing any critical information while writing the methods section ).  

• Structuring the methods section : Authors should diligently follow journal guidelines and adhere to the specific author instructions provided when writing the methods section . Journals typically have specific guidelines for formatting the methods section ; for example, Frontiers in Plant Sciences advises arranging the materials and methods section by subheading and citing relevant literature. There are several standardized checklists available for different study types in the biomedical field, including CONSORT (Consolidated Standards of Reporting Trials) for randomized clinical trials, PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analysis) for systematic reviews and meta-analysis, and STROBE (STrengthening the Reporting of OBservational studies in Epidemiology) for cohort, case-control, cross-sectional studies. Before starting the methods section , check the checklist available in your field that can function as a guide.     
• Organizing different sections to tell a story : Once you are sure of the format required for structuring the methods section , the next is to present the sections in a logical manner; as mentioned earlier, the sections can be organized according to the chronology or themes. In the chronological arrangement, you should discuss the methods in accordance with how the experiments were carried out. An example of the method section of a research paper of an animal study should first ideally include information about the species, weight, sex, strain, and age. Next, the number of animals, their initial conditions, and their living and housing conditions should also be mentioned. Second, how the groups are assigned and the intervention (drug treatment, stress, or other) given to each group, and finally, the details of tools and techniques used to measure, collect, and analyze the data. Experiments involving animal or human subjects should additionally state an ethics approval statement. It is best to arrange the section using the thematic approach when discussing distinct experiments not following a sequential order.  
• Define and explain the objects and procedure: Experimental procedure should clearly be stated in the methods section . Samples, necessary preparations (samples, treatment, and drug), and methods for manipulation need to be included. All variables (control, dependent, independent, and confounding) must be clearly defined, particularly if the confounding variables can affect the outcome of the study.  
• Match the order of the methods section with the order of results: Though not mandatory, organizing the manuscript in a logical and coherent manner can improve the readability and clarity of the paper. This can be done by following a consistent structure throughout the manuscript; readers can easily navigate through the different sections and understand the methods and results in relation to each other. Using experiment names as headings for both the methods and results sections can also make it simpler for readers to locate specific information and corroborate it if needed.   
• Relevant information must always be included: The methods section should have information on all experiments conducted and their details clearly mentioned. Ask the journal whether there is a way to offer more information in the supplemental files or external repositories if your target journal has strict word limitations. For example, Nature communications encourages authors to deposit their step-by-step protocols in an open-resource depository, Protocol Exchange which allows the protocols to be linked with the manuscript upon publication. Providing access to detailed protocols also helps to increase the transparency and reproducibility of the research.  
• It’s all in the details: The methods section should meticulously list all the materials, tools, instruments, and software used for different experiments. Specify the testing equipment on which data was obtained, together with its manufacturer’s information, location, city, and state or any other stimuli used to manipulate the variables. Provide specifics on the research process you employed; if it was a standard protocol, cite previous studies that also used the protocol.  Include any protocol modifications that were made, as well as any other factors that were taken into account when planning the study or gathering data. Any new or modified techniques should be explained by the authors. Typically, readers evaluate the reliability and validity of the procedures using the cited literature, and a widely accepted checklist helps to support the credibility of the methodology. Note: Authors should include a statement on sample size estimation (if applicable), which is often missed. It enables the reader to determine how many subjects will be required to detect the expected change in the outcome variables within a given confidence interval.  
• Write for the audience: While explaining the details in the methods section , authors should be mindful of their target audience, as some of the rationale or assumptions on which specific procedures are based might not always be obvious to the audience, particularly for a general audience. Therefore, when in doubt, the objective of a procedure should be specified either in relation to the research question or to the entire protocol.  
• Data interpretation and analysis : Information on data processing, statistical testing, levels of significance, and analysis tools and software should be added. Mention if the recommendations and expertise of an experienced statistician were followed. Also, evaluate and justify the preferred statistical method used in the study and its significance.  

What NOT to include in the methods section of a research paper  

To address “ how to write the methods section of a research paper ”, authors should not only pay careful attention to what to include but also what not to include in the methods section of a research paper . Here is a list of do not’s when writing the methods section :  

• Do not elaborate on specifics of standard methods/procedures: You should refrain from adding unnecessary details of experiments and practices that are well established and cited previously.  Instead, simply cite relevant literature or mention if the manufacturer’s protocol was followed.  
• Do not add unnecessary details : Do not include minute details of the experimental procedure and materials/instruments used that are not significant for the outcome of the experiment. For example, there is no need to mention the brand name of the water bath used for incubation.    
• Do not discuss the results: The methods section is not to discuss the results or refer to the tables and figures; save it for the results and discussion section. Also, focus on the methods selected to conduct the study and avoid diverting to other methods or commenting on their pros or cons.  
• Do not make the section bulky : For extensive methods and protocols, provide the essential details and share the rest of the information in the supplemental files. The writing should be clear yet concise to maintain the flow of the section.  

We hope that by this point, you understand how crucial it is to write a thoughtful and precise methods section and the ins and outs of how to write the methods section of a research paper . To restate, the entire purpose of the methods section is to enable others to reproduce the results or verify the research. We sincerely hope that this post has cleared up any confusion and given you a fresh perspective on the methods section .

As a parting gift, we’re leaving you with a handy checklist that will help you understand how to write the methods section of a research paper . Feel free to download this checklist and use or share this with those who you think may benefit from it.  

References  

• Bhattacharya, D. How to write the Methods section of a research paper. Editage Insights, 2018. https://www.editage.com/insights/how-to-write-the-methods-section-of-a-research-paper (2018).
• Kallet, R. H. How to Write the Methods Section of a Research Paper. Respiratory Care 49, 1229–1232 (2004). https://pubmed.ncbi.nlm.nih.gov/15447808/
• Grindstaff, T. L. & Saliba, S. A. AVOIDING MANUSCRIPT MISTAKES. Int J Sports Phys Ther 7, 518–524 (2012). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3474299/

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What is Research Methodology? Definition, Types, and Examples

Research methodology 1,2 is a structured and scientific approach used to collect, analyze, and interpret quantitative or qualitative data to answer research questions or test hypotheses. A research methodology is like a plan for carrying out research and helps keep researchers on track by limiting the scope of the research. Several aspects must be considered before selecting an appropriate research methodology, such as research limitations and ethical concerns that may affect your research.

The research methodology section in a scientific paper describes the different methodological choices made, such as the data collection and analysis methods, and why these choices were selected. The reasons should explain why the methods chosen are the most appropriate to answer the research question. A good research methodology also helps ensure the reliability and validity of the research findings. There are three types of research methodology—quantitative, qualitative, and mixed-method, which can be chosen based on the research objectives.

What is research methodology ?

A research methodology describes the techniques and procedures used to identify and analyze information regarding a specific research topic. It is a process by which researchers design their study so that they can achieve their objectives using the selected research instruments. It includes all the important aspects of research, including research design, data collection methods, data analysis methods, and the overall framework within which the research is conducted. While these points can help you understand what is research methodology, you also need to know why it is important to pick the right methodology.

Having a good research methodology in place has the following advantages: 3

• Helps other researchers who may want to replicate your research; the explanations will be of benefit to them.
• You can easily answer any questions about your research if they arise at a later stage.
• A research methodology provides a framework and guidelines for researchers to clearly define research questions, hypotheses, and objectives.
• It helps researchers identify the most appropriate research design, sampling technique, and data collection and analysis methods.
• A sound research methodology helps researchers ensure that their findings are valid and reliable and free from biases and errors.
• It also helps ensure that ethical guidelines are followed while conducting research.
• A good research methodology helps researchers in planning their research efficiently, by ensuring optimum usage of their time and resources.

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Types of research methodology.

There are three types of research methodology based on the type of research and the data required. 1

• Quantitative research methodology focuses on measuring and testing numerical data. This approach is good for reaching a large number of people in a short amount of time. This type of research helps in testing the causal relationships between variables, making predictions, and generalizing results to wider populations.
• Qualitative research methodology examines the opinions, behaviors, and experiences of people. It collects and analyzes words and textual data. This research methodology requires fewer participants but is still more time consuming because the time spent per participant is quite large. This method is used in exploratory research where the research problem being investigated is not clearly defined.
• Mixed-method research methodology uses the characteristics of both quantitative and qualitative research methodologies in the same study. This method allows researchers to validate their findings, verify if the results observed using both methods are complementary, and explain any unexpected results obtained from one method by using the other method.

What are the types of sampling designs in research methodology?

Sampling 4 is an important part of a research methodology and involves selecting a representative sample of the population to conduct the study, making statistical inferences about them, and estimating the characteristics of the whole population based on these inferences. There are two types of sampling designs in research methodology—probability and nonprobability.

• Probability sampling

In this type of sampling design, a sample is chosen from a larger population using some form of random selection, that is, every member of the population has an equal chance of being selected. The different types of probability sampling are:

• Systematic —sample members are chosen at regular intervals. It requires selecting a starting point for the sample and sample size determination that can be repeated at regular intervals. This type of sampling method has a predefined range; hence, it is the least time consuming.
• Stratified —researchers divide the population into smaller groups that don’t overlap but represent the entire population. While sampling, these groups can be organized, and then a sample can be drawn from each group separately.
• Cluster —the population is divided into clusters based on demographic parameters like age, sex, location, etc.
• Convenience —selects participants who are most easily accessible to researchers due to geographical proximity, availability at a particular time, etc.
• Purposive —participants are selected at the researcher’s discretion. Researchers consider the purpose of the study and the understanding of the target audience.
• Snowball —already selected participants use their social networks to refer the researcher to other potential participants.
• Quota —while designing the study, the researchers decide how many people with which characteristics to include as participants. The characteristics help in choosing people most likely to provide insights into the subject.

What are data collection methods?

During research, data are collected using various methods depending on the research methodology being followed and the research methods being undertaken. Both qualitative and quantitative research have different data collection methods, as listed below.

Qualitative research 5

• One-on-one interviews: Helps the interviewers understand a respondent’s subjective opinion and experience pertaining to a specific topic or event
• Document study/literature review/record keeping: Researchers’ review of already existing written materials such as archives, annual reports, research articles, guidelines, policy documents, etc.
• Focus groups: Constructive discussions that usually include a small sample of about 6-10 people and a moderator, to understand the participants’ opinion on a given topic.
• Qualitative observation : Researchers collect data using their five senses (sight, smell, touch, taste, and hearing).

Quantitative research 6

• Sampling: The most common type is probability sampling.
• Interviews: Commonly telephonic or done in-person.
• Observations: Structured observations are most commonly used in quantitative research. In this method, researchers make observations about specific behaviors of individuals in a structured setting.
• Document review: Reviewing existing research or documents to collect evidence for supporting the research.
• Surveys and questionnaires. Surveys can be administered both online and offline depending on the requirement and sample size.

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What are data analysis methods.

The data collected using the various methods for qualitative and quantitative research need to be analyzed to generate meaningful conclusions. These data analysis methods 7 also differ between quantitative and qualitative research.

Quantitative research involves a deductive method for data analysis where hypotheses are developed at the beginning of the research and precise measurement is required. The methods include statistical analysis applications to analyze numerical data and are grouped into two categories—descriptive and inferential.

Descriptive analysis is used to describe the basic features of different types of data to present it in a way that ensures the patterns become meaningful. The different types of descriptive analysis methods are:

• Measures of frequency (count, percent, frequency)
• Measures of central tendency (mean, median, mode)
• Measures of dispersion or variation (range, variance, standard deviation)
• Measure of position (percentile ranks, quartile ranks)

Inferential analysis is used to make predictions about a larger population based on the analysis of the data collected from a smaller population. This analysis is used to study the relationships between different variables. Some commonly used inferential data analysis methods are:

• Correlation: To understand the relationship between two or more variables.
• Cross-tabulation: Analyze the relationship between multiple variables.
• Regression analysis: Study the impact of independent variables on the dependent variable.
• Frequency tables: To understand the frequency of data.
• Analysis of variance: To test the degree to which two or more variables differ in an experiment.

Qualitative research involves an inductive method for data analysis where hypotheses are developed after data collection. The methods include:

• Content analysis: For analyzing documented information from text and images by determining the presence of certain words or concepts in texts.
• Narrative analysis: For analyzing content obtained from sources such as interviews, field observations, and surveys. The stories and opinions shared by people are used to answer research questions.
• Discourse analysis: For analyzing interactions with people considering the social context, that is, the lifestyle and environment, under which the interaction occurs.
• Grounded theory: Involves hypothesis creation by data collection and analysis to explain why a phenomenon occurred.
• Thematic analysis: To identify important themes or patterns in data and use these to address an issue.

How to choose a research methodology?

Here are some important factors to consider when choosing a research methodology: 8

• Research objectives, aims, and questions —these would help structure the research design.
• Review existing literature to identify any gaps in knowledge.
• Check the statistical requirements —if data-driven or statistical results are needed then quantitative research is the best. If the research questions can be answered based on people’s opinions and perceptions, then qualitative research is most suitable.
• Sample size —sample size can often determine the feasibility of a research methodology. For a large sample, less effort- and time-intensive methods are appropriate.
• Constraints —constraints of time, geography, and resources can help define the appropriate methodology.

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How to write a research methodology .

A research methodology should include the following components: 3,9

• Research design —should be selected based on the research question and the data required. Common research designs include experimental, quasi-experimental, correlational, descriptive, and exploratory.
• Research method —this can be quantitative, qualitative, or mixed-method.
• Reason for selecting a specific methodology —explain why this methodology is the most suitable to answer your research problem.
• Research instruments —explain the research instruments you plan to use, mainly referring to the data collection methods such as interviews, surveys, etc. Here as well, a reason should be mentioned for selecting the particular instrument.
• Sampling —this involves selecting a representative subset of the population being studied.
• Data collection —involves gathering data using several data collection methods, such as surveys, interviews, etc.
• Data analysis —describe the data analysis methods you will use once you’ve collected the data.
• Research limitations —mention any limitations you foresee while conducting your research.
• Validity and reliability —validity helps identify the accuracy and truthfulness of the findings; reliability refers to the consistency and stability of the results over time and across different conditions.
• Ethical considerations —research should be conducted ethically. The considerations include obtaining consent from participants, maintaining confidentiality, and addressing conflicts of interest.

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The methods section is a critical part of the research papers, allowing researchers to use this to understand your findings and replicate your work when pursuing their own research. However, it is usually also the most difficult section to write. This is where Paperpal can help you overcome the writer’s block and create the first draft in minutes with Paperpal Copilot, its secure generative AI feature suite.  

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• Choose the right words: Enhance text by choosing contextual synonyms based on how the words have been used in previously published work.  
• Check and verify text : Make sure the generated text showcases your methods correctly, has all the right citations, and is original and authentic. .   

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Frequently Asked Questions

Q1. What are the key components of research methodology?

A1. A good research methodology has the following key components:

• Research design
• Data collection procedures
• Data analysis methods
• Ethical considerations

Q2. Why is ethical consideration important in research methodology?

A2. Ethical consideration is important in research methodology to ensure the readers of the reliability and validity of the study. Researchers must clearly mention the ethical norms and standards followed during the conduct of the research and also mention if the research has been cleared by any institutional board. The following 10 points are the important principles related to ethical considerations: 10

• Participants should not be subjected to harm.
• Respect for the dignity of participants should be prioritized.
• Full consent should be obtained from participants before the study.
• Participants’ privacy should be ensured.
• Confidentiality of the research data should be ensured.
• Anonymity of individuals and organizations participating in the research should be maintained.
• The aims and objectives of the research should not be exaggerated.
• Affiliations, sources of funding, and any possible conflicts of interest should be declared.
• Communication in relation to the research should be honest and transparent.
• Misleading information and biased representation of primary data findings should be avoided.

Q3. What is the difference between methodology and method?

A3. Research methodology is different from a research method, although both terms are often confused. Research methods are the tools used to gather data, while the research methodology provides a framework for how research is planned, conducted, and analyzed. The latter guides researchers in making decisions about the most appropriate methods for their research. Research methods refer to the specific techniques, procedures, and tools used by researchers to collect, analyze, and interpret data, for instance surveys, questionnaires, interviews, etc.

Research methodology is, thus, an integral part of a research study. It helps ensure that you stay on track to meet your research objectives and answer your research questions using the most appropriate data collection and analysis tools based on your research design.

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• Research methodologies. Pfeiffer Library website. Accessed August 15, 2023. https://library.tiffin.edu/researchmethodologies/whatareresearchmethodologies
• Types of research methodology. Eduvoice website. Accessed August 16, 2023. https://eduvoice.in/types-research-methodology/
• The basics of research methodology: A key to quality research. Voxco. Accessed August 16, 2023. https://www.voxco.com/blog/what-is-research-methodology/
• Sampling methods: Types with examples. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/types-of-sampling-for-social-research/
• What is qualitative research? Methods, types, approaches, examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-qualitative-research-methods-types-examples/
• What is quantitative research? Definition, methods, types, and examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-quantitative-research-types-and-examples/
• Data analysis in research: Types & methods. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/data-analysis-in-research/#Data_analysis_in_qualitative_research
• Factors to consider while choosing the right research methodology. PhD Monster website. Accessed August 17, 2023. https://www.phdmonster.com/factors-to-consider-while-choosing-the-right-research-methodology/
• What is research methodology? Research and writing guides. Accessed August 14, 2023. https://paperpile.com/g/what-is-research-methodology/
• Ethical considerations. Business research methodology website. Accessed August 17, 2023. https://research-methodology.net/research-methodology/ethical-considerations/

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• Taotao Pan 1 , 2 ,
• Bohan Zhang   ORCID: orcid.org/0000-0001-6654-3567 1 , 2 ,
• Bowen Ma 1 , 2 ,
• Chuan Chen 1 , 2 ,
• Junfu Guo   ORCID: orcid.org/0000-0002-4195-7031 3 ,
• Chang Shi 3 ,
• Mei Li   ORCID: orcid.org/0000-0003-3310-2911 2 ,
• Chao Liu   ORCID: orcid.org/0009-0008-6892-6754 1 , 2 ,
• Yuxiang Li   ORCID: orcid.org/0000-0002-1575-3692 2 , 5 , 6 ,
• Yong Zhang   ORCID: orcid.org/0000-0001-9950-1793 2 , 5 , 6 &
• Shuangsang Fang   ORCID: orcid.org/0000-0002-4126-0074 1 , 2  

Nature Communications volume  15 , Article number:  7806 ( 2024 ) Cite this article

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• Bioinformatics
• Computational models
• Data processing
• Transcriptomics

Three-dimensional Spatial Transcriptomics has revolutionized our understanding of tissue regionalization, organogenesis, and development. However, existing approaches overlook either spatial information or experiment-induced distortions, leading to significant discrepancies between reconstruction results and in vivo cell locations, causing unreliable downstream analysis. To address these challenges, we propose ST-GEARS (Spatial Transcriptomics GEospatial profile recovery system through AnchoRS). By employing innovative Distributive Constraints into the Optimization scheme, ST-GEARS retrieves anchors with exceeding precision that connect closest spots across sections in vivo. Guided by the anchors, it first rigidly aligns sections, next solves and denoises Elastic Fields to counteract distortions. Through mathematically proved Bi-sectional Fields Application, it eventually recovers the original spatial profile. Studying ST-GEARS across number of sections, sectional distances and sequencing platforms, we observed its outstanding performance on tissue, cell, and gene levels. ST-GEARS provides precise and well-explainable ‘gears’ between in vivo situations and in vitro analysis, powerfully fueling potential of biological discoveries.

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Introduction.

Spatial transcriptomics (ST) is an omics technology that fuels biological research based on measuring gene expression on each position-recorded spot across sliced tissues 1 , 2 , 3 . Notably, a range of methods has been developed. In vivo sequencing (ISS) 4 platforms such as Barcoded Anatomy Resolved by Sequencing (BARseq) 5 and Spatially-resolved Transcript Amplicon Readout Mapping (STARmap) 6 rely on amplification, hybridization and imaging process to capture gene expression information. Next Generation Sequencing (NGS) 7 platform such as Visium 1 , Stereo-seq 8 and Slide-Seq2 9 uses spatial barcoding and capturing in their implementations. These methods offer various sequencing resolutions ranging from 100 µm 10 , 11 to 500 nm 8 , and can measure thousands 5 to tens of thousands 8 of genes simultaneously.

Single-slice ST studies have unleashed discoveries, and facilitated our understanding in diverse biological and medical fields 9 , 12 , 13 , 14 , 15 . Consequently, numerous processing pipelines and analysis models have been developed for ST data on a single section 16 , 17 , 18 , 19 , 20 , 21 . However, to truly capture transcriptomics in the real-world context, three-dimensional (3D) ST was designed to recover biological states and processes in real-world dimensions, without restriction of the isolated planes in single sectional ST studies. Various research has utilized the power of 3D ST to uncover insights in homeostasis, development, and diseases. Among them, Wang et al. 22 uncovered spatial cell state dynamics of Drosophila larval testis and revealed potential regulons of transcription factors. Mohenska et al. 23 revealed complex spatial patterns in Murine heart and identified novel markers for cardiac subsections. And Vickovic et al. 24 explored cell type localizations in Human rheumatoid arthritis synovium. The vast and large variety of downstream 3D research has posted the need for a reliable and automatic recovery method of in vivo spatial profile.

However, the collection process of ST data casts significant challenges onto the accurate reconstruction of 3D ST and the situation has not been overcome by current explorations. Specifically, in 3D ST experiments, individual slices are cross sectioned in a consistent direction, then manually placed on different chips or slides 14 , 25 . This operation introduces varying geospatial reference systems of distinct sections, and coordinates are distorted compared to their in vivo states. The distortions occur due to squeezing and stretching effects during the picking, holding, and relocation of the sections. Such different geospatial systems and distortions complicates the recovery of in vivo 3D profile. Among current recovery approaches, STUtility 26 realizes multi-section alignment through the registration of histology images, without considering either geospatial or molecular profile of mRNA, which leads to compromised accuracies. Recently published method PASTE 27 , and its second version PASTE2 28 achieve alignment using both gene expression and coordinate information, through optimization of mapping between individual spots across sections. These methods cause inaccurate mappings and produces rotational misalignments due to the nonadaptive regularization factors, and their uniform sum of probability assigned to all spots upon presence of spots without actual anchors. All above approaches only consider rigid alignment, yet neglect the correction of shape distortions, resulting in shape inconsistency across registered sections. Published method Gaussian Process Spatial Alignment (GPSA) 29 considers shape distortions in its alignment, yet it doesn’t involve structural consistency in its loss function, which can cause the model to overfit to local gene expression similarities, leading to mistaken distortions of spatial information. Moreover, its hypothesis space involves readout prediction in addition to coordinates alignment, causing uncertainty in direction of gradient descent, and vulnerabilities to input noises. Another alignment approach, Spatial-linked alignment tool (SLAT) 30 also focuses on anchors construction between sections, yet it doesn’t provide a methodology to construct 3D transcriptomics profile. Other tools focus on analysis and visualization of 3d data, such as Spateo 31 , VT3D 32 and StereoPy 33 .

To address these limitations, we introduce ST-GEARS, a 3D geospatial profile recovery approach designed for ST experiments. By formulating the problem using the framework of Fused Gromov-Wasserstein (FGW) Optimal Transport (OT) 34 , ST-GEARS incorporates both gene expression and structural similarity into the Optimization process to retrieve cross-sectional mappings of spots with the same in vivo planar positions, also referred to as ‘anchors’. During this process, we introduce innovative Distributive Constraints that allow for different emphasis on distinct spot groups. The strategy addresses importance of expression consistent groups and suppresses inconsistent groups from imposing disturbances to optimization. Hence it increases anchor accuracy compared to current approaches. ST-GEARS utilizes the retrieved anchors to initially perform rigid alignment of sections. Subsequently, it introduces Elastic Field guided by the anchors to represent the deformation and knowledge to correct it according to each spot’s location. To enhance the quality of the field, Gaussian Smoothing is applied for denoising purposes. ST-GEARS then applies Bi-sectional Application to correction of each section’s spatial profile based on its denoised fields calculated with its neighboring sections. With validity proved mathematically, Bi-sectional Application eliminates distortions of sections, resulting in the successful recovery of a 3D in vivo spatial profile.

To understand effects of ST-GEARS, we first studied its counterparts with innovations including anchors retrieval and elastic registration, respectively on Human dorsolateral prefrontal cortex (DLPFC) 35 , and Drosophila larva 22 . We found an advanced anchors accuracy of ST-GEARS compared to other available methods involving anchor’s concept and unveiled Distributive Constraints as reason behind the advancement. We validated the effectiveness of elastic registration process of ST-GEARS on both tissue shape smoothness and cross-sectional consistency. Then, we studied output of ST-GEARS and other methods on their reconstruction of Mouse hippocampus tissues 36 , Drosophila embryo individual 22 and a complete Mouse brain 37 . The result was studied on morphological, cell and gene levels. ST-GEARS was found to be the only method that correctly reconstruct on all cases despite of cross-sectioning distance, number of sections, and sequencing platforms, and it was found to output the most accurate spatial information under both annotation type or clustering information, and hybridization evidence.

ST-GEARS algorithm

ST-GEARS uses ST data as its inputs, including mRNA expression, spatial coordinates as well as approximate grouping information such as clustering or annotation of each observation. Then it recovers 3D geospatial profile in following steps (Fig.  1 ).

a The automatic pipeline of ST-GEARS which recovers ST-GEARS 3D in vivo spatial information by ordered steps including Fused Gromov Wasserstein (FGW) Optimal Transport (OT) problem parameter computing, problem formulating and solving which outputs probabilistic anchors across sections, rigid registration through Procrustes Analysis which solves optimal positional alignment using the anchors, and finally elastic registration. The input of the method is Unique molecular identifier (UMI) counts and location of each spot measured by ST technology, along with their annotations or cross-section clustering result. And the output of the method is recovered 3D in vivo spatial information of the experimented tissue, or sample. b FGW OT problem parameter computing, which assigns nonuniform weights to spots in preparation for future problem formulating, based on cross-sectional similarity of annotation types or clusters. c FGW OT problem formulating, whose setting aims to solve probabilistic anchors joining spots with highest in vivo proximity, through optimizing the combination of gene expression and structural similarity 34 . FGW OT problem solving, which is implemented based on Conditional Gradient (CG) method, leading to retrieved probabilistic anchors. d Elastic registration, which utilizes the anchors again to compute and denoise distortion fields which guides the elimination of distortions, then applies the fields bi-sectionally to positionally aligned sections, leading to the recovered 3D in vivo spatial information.

(1) Optimization problem formulation under scheme of FGW OT with enhancement of Distributive Constraints. FGW OT formulation is established to enable solving of ‘anchors’, which are the joining of pair of spots with same in vivo planar positions. Noticeably, each solved anchor is equipped with a probability that describes its strength of connection, and each spot is solved to have zero to multiple anchors. Among each two sections, section-specific groups of spots, and genes are initially excluded from the formulation to avoid causing disturbances to anchors computing. Considering that connected spots are more spatially approximate, and more similar in gene expression because of shared cell identity 38 , 39 , FGW was adopted to combine the gene expression and structural terms in optimization, enabling highest gene expression similarity between mapped spots, at the same time keeping similar spot positions relative to their sections. Moreover, an innovative Distributive Constraints setting is designed and integrated into FGW OT’s formulation, to assign higher emphasis on spots or cells whose annotation or cluster express high similarity across section, and vice versa. Distributive Constraints leads registration to rely more on expression-consistent regions of sections, hence largely enhancing both accuracy of anchors and precision of following rigid and elastic registration.

(2) Optimization problem solving utilizing self-adaptive regularization and conditional gradient descent. Our designed Self-adaptive Regularization strategy automatically determines the relative importance between gene expression and structural terms in the optimization problem. This strategy leads to an optimal regularization factor across different section distances, spot sizes, extent of distortions, and data quality such as level of diffusion. Conditional Gradient 34 is adopted as optimizer, which updates anchors iteratively towards higher expression and structural similarity with each iteration. The efficacy of Conditional Gradient has been demonstrated through its convergence to a local optimal point 40 , thereby ensuring the robustness and effectiveness of our approach.

(3) Rigid registration by Procrustes Analysis 41 . After filtering out anchors with relatively low probabilities, the optimal transformation and rotation of each section are analytically solved through Procrustes Analysis, which minimizes summed spatial distances of spots anchored to each other. With the transformation and rotation applied, sections are positionally aligned.

(4) Elastic registration guided by anchors. Based on rigid registration result and anchors solved by FGW OT, elastic registration is implemented through the process including elastic field inference, 2D Gaussian denoising, and bi-sectional fields application. Based on each rigidly registered section, elastic fields is inferred leveraging the location difference between its own spots and its anchored spots on anterior and posterior neighbor sections. An elastic field is a 2D displacement distribution, describing how displacement values are distributed across different locations. Making use of continuity of deformation at local scales, 2D Gaussian Denoising convolutes all over the fields to reduce noises. With denoised fields, our designed Bi-sectional Fields Application corrects each section’s deformation according to its fields calculated with anterior and posterior neighbor sections. The bi-sectional correction method is mathematically proved to approximately recover each section’s spatial profile to its original state.

Enhancement of anchor retrieval accuracy through distributive constraints

As was unfolded, ST-GEARS is an algorithm flow jointly constituted of probabilistic anchor computation and spatial information recovery. Hence, to validate the effectiveness of our method and demonstrate its underlying design philosophy, we conducted comprehensive studies on the two counterparts using real-world data. To begin, we utilized the DLPFC dataset 35 to study our anchors retrieving accuracy with emphasis on the effect of Distributive Constraints design.

To assess the effects of Distributive Constraints on anchor accuracy, we compared ST-GEARS with and without this setting, and with other constraints involving methods including PASTE, PASTE2 and SLAT. We investigated constraint values assigned by these methods, as well as their solved number of anchors and maximum anchor probability of each spot. Furthermore, we examined the annotation types that were considered connected based on the computed anchors to assess accuracy of anchors. Among the methods we compared, ST-GEARS with Distributive Constraints was found to assign different constraint values to spots within different neuron layers, while the others assigned uniform constraints to all layers (Fig.  2a , Supplementary Fig.  1 ). The results of ST-GEARS showed that both number of anchors and the anchors’ maximum probabilities for each spot were lower in Layer 2 and Layer 4 compared to the thicker layers. However, this pattern was not observed in methods without Distributive Constraints setting (Fig.  2a , Supplementary Fig.  1 ). To illustrate the impact of this strategy on anchor accuracy, we tagged each spot with annotation of its connected spot by anchor with highest probability. We then compared this result to the tagged spot’s original annotation (Fig.  2a , Supplementary Fig.  1 ). Under Distributive Constraints, ST-GEARS achieved a significantly higher proximity between annotations compared to PASTE and our method without Distributive Constraints. PASTE2 also led to approximate annotations, but it anchored multiple spots to spots from neighboring layers, particularly those near layer boundaries. SLAT also mapped multiple spots to spots from different tissue layers, particularly of spots located on layer 2, 4 and 6.

a (from left to right) 1st and 2nd human dorsolateral prefrontal cortex (DLPFC) section of patient #3 by Maynard et al. 35 with their provided annotations and our anchors showcase, (of the same section pair) probabilistic constraints settings in Optimal Transport (OT) problem formulating, no. of anchors computed on each spot, max. anchor probability value computed of each spot, and annotation type mapped back to spots through computed anchors; (from top to bottom) respectively by PASTE, PASTE2, SLAT, ours without distributive constraints setting, and ours. The distinction of different annotation types on the 1st section is marked by dotted lines. Mapping accuracy is used to measure accuracy of anchors and is marked alongside respective annotation type mapping visualizations. b Mapping accuracy measured on anchors of sections pairs used in ( b ) by PASTE, PASTE2, SLAT, and ST-GEARS. c Comparison of no. of anchors histograms between ST-GEARS and ST-GEARS without distributive constraints, of sections pairs of 1st and 2nd, 2nd and 3rd, and 3rd and 4th sections. The Probability Density Function (PDF) estimated by Gaussian kernel was plotted in dotted lines with the same color of histograms, to highlight the distribution differences. Source data are provided as a Source Data file.

To evaluate the precision of anchors, we conducted a comparison with the Mapping accuracy index introduced by PASTE 27 . This index measures the weighted percentage \({\sum}_{i,j,l(i)=l(j)}{\pi }_{{ij}}\) of anchors that connect spots with same annotation. As a result, ST-GEARS outperformed PASTE2 and SLAT, and reached a score that was over 0.5 (out of 1) higher than both PASTE and our method without Distributive Constraints (Fig.  2a , b , Supplementary Fig.  1 ).

To uncover the reasons behind the aforementioned phenomena, as the functional area in between thicker neocortical layers, thinner neocortical layers have comparable transcriptomic similarity with their adjacent layers in gene expression, than with its own annotation type 1 , 35 . This implies that, in contrast to thicker layers, thinner layers tend to introduce more disturbances during anchor computation. However, the Distributive Constraints imposed suppression on these annotation types by assigning a smaller sum of probability to each of their spots. The suppression was reflected in above results where each spot in Layer 2 and Layer 4 has fewer assigned anchors and a lower maximum probability (Fig.  2a , Supplementary Fig.  1 ). Further analysis on all spots in the DLPFC reveals that a certain percentage of spots were suppressed in anchor generation due to the Distributive Constraints (Fig.  2c , Supplementary Fig.  2 ).

Recovery of in vivo shape profile through elastic registration

We then utilized Drosophila larva data to investigate the spatial profile recovery effect of ST-GEARS, with an emphasis on our innovated elastic registration. We first applied rigid registration to Drosophila larva sections and observed a visually aligned configuration of individual sections (Supplementary Fig.  3 ). By further mapping cell annotations back to their previous sections, according to the strongest anchors of each spot, the projected annotations are visually in match with original ones (Supplementary Fig.  4 ). The accuracy of the mapping matching between annotations was quantified by Mapping accuracy (Supplementary Fig.  5 ). The above findings validated that ST-GEARS produced reliable anchors and accurately aligned sections through rigid registration. However, when stacking the sections together, we observed an inconsistency on the edge of lateral cross-section of the rigid result (Supplementary Fig.  6 ). This inconsistency doesn’t conform to the knowledge of intra-tissue and overall structural continuity of Drosophila larvae.

After applying elastic registration to the rigidly-aligned larva, we observed a notable improvement in the continuity of the cross section above, indicating a closer-to-real spatial information being retrieved. To further understand the effect of elastic operation on the dataset, we compared the changes in area of the complete body and three individual tissues (trachea, central nervous system (CNS), and fat body) on all sections. We observed an enhanced smoothness in the curves of elastically registered sections, which aligns with the continuous morphology of the larva as expected by theoretical knowledge. To quantify the smoothing effect, we calculated Scale-independent Standard Deviation of Differences ( \({SI}-{STD}-{DI}={STD}(\{{s}_{i}-{s}_{i-1}:i\in [{\mathrm{1,2}},...,I-1]\})/{|mean}(\{{s}_{i}-{s}_{i-1}:i\in [{\mathrm{1,2}},...,I-1]\})|\) ) onto the curves, which measures the smoothness of area changes along the sectioning direction (Fig.  3a and Methods). A decrease of SI-STD-DI on all tissues and the body provided empirical evidence for the improved smoothness. To further investigate the recovery of internal structures, we introduced Mean Structural Similarity (MSSIM). MSSIM takes structurally consistent sections as input, and measures pairwise internal similarity of reconstructed result using annotations or clustering information (Supplementary Fig.  7 ). (See Methods for details). An improved MSSIM was noticed on all 4 sections, indicating that elastic registration further recovers internal geospatial continuity on basis of rigid operation(Fig.  3b ). By comparing registration effect of individual sections, we also observed that the elastic process successfully rectified a bending flaw along the edge of the third section, (Fig.  3c ). The shape fixing highlighted that ST-GEARS not only yielded a more structurally consistent 3D volume, but also provided a more accurate morphology for single sections. The improved smoothness, the recovered structural continuity, and the shape fixing collectively demonstrate that elastic registration effectively recovers geospatial profile.

a A comparison of area changes of 3 tissues and complete body of Drosophila Larva, between result of rigid registration and result of elastic registration appended to rigid registration. The areas are calculated based on recovered spot position of different tissues along cross-sectioning direction. Standard Deviation of Differences (SI-STD-DI) quantifying the smoothness is marked alongside each curve. b A comparison of structural accuracy, measured by Mean Structural Similarity (MSSIM), of selected section pairs from Drosophila Larva (L3), between result of rigid registration only and result of elastic registration appended to rigid registration. The chosen section pairs are the structurally consistent ones. c Comparison of individual sections recovered by rigid registration only and by elastic registration appended to rigid registration, of 1st to 5th section of Drosophila Larva (L3). Shape correction of bended area in the 3 rd section, and increased cross-sectional consistency on the 4th and 5th section were highlighted by blue arrows. Source data are provided as a Source Data file.

With elastic process validated and applied onto rigid registration result, the recovery of spatial information was completed. Stacking individual sections of the elastic result, a complete geospatial profile of the larva was generated (Supplementary Fig.  8 ), visualizing the ST-GEARS’ ability of in vivo spatial information recovery.

Application to sagittal sections of Mouse hippocampus

After validating the component phases of ST-GEARS, we proceeded to apply the method to multiple real-world problems to recover geospatial profiles. We first focused on two sagittal sections of Mouse hippocampus 36 (Supplementary Fig.  9 ) that were 10 μm apart, accounting for 1–2 layers of Cornu Ammonis (CA) 1 neurons 42 . Considering the proximity of these sections, we assumed no structural differences between them.

To compare the differences of registration effect among methods, we extracted CA fields and dentate gyrus (DG) beads (Supplementary Fig.  10 ), then stacked the two sections for a more obvious contrast (Fig.  4a ). PASTE2 failed in performing the registration, leaving the sections unaligned. By GPSA, the sections’ positions were aligned, yet the 2nd section were squeezed into a narrower region than first one, leading to a contradiction of region’s location. The ‘narrowing’ phenomena may be caused by the overfitting of GPSA model on gene expression similarity, since it doesn’t involve structural similarity between registered sections in loss function. The scale on horizontal and vertical axis was distorted due to the equal scale range strategy adopted in GPSA’s preprocessing. STalign also misaligned the sections, leaving an obvious angle between two slices in registration result. This may be due to the method’s processing of ST data into images which completely relies on gene expression abundance to decide pixel intensities. On the sagittal section of Mouse hippocampus, the abundance difference between regions may not provide sufficient structural information required by registration. In the comparison between PASTE and ST-GEARS, our method demonstrates a more accurate centerline overlapping of CA fields and DG compared to PASTE. This indicated an enhanced recovery of spatial structure consistency and an improved registration effect. To quantitatively evaluate these findings, we utilized the MSSIM index as a measure of structural consistency and compared it among PASTE, PASTE2, GPSA, STalign and ST-GEARS (Fig.  4b ). Consistent with the results of centerline, ST-GEARS achieved a higher MSSIM score than GPSA and PASTE, surpassing PASTE2 and STalign by >0.2 out of 1. By comparing memory efficiency across all methods, ST-GEARS and PASTE used ~1 GB less memory than PASTE2, GPSA and STalign, and the peak memory across ST-GEARS and PASTE was almost the same (Supplementary Fig.  11 ). In perspective of time efficiency, registration utilizing ST-GEARS, STalign, GPSA and PASTE was much faster than PASTE2.

a Stacked projections of Cornu Ammonis (CA) fields and dentate gyrus (DG), of pre-registered and registered result of Mouse hippocampus sagittal sections with 10 µm distance, respectively by PASTE, PASTE2, GPSA, STalign and ST-GEARS. b A comparison of both MSSIM measuring structural accuracy and Mapping accuracy measuring anchor accuracy of the 2 registered sections, across PASTE, PASTE2, GPSA, STalign and ST-GEARS. c Stacked projections of region-specific annotation types including DG, Neurogenesis, subiculum, CA1, CA2 and CA3, registered by ST-GEARS. Each column highlights the stacked projection of a single annotation type. Source data are provided as a Source Data file.

To understand reasons behind our enhancement, we thoroughly examined the anchors generated by PASTE, PASTE2 and ST-GEARS, as well as the effects of our elastic registration. By mapping cluster information of the 2nd section to the 1st, and the 1st to the 2nd through anchors, we found correspondences between the projected and original annotations (Supplementary Fig.  12 ). Accordingly, our Mapping accuracy was over 0.25 higher than PASTE and over 0.45 than PASTE2 (Fig.  4a ), indicating our exceptional anchor accuracy. To understand and further substantiate this advantage, we visualized the probabilistic constraints and its resulted anchors probabilities (Supplementary Fig.  13a ). It is worth noting that ST-GEARS implemented Distributive Constraints, in contrast to the uniform distributions used by PASTE. As a result, a certain percentage of spots were found to be suppressed in anchors connection by ST-GEARS (Supplementary Fig.  13b ) compared to PASTE, leaving the registration to rely more on spots with higher cross-sectional similarity and less computational disturbances, and hence lead to a higher anchor accuracy. We excluded Distributive Constraints from ST-GEARS, and noticed an obvious decrease of mapping accuracy on the hippocampus dataset (Supplementary Fig.  14 ), indicating the contribution of Distributive Constraints on anchors accuracy. In the study of elastic effect, we found an increased overlapping of centerlines by elastic registration than by rigid operation only when overlapping CA fields and DG (Fig.  4b ). Quantitively by MSSIM, the cross-sectional similarity was found to be increased by elastic registration (Supplementary Fig.  15 ). These findings suggest that the combination of Distributive Constraints and elastic process contributed to the enhanced registration of the Mouse hippocampus.

To explore the potential effect of impact of our registration on downstream analysis, we extracted region-specific annotation types from the sections, and analyzed their overlapping through stacking registered sections together (Fig.  4c ). In all annotation types including DG, Neurogenesis, subiculum, CA1, CA2 and CA3, the distribution regions from both sections were nearly identical. The overlapping result unveils that ST-GEARS integrated the spatial profile of same cell subpopulations, enabling a convenient and accurate downstream analysis of multiple sections.

Application to 3D reconstruction of Drosophila embryo

Besides tissue level registration of Mouse hippocampus, to evaluate the performance of ST-GEARS in reconstructing individual with multiple sections, we further tested it on a Drosophila embryo. The transcriptomics of embryo was measured by Stereo-seq, with 7 μm cross-sectioning distance 22 . By quantifying the registration effect of spatial information recovery and comparing it to PASTE, PASTE2, GPSA and STalign, we found that ST-GEARS achieved the highest MSSIM in five out of the six structurally consistent pairs (Fig.  5a ). On the pair where ST-GEARS did not result in highest MSSIM, it surpassed PASTE, and achieved a similar score to PASTE2. By comparing area changes with SI-STD-DI quantification of the complete section, and three individual tissues including epidermis, midgut and foregut, ST-GEARS yielded higher smoothness on all regions than all other approaches, both visually and quantitatively (Fig.  5b ).

a A comparison of Mean Structural Similarity (MSSIM) measuring structural similarity, of section pairs that are structurally consistent from Drosophila Embryo (E14-16h), between reconstruction results of PASTE, PASTE2, GPSA, STalign and ST-GEARS. b A comparison of area changes of 3 tissues and complete body of Drosophila Embryo, along cross-sectioning direction, between reconstruction result of PASTE, PASTE2, GPSA, STalign and ST-GEARS. Standard Deviation of Differences (SI-STD-DI) which measures structural consistency is marked alongside each curve to quantify the smoothness. The smoothness difference of ST-GEARS compared to PASTE, PASTE2 and STalign are highlighted by orange rectangles. c Reconstructed individual sections with recovered spatial location of each spot. In result of PASTE, the incorrect flipping on the 15th section was highlighted in orange. In result of PASTE2, gradual rotations were marked by the 1st, 5th, 9th, 13th and 16th sections’ approximate symmetry axis whereas symmetry axis of the 1st section was replicated onto the 16th for angle comparison. In result of GPSA, mistakenly distorted sections were marked by purple arrows. In result of STalign, the incorrect flipping on the 13th section was highlighted in orange. In result of ST-GEARS, the fix of dissecting area on the 15th section was marked by a blue arrow. d Dorsal view of 3D reconstructed Drosophila embryo by PASTE, PASTE2, GPSA, STalign and ST-GEARS. The inaccurate regionalization of midgut was circled and pointed with arrow in orange. The resulted extruding part of single section by PASTE2 was circled and pointed in blue. e Mapping accuracy of all section pairs by PASTE, PASTE2 and ST-GEARS. f By dorsal view, regionalization of marker gene Cpr56F and Osi7 by PASTE, PASTE2, GPSA, STalign and ST-GEARS, and their comparison with hybridization result from Berkeley Drosophila Genome Project (BDGP) database. The gathering expression regions were highlighted by dotted lines. Source data are provided as a Source Data file.

To compare the reconstruction effect, we studied both registered individual section, and reconstructed 3D volume. Among the methods compared, PASTE produced a wrong flipping on the 15 th section along A-P axis (Fig.  5c ). Stacking sections back to 3D and investigating on dorsal view, the wrong flipping caused a false regionalization of foregut circled in orange (Fig.  5d ). Along the first to last section registered by PASTE2, a gradual rotation was witnessed (Fig.  5c ), leading to over 20 degrees of angular misalignment between the first and the last section. Similar to PASTE, this misalignment also caused the wrong regionalization of foregut in 3D map (Fig.  4d ). Equally induced by the rotation, sections were found to extrude in the 3D result circled in blue, breaking the round overall morphology of the embryo. GPSA caused false distortion of 8 out of 16 sections as pointed by purple arrows (Fig.  5c ) and the stacked sections formed a dorsal view of an isolated circle and an inner region (Fig.  5d ). The phenomena may be due to its overfitting onto expressions, which is caused by the contradiction between its hypothesis of consistent readout across sections, and the large readout variation across 16 sections in this application. Similar to PASTE, STalign also produced a wrong flipping, on the 13 th section along A-P axis (Fig.  5c ). Stacking the projections back to 3D, a mistaken regionalization of foregut, caused by the wrong flipping, was circled in orange (Fig.  5d ). In contrast, ST-GEARS avoided all of these mistakes in its results (Fig.  5c ). From the perspective of individual section profiles, noticeably in the 15 th section, we observed a significant reduction in the dissecting region between two parallel lines, indicating the successful fixation of flaws in the session. By comparing time usage across all methods, ST-GEARS achieved the 2nd lowest time consumption in registration (Supplementary Fig.  11 ). In terms of memory consumption, ST-GEARS, PASTE and STalign used much less memory than PASTE2 and GPSA. The three most memory efficient methods used almost identity peak memory, with the value fluctuation of <7%.

To comprehend the rationale behind our improvement, we analyzed the anchors generated by the three methods and the impact of our elastic registration. In the investigation of anchor accuracy, we discovered that ST-GEARS achieves the highest mapping accuracy among all section pairs (Fig.  5e ), suggesting its advanced ability to generate precise anchors, which forms the basis for precise spatial profile recovery. To understand this advancement, probabilistic constraints and its resulted anchors distributions (Supplementary Fig.  16 , Supplementary Fig.  17 ) were studied. With Distributive Constraints (Supplementary Fig.  16a ), ST-GEARS generated different maximum probabilities on different annotation types (Supplementary Fig.  16b ), which indicates that annotation types with higher cross-sectional consistency were prioritized in anchor generation. This selection led to reduced computational disturbances, and hence higher accuracy of anchors. We also compared anchor accuracy with and without Distributive Constraints adopted, and noticed an increase of mapping accuracy on each pair of sections (Supplementary Fig.  18 ). In final registration result, ST-GEARS without Distributive Constraints failed to fix the experimental flaw on the 15 th section (Supplementary Fig.  19 ), in contrast to effect upon the setting adopted (Fig.  5c ). Above findings validate the contributive effect of Distributive Constraints in our method. In study of elastic registration in shape smoothness, we witnessed an increased level of smoothness of tissue epidermis, foregut, and midgut, as well as the complete section, through area changes quantified by SI-STD-DI index (Supplementary Fig.  20 ). In internal structure aspect, an increased MSSIM of structural consistent pairs were noticed (Supplementary Fig.  21 ). An experimental flaw on the 15 th section was also fixed by elastic registration (Supplementary Fig.  22 ). Above findings point that the enhancement of registration accuracy on Drosophila embryo was induced by Distributive Constraints and elastic process.

By mapping spots back to 3D space, we further investigated the effect of different method on downstream analysis, in the perspective of genes expression (Fig.  5f ). Cpr56F and Osi7 were selected as marker genes, which were found to respectively highly express in foregut, and foregut plus epidermis region 22 . Investigating Cpr56F expression by ST-GEARS from dorsal view, we noticed three highly expressing regions, at anterior end, front region, and posterior end of the embryo. The finding matches the hybridization result of stage 13-16 Drosophila embryo extracted from Berkeley Drosophila Genome Project (BDGP) database. In contrast, none of PASTE, PASTE2, GPSA and STalign presented high expression at all three locations. When analyzing the distribution of Osi7 by PASTE, PASTE2 and STalign, we noticed a sharp decrease in expression from inner region to the outer layer marked by purple arrows, contradicting the prior knowledge of high expression in the epidermis. This is probably because PASTE and PASTE2 do not consider distortion correction as part of their methods, leaving section edges un-coincided and marker genes not obviously highly expressed on the outermost region. Though involving distortion correction, STalign lost certain amount of structural information by transforming ST data to image utilizing only information of regional gene expression abundance. The registration did not adequately correct distortion without support of enough structural messages. Similarly, PASTE2 failed to capture expression in outer layers and instead revealed a high expression in one inter-connected area, which did not correspond to the separate expression regions observed in hybridization result. No spatial pattern was witnessed when analyzing distribution of Osi7 by GPSA, which forms an obvious contrast to its hybridization evidence. Comparably, none of the violations was shown in the result of ST-GEARS. The comparison of spatial distribution indicated our potential capability to better enhance the process of downstream gene-related analysis.

Application to Mouse brain reconstruction

The design of 3D experiments involves various levels of sectioning distances 22 , 36 , 37 . To further investigate the applicability of ST-GEARS on ST data with larger slice intervals, we applied the method to a complete Mouse brain hemisphere dataset, which consists of 40 coronal sections (Supplementary Fig.  23a ), with a sectioning distance of 200 μm 37 . The transcriptomics data was measured by BARseq, which includes sequencing data and its cross-modal histology images. Each observation represents captured transcriptomics surrounded by the boundary of a cell.

Through respectively applying PASTE, PASTE2, GPSA, STalign and ST-GEARS onto the dataset, we observed multiple misaligned sections produced by approaches including PASTE, PASTE2, GPSA and STalign (Supplementary Fig.  23b , Supplementary Fig.  23c , Supplementary Fig.  23d , Fig.  6a ). In PASTE, these misalignments include 2 sections with ~ 180° angular misalignment (Supplementary Fig.  23b ). By PASTE2, 4 rotational misalignments and 8 positional misalignments were noticed (Supplementary Fig.  23d ). By GPSA, 12 sections were observed to be rotationally misaligned, and 3 sections were mistakenly distorted (Supplementary Fig.  23b ), probably due to its overfitting onto expressions discussed in analysis of Drosophila embryo. The scale on horizontal and vertical axis was distorted maybe due to the similar reason analyzed in Mouse hippocampus. And by STalign, 7 rotational misalignments were generated (Supplementary Fig.  23e ). As a clear contrast, our algorithm correctly aligned all 40 sections with 200 μm intervals (Supplementary Fig.  23f ). To more accurately assess the result of our registration, we employed the direction of the cutting lines induced during tissue processing 37 , and compared the consistency of tilt angles of these lines in the 20th, 25th, 26th, 27th, 33rd, 34th and 37th slices where these lines are visible. Notably, neither visual angle differences nor cutting line curving were observed, indicating that the sections were properly aligned by ST-GEARS (Fig.  6a , Supplementary Fig.  23f ). To quantify the registration accuracy in aspect of structural continuity, we calculated MSSIM scores of 11 section pairs that are structural consistent (Fig.  6b ). Consistent with the visual observations, PASTE2 presented a much larger score range than other methods, which reflects its instability across sections in this dataset, and GPSA exhibited the lowest median MSSIM score indicating its suboptimal average performance. By comparison, PASTE yielded a higher median score and a smaller variation, while ST-GEARS resulted in the highest median score and the smallest variation among all methods. In terms of computational efficiency, ST-GEARS achieved the 2nd lowest time consumption and lowest peak memory consumption across all methods (Supplementary Fig.  11 ).

a Reconstructed individual sections with recovered spatial location of each spot from the 25th to 36th section. Positional misalignments are marked by arrows of green, and angular misalignments are marked by arrows of orange. Visible cutting lines by ST-GEARS are marked by dotted lines. b A comparison of Mean Structural Similarity (MSSIM) score of 11 section pairs that are structurally consistent, between result of PASTE, PASTE2, GPSA, STalign and our method. The 11 biological replicates were studied, which were derived from different closest section pairs with each section pair representing smallest unit of study. Non control group was used as a MSSIM close to 1 is assumed to the idealized similarity value of the structurally similar pairs, hence a higher MSSIM value indicates higher reconstruction accuracy. The red lines positions show median score; the box extends from the first quartile (Q1) to the third quartile (Q3) of scores; the lower whisker is at the lowest datum above Q1 − 0.5 * (Q3-Q1), and the upper whisker is at the highest datum below Q3 + 0.5*(Q3-Q1); scores out of whiskers range are marked by circles. c Perspective, Lateral and Anterior view of reconstructed Mouse brain hemisphere. d Anterior view of layer annotation types distribution of reconstructed Mouse brain hemisphere. Source data are provided as a Source Data file.

To understand the reasons behind our progress, we examined anchor accuracy changes with regularization factors during ST-GEARS computation (Supplementary Fig.  24 ). Out of 39 section pairs, we observed a change in mapping accuracy >0.1 (out of 1) in 12 pairs. By Self-adaptive Regularization which was designed to face varying data characteristics which also includes varying section distances, regularization factor that leads to optimal mapping accuracy was selected, leading to an increased anchors accuracy in the 12 section pairs. Notably, among these 12 pairs, pairs 29th & 30th, 31st & 32nd and 32nd & 33rd were correctly aligned by ST-GEARS but misaligned by PASTE, which doesn’t adopt any self-adaptive regularization strategy.

After validating the registration result, we investigated the recovered cell-types’ distribution in the 3D space to assess the effectiveness of the reconstruction and its impact on further analysis. We observed that the complete morphology of hemisphere was recovered by ST-GEARS, with clear distinction of different tissues on perspective, lateral and anterior views (Fig.  6c ). We further studied the distribution of separate annotation types within cortex layers and found that 3D regionalization of each annotation type was recovered by ST-GEARS (Fig.  6d ). The reconstructed result indicated the adaptability of ST-GEARS across various scales of sectioning intervals, and its applicability on both bin-level, and cell-level datasets on which histology information is incorporated.

We introduce ST-GEARS, a 3D geospatial profile recovery approach for ST experiments. Leveraging the formulation of FGW OT, ST-GEARS utilizes both gene expression and structural similarities to retrieve cross-sectional mappings of spots with same in vivo planar coordinates, referred to as ‘anchors’. To further enhance accuracy, it uses our innovated Distributive Constraints to enhance the accuracy. Then it rigidly aligns sections utilizing the anchors, before finally eliminating section distortions using Gaussian-denoised Elastic Fields and its Bi-sectional Application.

We validate counterpart of ST-GEARS including anchors retrieval and elastic registration, respectively on DLPFC and Drosophila larva dataset. In the validation of anchors retrieval, through Mapping accuracy evaluation of retrieved anchors, ST-GEARS consistently outperformed PASTE and PASTE2 across all section pairs. We show Distributive Constraints as reasons behind its distinguished performance, which effectively suppressed the generation of anchors between spot groups with low cross-sectional similarity while enhances their generation among groups with higher similarity. To investigate the effectiveness of the elastic registration process, we evaluate the effects of tissue area changes and cross-sectional similarity using the Drosophila larvae dataset. Both smoother tissue area curves and higher similarity observed between structurally consistent sections confirm the efficacy of the elastic process of ST-GEARS.

We demonstrate ST-GEARS’s advanced accuracy of reconstruction compared to current approaches including PASTE, PASTE2 and GPSA, and its positive impact on downstream analysis compared to existing approaches. Our evaluation encompasses diverse application cases, including registration of two adjacent sections of Mouse hippocampus tissue measured by Slide-seq, reconstruction of 16 sections of Drosophila embryo individual measured by Stereo-seq, and reconstruction of a complete Mouse brain measured by BARseq, including 40 sections with sectioning interval as far as 200 μm. Among the methods, registered result by ST-GEARS exhibited the highest intra-structural consistency measured by MSSIM for two hippocampus sections separated by a single layer of neurons. On 16 sections of a Drosophila embryo individual, our method’s outstanding accuracy is indicated by both MSSIM and smoothness of tissue area changes. Importantly, ST-GEARS provides more reliable embryo morphology, precise tissue regionalization, and accurate marker gene distribution under hybridization evidence compared to existing approaches. This suggests that ST-GEARS provides higher quality tissues, cells, and genes information. On Mouse brain sections with large intervals of 200 μm, ST-GEARS avoided positional and angular misalignments that occur in result of PASTE and PASTE2. The improvement was quantified by a higher MSSIM. Both hemisphere morphology and cortex layer regionalization were reflected in the result of 3D reconstruction by ST-GEARS. The successful representation of important structural and functional features in the aforementioned studies collectively underscores ST-GEARS’ reliability and capability for advancing 3D downstream research, enabling more comprehensive and insightful analysis of complex biological systems.

To further enhance and extend our method, opportunities in various aspects are anticipated to be explored. Firstly, algorithm aspects including hyperparameter sensitivity and scalability can be further explored for a more enhanced method performance. Though recommended values are provided for two of its hyperparameters, method performance is still affected by parameter values, raising the potential issue of overfitting and sensitivity which can be further studied. In scalability aspect, ST-GEARS introduces obvious computational cost increasement when dealing with large-scale datasets. Though strategy of Granularity adjusting is innovated to down-grade complexity, opportunity of improving robustness on increasing scale of data is expected to be further explored. Secondly, tasks aimed at improving data preprocessing, including but not limited to batch effect removal and diffusion correction, are expected to be integrated into our method, considering their coupling property with registration task itself: inaccuracies in input data introduce perturbations to anchors optimization, while recovered spatial information of our method may assist data quality enhancement by providing registered sections. Thirdly, the ST-GEARS’ Distributive Constraint takes rough grouping information as its input, which may potentially introduce computational burden during the reconstruction process. To address this, an automatic step is expected to be developed to reliably cluster spots while maintaining computational efficiency of the overall process. This step can be integrated into our method either as preprocessing, or as a coupling task, similarly to our expectation of data quality enhancement. Finally, we envision incorporating a wider scope of anchors applications into our existing framework. such as information integration of sections across time, across modalities and even across species. With interpretability, robustness and accuracy provided by ST-GEARS, we anticipate its applications and extension in various areas of biological and medical research. We believe that our method can help address a multitude of questions regarding growth and development, disease mechanisms, and evolutionary processes.

FGW OT description

Fused Gromov Wasserstein (FGW) Optimal Transport (OT) is the modeling of spot-wise or cell-wise similarity between two sections, with the purpose of solving optimal mappings between the spots or cells, with mappings also called ‘anchors’. By FGW OT, the optimal group of mappings enables highest gene expression similarity between mapped spots, at the same time keeping similar positions relative to their located sections.

The required input of FGW OT includes genes expression, spot or cell locations before registration, and constraint values which assigns different weight to the optimization on different spots or cells. For gene expression, we introduce \({{\bf{A}}}\in {R}^{{n}_{A},m}\) for section A, to describe normalized count of unique molecular identifiers (UMIs) of different genes of each cell or spot, thereinto n A denotes number of spots in slice A, and m denotes number of genes that are captured in both sections. Similarly, we describe gene expression on section B as \({{\bf{B}}}\in {R}^{{n}_{B},m}\) , with genes arranged in the same order as in A . For spot or cell locations, we introduce \({{{\bf{X}}}}_{{{\bf{A}}}}\in {R}^{{n}_{A},2}\) to describe spots locations of section A, with the 1st column storing horizontal coordinates and the 2nd storing vertical coordinates. Similarly, we have \({{{\bf{X}}}}_{{{\bf{B}}}}\in {R}^{{n}_{B},2}\) to describe spots locations in section B. Spots are arranged in the same order in gene expression and location matrices. Constraint values are discussed in section of Distributive Constraints.

FGW OT solves:

Thereinto, \({{{\bf{M}}}}_{{{\bf{AB}}}}\in {R}^{{n}_{A},{n}_{B}}\) describes the similarity of each pair of spots respectively on section A and B, formulated as \({{{\bf{M}}}}_{{{\bf{i}}},{{\bf{j}}}}^{({{\bf{AB}}})}={KL}({A}_{i,:},{B}_{j,:})\) . Be noted that \({{{\rm{M}}}}_{{{\rm{i}}},{{\rm{j}}}}^{({{\rm{AB}}})}\) still indicates spot-wise similarity M AB , with section code AB being moved to superscript and added parenthesis for clarity, since subscript location are taken by spot index i, j . KL denotes Kullback-Leibler (KL) divergence 43 . \({{{\bf{C}}}}_{{{\bf{A}}}}\in {R}^{{n}_{A},{n}_{A}}\) describes spot-wise distance within section A, with \({{{\bf{C}}}}_{{{\bf{i}}}{{,}}{{\bf{j}}}}^{({{\bf{A}}})}={dis}({{{\bf{X}}}}_{{{\bf{i}}}{{,}}{{:}}}^{({{\bf{A}}})}{{,}}{{{\bf{X}}}}_{{{\bf{j}}},{{:}}}^{({{\bf{A}}})})\) , and dis denoting Euclidean distance measure. Be noted that \({{{\rm{X}}}}_{{{\rm{i}}},:}^{({{\rm{A}}})}\) and \({{{\rm{X}}}}_{{{\rm{j}}},:}^{({{\rm{A}}})}\) still indicate spot locations X A , with section code A being moved to superscript and added parenthesis for clarity, since subscript location are taken by spot index i and j . \({{{\rm{C}}}}_{{{\rm{i}}},{{\rm{j}}}}^{({{\rm{A}}})}\) refers to spot-wise distance C A for the same reason. Similarly, \({{{\bf{C}}}}_{{{\bf{B}}}}\in {R}^{{n}_{B},{n}_{B}}\) describes spot-wise distance of section B. \({{\bf{L}}}\in {R}^{{n}_{A},{n}_{B},{n}_{A},{n}_{B}}\) defines the difference between all spot pair distance respectively on section A and B, with \({{{\rm{L}}}}_{{{\rm{i}}},{{\rm{j}}},{{\rm{k}}},{{\rm{l}}}}=|{{{\rm{C}}}}_{{{\rm{i}}},{{\rm{k}}}}^{({{\rm{A}}})}-{{{\rm{C}}}}_{{{\rm{j}}},{{\rm{l}}}}^{({{\rm{B}}})}|\) . ⊗ denotes Kronecker product of two matrices; 〈,〉 denotes matrix multiplication.

Adjacency matrix \({{\mathbf{\pi }}}\in {R}^{({n}_{A},{n}_{B})}\) to be optimized stores strength of anchors between spots from the two sections, with row index representing spots on section A, and column index representing spots on section B. Sum of elements of π is 1. With \({{\langle }}{{{\bf{M}}}}_{{{\bf{AB}}}}^{{{\bf{2}}}}{{,}}{{\mathbf{\pi }}}{{\rangle }}\) , the similarity of mapped spots are measured. With \({{\langle }}{{{\bf{L}}}}^{{{\bf{2}}}}({{{\bf{C}}}}_{{{\bf{A}}}}{{,}}{{{\bf{C}}}}_{{{\bf{B}}}}){{\times}}{{\mathbf{\pi }}}{{,}}{{\mathbf{\pi }}}{{\rangle }}\) , similarity between distance of spot pairs on section A, with its anchored spot pairs on section B, is measured. \(\langle {{{\bf{L}}}}^{{{\bf{2}}}}({{{\bf{C}}}}_{{{\bf{A}}}}{l{{,}}}{{{\bf{C}}}}_{{{\bf{B}}}})\otimes {{\mathbf{\pi }}}{{,}}{{\mathbf{\pi }}}\rangle\) describes similarity between spatial structures under the anchors’ connection. α ∈ [0,1] denotes regularization factor, which specifies the relative importance of structure similarity compared to expression similarity. W A and W B are constraint values that are introduced in section of Distributive Constraints.

With the formulation above, FGW OT solves optimal anchors between the spots, or cells, which enables maximum weighted combination of gene expression similarity and position similarity of mapped spots or cells.

Distributive constraints

As adopted by constraint values in FGW OT, we introduce Distributive Constraints, to assign different emphasis to spots or cells in the optimization. Distributive Constraints utilizes cell type component information to differentiate the emphasis: if an annotation or cluster express high similarity across sections, its corresponding spots or cells will be placed relatively high sum of probability, and vice versa. With higher sum of probability, more anchors and anchors with higher strength are generated, while less anchors are produced on spots with lower sum of probability. This operation leads registration to rely more on expression-consistent regions of sections, hence largely enhancing both accuracy of anchors and precision of following rigid and elastic registration.

The required inputs of Distributive Constraints include \({{{\bf{G}}}}_{{{\bf{A}}}}\in {R}^{{n}_{A}}\) and \({{{\bf{G}}}}_{{{\bf{B}}}}\in {R}^{{n}_{B}}\) , which store the grouping information such as annotation type or cluster of each spot in section A and B. We then summarize the repeated annotations or clusters from G A and G B , and put the unique values in \({{\bf{g}}}\in {R}^{{n}_{{group}}}\) . n group is the number of unique annotation type or clusters. Then implemented in ST-GEARS, for each annotation type or cluster g i , we calculate the average gene expression across spots:

Be noted that \({{{\bf{G}}}}_{{{{\bf{i}}}}^{{\prime} }}^{({{\bf{A}}})}\) and \({{{\bf{G}}}}_{{{{\bf{i}}}}^{{\prime} }}^{({{\bf{B}}})}\) still indicate grouping information G A and G B , with section code A and B being moved to superscript and added parenthesis for clarity, since subscript location are taken by spot index i ′ and j ′. And \({{\bf{1}}}_{{{{\bf{n}}}}_{{{\bf{A}}}}}\) and \({{\bf{1}}}_{{{{\bf{n}}}}_{{{\bf{B}}}}}\) are both row vectors of ones.

With average gene expression of each annotation type or cluster, with the form of distribution, we measure its difference across sections by KL divergence. Then the calculated distance is mapped by logistic kernel, to further emphasize differences between relatively consistent annotations or clusters.

\({di}{s}_{{map}}={f}_{{logistic}}({dis})\) , where \({f}_{{logistic}}\left(x\right)=\frac{1}{1+{e}^{-x}}-0.5\) . Putting scaler value dis of each annotation or cluster together, we have a vector \({{\bf{DI}}}{{{\bf{S}}}}_{{{\bf{map}}}}\in {R}^{{n}_{{celltype}}}\) . Finally, we transform the distance to similarity, map the similarity result back to each spot:

We further apply normalization on the result:

W A and W B are constraints values applied in (1). Since the values are computed based on similarity measure using cell composition information, weight of FGW OT is automatically redistributed, with higher emphasis on more consistent regions across sections, and less emphasis on less consistent area. Enhanced anchor accuracy hence registration accuracy is then achieved.

Self-adaptive regularization

In FGW OT formulation, a regularization factor is included to specify the relative importance of structural similarity compared to expression similarity during optimization. ST-GEARS includes a self-adaptive regularization method that determines the factor value, that induces highest overall accuracy of anchors despite of varying situations. Situations include but are not limited to section distances, spot sizes, extent of distortions, and data quality such as level of diffusion.

By practice, our method respectively adopts factors on multiple scales including 0.8, 0.4, 0.2, 0.1, 0.05, 0.025, 0.013, and 0.006. The candidate values vary exponentially, for ST-GEARS to find the optimal term regardless of scale differences between expression and structural term in (1). The accuracy of each set of optimized anchors by every regularization factor was evaluated, by measuring weighted percentage \({\sum}_{{{{\bf{G}}}}_{{{\bf{i}}}}^{{{(}}{{\bf{A}}}{{)}}}{{=}}{{{\bf{G}}}}_{{{\bf{j}}}}^{{{(}}{{\bf{B}}}{{)}}}}{{{\boldsymbol{\pi }}}}_{{{\bf{i}}}{{,}}{{\bf{j}}}}\) of anchors that join spots with same annotation types or clusters. Be noted that \({{{\rm{G}}}}_{{{\rm{i}}}}^{({{\rm{A}}})}\) and \({{{\rm{G}}}}_{{{\rm{j}}}}^{({{\rm{B}}})}\) still indicate grouping information G A and G B , respectively, with section code A and B being moved to superscript and added parenthesis for clarity, since subscript location are taken by spot index i and j . The regularization factor value that achieves highest accuracy is then adopted by our method.

Elastic field inference

Finding spots with highest probability.

After rigid registration, elastic fields are inferred based on the anchors with the highest probability for each spot or cell. For elastic field to be applied on each section, it is calculated using its anchors with closest sections, as well as spatial coordinates of sections after rigid registration. Along cross-sectioning order, each section in the middle has two closest sections, respectively on its anterior and posterior sides. Exceptionally, if a section is on anterior or posterior end, it has only one closest section.

Specifically for a section in the middle with N spots, we calculate \({{{\bf{I}}}}_{{{\bf{pre}}}}\epsilon {Z}^{N}\) and \({{{\bf{I}}}}_{{{\bf{next}}}}\epsilon {Z}^{N}\) which stores the mapped spots on anterior and posterior neighbor section for each of its spots. The calculation takes as input adjacency matrix π pre , which stores anchors with the anterior neighbor section output by FGW OT, and π next storing anchors with posterior section.

Be noted that \({{{\rm{\pi }}}}_{:,{{\rm{n}}}}^{\left({{\rm{pre}}}\right)}\) and \(\,{{{\rm{\pi }}}}_{{{\rm{n}}},:}^{({{\rm{next}}})}\) still indicate adjacency matrix π pre and π next , with direction code pre and next being moved to superscript and added parenthesis for clarity, since subscript location are taken by spot index n .

Notably, not every spot in a selected section has its own anchored spot, due to multiple strategies including distributive constraint and anchors filtration, hence their corresponding element in I pre and I next are null. For section located on posterior end, only I next is applicable; and for section located on anterior end, only \({{{\bf{I}}}}_{{{\rm{pre}}}}^{{{\rm{n}}}}\) is applicable.

Elastic field establishment

After specifying spots with highest probability, ST-GEARS calculates location displacements between the spots, then establishes elastic fields for each section. An elastic field is a 2D displacement distribution, describing how displacement values are distributed across different locations. And it is established to enable ST-GEARS to benefit from further denoising functions to reduce elastic operation outliers and improve elastic effect consistency across regions.

For each section located in the middle, 4 elastic fields are generated. Two of those represent the section’s horizontal and vertical displacement distribution compared to anterior neighbor section, denoted as 2D matrix F (x_pre) and F (y_pre) , while the other two represent its horizontal and vertical displacement distribution compared to posterior neighbor, denoted as F (x_next) and F (y_next) . To initialize F (x_pre) , F (y_pre) , F (x_next) and F (y_next) for the section, the shape of the matrix is first decided. Its height denoted by Height and width denoted by Width are calculated by gridding the spot locations using a fixed step. Height and Width are shared across the 4 matrices:

For its input, \({{\bf{X}}}\in {R}^{N,2}\) denotes spots location of current section after rigid registration. For a single section, we prepare \({{{\bf{X}}}}^{{{(}}{{\bf{pre}}}{{)}}}\epsilon {R}^{{N\_pre},2}\) and \({{{\bf{X}}}}^{{{(}}{{\bf{next}}}{{)}}}\epsilon {R}^{{N\_next},2}\) as spots location of its anterior and posterior section after rigid alignment, respectively. psize represents average distance between closest spot or cell centers, and it is to be input by users. The matrix has no filled values to this step.

To fill in the fields, we first transform spot locations into the coordinate system of field. With \({{\bf{X}}}\_{{\bf{shifted}}}\,\epsilon {R}^{N,2}\) and \({{\bf{X}}}\_{{\bf{pixel}}}\,\epsilon {R}^{N,2}\) :

We then calculate location displacements between each of its spots and their anchored spots with highest probability, on both anterior and posterior neighbors. With \({{\bf{X}}}\_{{\bf{corres}}}\,\epsilon {R}^{N,2}\) and \({{\bf{X}}}\_{{\bf{delta}}}\epsilon {R}^{N,2}\) :

With the spot locations in field coordinates and the displacement values above, we fill in corresponding elements of the elastic field:

By the end of Eqs. ( 2 ), 4 elastic fields for each section in the middle is established. However, some elements in the matrix are still empty, because of absence of spots or cells located in the grid of location. To address this problem, 2d nearest interpolation method 44 was adopted, which fills in every empty element, with the displacement value of its neighboring elements:

thereinto \({{\bf{mes}}}{{{\bf{h}}}}_{{{\bf{trans}}}}\epsilon {N}^{{n}_{{grids}}\times 2}\) denotes grid coordinates of the designed field, with \({n}_{{grids}}={Height}\times {Width}\) . And f interp_grid denotes the nearest interpolation method.

For section located on posterior end, only F (x_next) and F (y_next) are applicable; and for section located on anterior end, only F (x_pre) and F (y_pre) are applicable.

2D Gaussian denoising

As caused by exerted force, the displacement or elastic field is expected to have static or smoothly changing values across different locations 45 , 46 , 47 . ST-GEARS makes use of this property, to smoothen the field and to reduce errors in the field caused by any upper stream process, such as raw data noises and inaccuracy in anchor computation. Gaussian filtering 48 , 49 is adopted to implement the denoising, similarly to image denoising processes 50 , 51 . Denoised elastic fields are then generated.

It calculates weighted average across the neighboring region of each element to replace its value:

where f gaussian_filter denotes the method of Gaussian filtering.

Bi-sectional fields application

Bi-sectional fields application plan.

With elastic fields generated and denoised, ST-GEARS uses the fields as a guidance to correct distortion for each section. Through querying the elastic fields with spatial location of each spot, the displacement to be implemented is returned. For a section in the middle, its elastic fields calculated with both anterior and posterior neighbor sections are queried, and guidance provided by both anterior and posterior sections are applied on the rigid aligned result, called ‘Bi-sectional Fields Application’. After the application, the distortion of the section is corrected, and the elastic registration result is generated.

Specifically, the denoised elastic fields are first queried, returning the displacement to be implemented:

Next, average displacement returned by both anterior and posterior sections are applied on the rigid registration result, leading to final elastic registration result \({{\bf{X}}}\_{{\bf{final}}}\in {R}^{N,2}\!\!:\)

For section located on posterior end,

For section located on anterior end,

The validity of this plan is proved in the section: Proof of validity of Bi-sectional Fields Application.

Proof of validity of Bi-sectional fields application

Bi-sectional Fields Application accurately recovers the spatial profile before distortion, by averaging and applying displacement value guided by both anterior and posterior neighbor section. The effect is approved mathematically as following:

Take section A, B, and C as an example of a sequence of sections, with X A , X B and X C denoting their spots’ spatial information after rigid alignment, and X A_insitu , X B_insitu and X C_insitu denoting their in vivo spatial information. The distortion occurred to the slices during experiments are denoted as X A_dis , X B_dis and X C_dis .

According to Bi-sectional Fields Application, the corrected spatial information is:

Based on the in vivo morphological consistency across sections, spatial information of section B can be approximated by an average of information of A and C, written as

Given that X A_dis and X C_dis can be seen as independent and identically distributed sets of variables,

where μ ABC is the universal mean, and Σ ABC is the variance of the 2d displacement information.

Inserting the terms (4) and (5) back to Eq. ( 3 ) gives

indicating the proximity of corrected spatial information to in vivo spatial information.

Evaluation metrix

We evaluated the accuracy of anchors by index of Mapping Accuracy, and measured the reconstruction effect by MSSIM and SI-STD-DI, in both elastic effect study and overall methodology comparison.

Mapping accuracy

Designed and adopted by PASTE 27 , Mapping Accuracy calculates the weighted percentage of anchors joining spots with same annotation.

MSSIM index

MSSIM measures the accuracy of registration, based on the assumption that in some sectioning positions, tissue morphology remains almost consistent across slices. The method quantifies the accuracy, by measuring the similarity of annotation type distribution of such section pairs.

To implement the quantification, first, structurally consistent section pairs are selected among all sections arranged in sequence.

Next, on each section from the pair, transformation from individual spots to a complete image is implemented, by gridding the rectangular area that surrounds the tissue, and assigning each grid of a value that represents the annotation type which occurs most frequently in the grid. The resulted image describes the annotation type distribution of the section.

Finally, similarity between each pair of images is measured, by index of MSSIM 52 . The method generates a window with fixed size, slides the window simultaneously on both images, and compares the two framed parts by windows on their intensity, contrast, and structures. Among those, the intensity difference is measured by difference of average pixel values, the contrast difference is measured by comparing variance of the two sets of framed pixel values, and the structure difference is measured by comparing their covariances. A Structural Similarity of Images (SSIM) index is calculated for each position of the window using \({SSIM}(X,Y)=\frac{(2{\mu }_{x}{\mu }_{y})(2{\sigma }_{{xy}}+{c}_{2})}{({\mu }_{x}^{2}+{\mu }_{y}^{2}+{c}_{1})({\sigma }_{x}^{2}+{\sigma }_{y}^{2}+{c}_{2})}\) , where μ x and μ y denote average pixel values of the frames, σ x and σ y denote variances of the frames, and σ xy denotes covariances of the two frames. c 1 and c 2 are constants to avoid 0 value of the divisor. Averaging the SSIM value across all windows gives the final MSSIM result of the two sections.

SI-STD-DI measures smoothness of area changing across sections along a fixed axis, by calculating the standard deviation of area changes on each pair of adjacent sections and scale the result by dividing it by average area.

Software and code

Data analysis.

All software used to analyze data in this study are open-sourced Python packages, including anndata = 0.9.2, numpy = 1.22.4, pandas = 1.4.3, scipy = 1.10.1, matplotlib = 3.5.2, k3d = 2.15.3.

Statistics and reproducibility

No statistical method was used to predetermine sample size. No data were excluded from the analyses. The experiments were not randomized. The Investigators were not blinded to allocation during experiments and outcome assessment.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

All data used in this research were collected from published sources. DLPFC data was obtained from the research: Transcriptome-scale Spatial Gene Expression in the Human Dorsolateral Prefrontal Cortex, with data downloading link of http://research.libd.org/spatialLIBD/index.html ; Drosophila embryo and Drosophila larva data were collected from High-resolution 3d Spatiotemporal Transcriptomic Maps of Developing Drosophila Embryos and Larvae, with the dataset link of https://db.cngb.org/stomics/datasets/STDS0000060 . Mouse brain data was collected from research: Modular cell type organization of cortical areas revealed by in vivo sequencing. The download link is: https://data.mendeley.com/datasets/8bhhk7c5n9/1 . All datasets were generated on Spatial Transcriptomics platform, with DLPFC data generated by Visium technology of 10x Genomics, Mouse brain data generated by BARseq of Cold Spring Harbor Laboratory, while Drosophila embryo and larva generated by Stereo-seq technology of BGI.  Source data are provided with this paper.

Code availability

The methods of ST-GEARS is packaged, and distributed as an open-source, publicly available repository at https://github.com/STOmics/ST-GEARS 53 .

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Acknowledgements

This work is part of the “SpatioTemporal Omics Consortium” (STOC) paper package. A list of STOC members is available at: http://sto-consortium.org . We acknowledge the Stomics Cloud platform ( https://cloud.stomics.tech/ ) for providing convenient ways for analyzing spatial omics datasets. We acknowledge the CNGB Nucleotide Sequence Archive (CNSA) of China National GeneBank DataBase (CNGBdb) for maintaining the Drosophila database. This work is supported by National Natural Science Foundation of China (32300526 to S. F., 32100514 to M. X.). We thank Weizhen Xue for the inspirational discussion towards design of Distributive Constraints. We thank Yating Ren for her advice towards a more efficient code implementation. We thank Dr. Xiaojie Qiu and Dr. Yinqi Bai for the discussion on the registration topic and their advice on our work.

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Tianyi Xia was responsible of method design, analysis design and implementation, as well as drafting of this manuscript. Dr. Luni Hu participated in structure design of the applications. Lulu Zuo was in part of 3D visualizations design, and she helps maintain our online repository. Tianyi Xia, Lei Cao, Lulu Zuo and Dr. Luni Hu conducted experiments and analysis for reply to peer review. Dr. Yunjia Zhang provided insights in anchors results interpretation of DLPFC dataset, and in accuracy analysis of mouse brain dataset. Dr. Mengyang Xu revised this article. Lei Zhang and Bowen Ma offered numerous suggestions to enhance computational efficiency, in both memory and time. Taotao Pan and Chuan Chen provided suggestions in data preprocessing. Qin Lu, Bohan Zhang, Junfu Guo, Chang Shi and Mei Li provided suggestions for this study. Dr. Shuangsang Fang supervised this study in structure and analysis design, and she revised this article. Chao Liu, Yuxiang Li and Yong Zhang supervised this study.

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Xia, T., Hu, L., Zuo, L. et al. ST-GEARS: Advancing 3D downstream research through accurate spatial information recovery. Nat Commun 15 , 7806 (2024). https://doi.org/10.1038/s41467-024-51935-0

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Drug–Drug Interactions Involving High-Alert Medications that Lead to Interaction-Associated Symptoms in Pediatric Intensive Care Patients: A Retrospective Study

Lisa marie kiesel.

1 Clinical Pharmacy, Institute of Pharmacy, Medical Faculty, Leipzig University, and Drug Safety Center, Leipzig University and Leipzig University Hospital, Leipzig, Germany

Astrid Bertsche

2 Division of Neuropediatrics, University Hospital for Children and Adolescents, Greifswald, Germany

3 Center for Pediatric Research, University Hospital for Children and Adolescents, Leipzig, Germany

Wieland Kiess

Manuela siekmeyer, thilo bertsche, martina patrizia neininger, associated data.

Children treated in a pediatric intensive care unit (PICU) often receive several drugs together, among them drugs defined as high-alert medications (HAMs). Those drugs carry a high risk of causing patient harm, for example, due to a higher potential for interactions. HAMs should therefore be administered with caution, especially in a PICU.

The objective of the current study was to identify drug–drug interactions involving HAMs that increase the risk of interaction-associated symptoms in pediatric intensive care.

In a retrospective study, we analyzed the electronic documentation of patients hospitalized for at least 48 h in a general PICU who received at least two different drugs within a 24-h interval. We assessed potential drug–drug interactions involving HAM on the basis of the two drug information databases UpToDate and drugs.com. Furthermore, we analyzed whether symptoms were observed after the administration of drug pairs that could lead to interaction-associated symptoms. For drug pairs involving HAM administered on at least 2% of patient days, and symptoms observed at least ten times after a respective drug pair, we calculated odds ratios, 95% confidence intervals, and p -values by using a univariate binary logistic regression.

Among 315 analyzed patients, 81.3% (256/315) received drugs defined as high-alert medication for pediatric patients. Those high-alert medications were involved in 20,150 potential drug–drug interactions. In 14.0% (2830/20,150) of these, one or more symptoms were observed that could be a possible consequence of the interaction, resulting in 3203 observed symptoms affecting 56.3% (144/256) of patients receiving high-alert medication. The odds ratios for symptoms observed after a drug–drug interaction were increased for eight specific symptoms (each p ≤ 0.05), especially hemodynamic alterations and disturbances of electrolyte and fluid balance. The odds ratio was highest for decreased blood pressure observed after the administration of the drug pair fentanyl and furosemide (OR 5.06; 95% confidence interval 3.5–7.4; p < 0.001). Increased odds ratios for specific symptoms observed after drug–drug interactions resulted from eight combinations composed of eight different drugs: digoxin, fentanyl, midazolam, phenobarbital, potassium salts and vancomycin (high-alert medications), and the diuretics furosemide and hydrochlorothiazide (non-high-alert medications). The resulting drug pairs were: potassium salts–furosemide, fentanyl–furosemide, vancomycin–furosemide, digoxin–furosemide, digoxin–hydrochlorothiazide, fentanyl–phenobarbital, potassium salts–hydrochlorothiazide, and midazolam–hydrochlorothiazide.

Conclusions

In a cohort of PICU patients, this study identified eight specific drug pairs involving high-alert medications that may increase the risk of interaction-associated symptoms, mainly hemodynamic alterations and electrolyte/fluid balance disturbances. If the administration of those drug pairs is unavoidable, patients should be closely monitored.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40272-024-00641-x.

Introduction

Children admitted to a pediatric intensive care unit (PICU) are often in a critical state of health and require complex drug treatment. Although administration of multiple drugs together leads to an increased risk of drug-related problems [ 1 , 2 ], previous studies reported most patients in PICUs received a median number of ten different drugs per patient day [ 3 , 4 ]. Especially in the PICU, so-called high-alert medications (HAMs) must be administered frequently. Due to various factors, such as a narrow therapeutic range or a high potential for drug–drug interactions (DDIs) [ 5 , 6 ], these drugs bear a higher risk of causing patient harm compared with other drugs, according to the Institute for Safe Medication Practices (ISMP) [ 7 ]. Therefore, the administration of HAM should be given careful consideration. The ISMP developed its first list of HAMs for the acute care setting in 1995 [ 7 ]. Until now, few studies have identified specialized lists of HAM for children [ 6 , 8 – 10 ]. Schilling et al. combined results from three previous studies to develop a list of 20 HAMs for pediatric patients in the German setting. They described DDI as a drug-related problem for half of those 20 [ 6 ].

There is scant literature about DDIs involving HAMs for pediatric patients or their implications for children admitted to a PICU. Therefore, we aimed to identify DDIs involving HAMs that may increase the risk of interaction-associated symptoms. We specifically targeted drug pairs that should be avoided in daily clinical practice or closely monitored if their administration is unavoidable. We did not distinguish between different severity grades for DDI and symptoms, as we aimed to assess the most common DDIs regardless of their classification according to the databases, and we endeavored not to overlook any relevant symptoms. Therefore, we also included drug–drug interactions with a low classification according to the databases, as these can also severely affect patients in a critical health state.

Material and Methods

Study design.

This retrospective study analyzed data from April 2018 to March 2019 obtained in a general PICU of a university hospital in Germany. Patients of all pediatric age groups were treated in the study unit, except neonates, who were treated in a separate neonatal intensive care unit. We assessed the electronic documentation for each patient in the hospital’s patient data management system to identify potential DDIs (pDDIs) involving at least one drug defined as a HAM. Furthermore, we analyzed symptoms observed after these pDDIs to detect interaction-associated symptoms.

We included patients hospitalized for at least 48 h in the study unit who received at least two different drugs within a 24-h interval during their stay. Patients on chemotherapy were excluded because they were mainly treated at the pediatric oncology unit of the university hospital and only transferred to the PICU for a short time if their health condition deteriorated severely.

The study titled “Adverse drug reactions in an interdisciplinary PICU” was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee at the Medical Faculty, Leipzig University, Germany (study number: 127/19-ek) on 2 May 2019. The ethics committee waived informed consent because this was a retrospective study, and patients’ treatment was not influenced.

Identification of HAM in the PICU and pDDIs Involving at Least One HAM in Clinical Care

On the basis of the electronic patient documentation, we first examined the administration of the 20 drugs that Schilling et al. [ 6 ] defined as HAM for hospitalized pediatric patients. We included 15 of those HAMs in our analysis because 5 of the 20 defined HAMs were not administered in our PICU during the study period: cyclosporine, phenytoin, amiodarone, vecuronium, and rocuronium. Second, we evaluated pDDIs that involved at least one HAM for each patient day. For this purpose, potentially interacting drug pairs were identified on the basis of two drug information databases: UpToDate (provided by Wolters Kluwer, Riverwoods, Illinois, USA) and drugs.com (provided by Drugsite Trust, Auckland, New Zealand). Each drug pair for which an interaction alert was reported in at least one of the databases was defined as a pDDI. To identify potentially interacting drug pairs, we considered a maximum time interval of 24 h between administering a HAM and another potentially interacting drug, regardless of whether the second drug was defined as a HAM. We considered a 24-h interval to be appropriate because estimating the correct half-lives of interacting drugs in individual patients in our cohort was hardly feasible due to the general developmental variability of pharmacokinetics and pharmacodynamics in children and the possible influence of the individual patient’s condition. Hence, if a potentially interacting drug was administered 24 h before or after a HAM, the event was categorized as a pDDI. If a pDDI occurred more than once within the defined time interval, it was counted only once. For continuous infusions, it was assessed for each drug administered concurrently whether a pDDI occurred due to the additional drug.

Identification of Interaction-Associated Symptoms

For each pDDI, we investigated whether symptoms that could be associated with it were observed after the administration. For this investigation, we examined the nurses’ and physicians’ daily documentation of the patient’s condition for symptoms that occurred within a 24-hour interval after administration of the second drug of the relevant drug pair. The documentation included automatically recorded vital parameters, laboratory parameters, and additional documentation, such as non-measurable symptoms as nausea or vomiting (Online Resource 1). For vital and laboratory parameters, age-dependent standard ranges for infants, children, and adolescents were determined by the treating physicians. For some patients, the attending physician adjusted the standard ranges to the patient’s health condition. In our analyses, we considered deviations from the determined patient–individual ranges. We focused on symptoms that were identified as possible consequences of a pDDI according to our database search in UpToDate and drugs.com. If at least one of these symptoms was associated with the relevant drug pair at a statistically significant odds ratio (OR), this was defined as a DDI. Since we took the underlying data on the symptoms from the documentation of physicians and nursing staff, it can be assumed that those symptoms were clinically relevant, as they would otherwise not have been documented.

To estimate the risk associated with the interaction of a particular drug pair for an observed symptom, we calculated the OR and 95% confidence interval. To ensure that the calculation was based on a sufficient occurrence of a particular drug pair and corresponding symptom, we set two criteria. First, we only considered potentially interacting drug pairs administered on at least 2% of patient days. Second, we focused only on corresponding symptoms observed at least ten times after a given drug pair. Combining potentially interacting drug pairs and symptoms that met these criteria, we created a contingency table that presents the frequency of the following combinations on each patient day: both the potentially interacting drug pair and corresponding symptom were observed; only the potentially interacting drug pair was observed; only the symptom was observed; and neither the potentially interacting drug pair nor the symptom was observed. On the basis of the contingency table, a univariate logistic regression was performed to obtain OR, 95% confidence interval, and p -value. The calculation was conducted using IBM SPSS Statistics Version 29 (IBM Corporation, Armonk, New York, USA). A p value ≤ 0.05 was considered to indicate significance.

Characteristics of Patients and Administered Drugs

We examined 1263 patients admitted to the PICU during the study period for the inclusion criteria (Fig. ​ (Fig.1). 1 ). Of those, 315 (24.9%) patients fulfilled the inclusion criteria. Baseline patient characteristics are presented in Table ​ Table1. 1 . In total, 255 different drugs were administered to the patients. Of these drugs, 5.9% (15/255) were identified as HAM for hospitalized pediatric patients, according to the study by Schilling et al. [ 6 ] (Table ​ (Table2). 2 ). The most commonly administered sedative during the study period was midazolam [affected 173/315 (54.0%) patients on 1011/3788 (26.7%) patient days; Online Resource 2]. Potassium salts were the most frequently administered HAM, used on 39.0% of patient days (1477/3788), in 47.3% (149/315) of patients (Table ​ (Table3 3 ).

Flow chart of patient inclusion

Baseline patient characteristics

PICU pediatric intensive care unit

Characteristics of drug therapy

HAM high-alert medication

Frequency of high-alert medications administered in the pediatric intensive care unit during the study period. In our analysis, we included 15 of 20 drugs defined as high-alert medications for hospitalized pediatric patients according to Schilling et al. [ 6 ]

The remaining five high-alert medications were not administered during the study period: cyclosporine, phenytoin, amiodarone, vecuronium, and rocuronium

pDDIs Involving at Least One HAM

Analyzing each patient’s electronic documentation, we identified 20,150 pDDIs involving at least one HAM on the basis of our database search in UpToDate and drugs.com. We calculated a rate of 78.7 pDDIs per patient that involved at least one HAM (20,150 pDDI involving at least one HAM/256 patients receiving HAM). The 20,150 pDDIs resulted from 469 different drug pairs. Of these potentially interacting drug pairs, 14.3% (67/469) were administered on at least 2% of patient days. The frequency of the potentially interacting drug pairs and their classifications according to the databases is presented in Online Resource 3.

Interaction-Associated Symptoms Identified in the PICU

We observed at least one symptom after 14.0% (2830/20,150) of pDDIs, resulting in a total of 3203 observed symptoms affecting 56.3% (144/256) of patients receiving HAM (Table  4 ). While we observed one symptom after the administration of 87.7% (2482/2830) of those pDDIs, more than one symptom was observed after 12.3% (348/2830) of pDDIs.

Frequency of symptoms observed after potential drug–drug interactions involving high-alert medications

HAM high-alert medication, pDDI potential drug–drug interaction, PTH parathyroid hormone

The most pDDIs after which we observed at least one symptom involved potassium salts (2.4%; 493/20,150), followed closely by digoxin (2.4%; 480/20,150) and fentanyl (2.4%; 476/20,150; Fig. ​ Fig.2 2 ).

For each high-alert medication, the number of potential drug–drug interactions (total interactions: N = 20,150) is plotted against how often at least one symptom was observed after a potential drug–drug interaction involving the respective high-alert medication (total interactions followed by symptoms: N = 2830)

For 33.1% (1061/3203) of observed symptoms, the preconditions for the calculation of the OR were fulfilled (Table ​ (Table5). 5 ). We found an increased OR for hyponatremia, hypokalemia, decreased blood pressure, increased heart rate, urinary retention, edema, sweating, and restlessness (each p ≤ 0.05; Table ​ Table5). 5 ). Those eight specific symptoms accounted for 28.0% (897/3203) of all observed symptoms potentially related to DDI. These DDIs involved eight different drugs in eight different combinations. Of the eight drugs, 75% (6/8) were defined as HAM for pediatric patients: digoxin, fentanyl, midazolam, phenobarbital, potassium salts, and vancomycin. The remaining 25% (2/8) were diuretics not defined as HAM: furosemide and hydrochlorothiazide. The highest OR was found for decreased blood pressure observed after administration of the drug pair fentanyl and furosemide (OR 5.06; 95% CI 3.5–7.4; p < 0.001), followed by hypokalemia observed after administration of the drug pairs digoxin and furosemide (OR 4.16; 95% CI 3.1–5.6; p < 0.001) and digoxin and hydrochlorothiazide (OR 3.86; 95% CI 2.9–5.1; p < 0.001).

Drug–drug interactions involving high-alert medications and subsequent symptoms observed within 24 h after the administration of the respective drug–drug interaction

For each drug combination and observed symptom, the frequencies of patient days on which the respective potential drug–drug interaction was or was not administered and whether the symptom was observed is shown. From those numbers, the odds ratios, 95% confidence intervals, and p -values were calculated using a univariate logistic regression

HCT hydrochlorothiazide, n/a not applicable (not listed in the respective database), pDDI potential drug–drug interaction

*Significant

a Categorized as high-alert medication for hospitalized pediatric patients according to Schilling et al. [ 6 ]

b Classification used in UpToDate: “D—Consider therapy modification; C—Monitor therapy; B—No action needed. Agents may interact with each other”

c Classification used in Drugs.com: “Major—Avoid combinations; Moderate—Usually avoid combination. Use it only under special circumstances; Minor—Take steps to circumvent the interaction risk and/or establish a monitoring plan”

HAMs are Common Drugs Administered in the PICU

According to the ISMP, HAMs carry a higher risk of patient harm compared with ordinary drugs [ 7 ]. Even when used as prescribed, they significantly increase the risk of drug-related problems [ 11 ]. In our study, 81% of critically ill children received at least one drug defined as HAM for pediatric patients by Schilling et al. [ 6 ]. Potassium salts, midazolam, and vancomycin were the HAMs most frequently administered. This is in line with a previous study in a pediatric emergency setting reporting that 91% of patients were prescribed at least one HAM, with potassium salts being the most frequently administered [ 12 ].

More than 20,000 pDDIs with HAM During a 1-Year Study

It is widely known that pDDIs are highly prevalent in PICUs. They are associated with various factors, such as a high number of administered drugs, a complex chronic condition, or an increased length of hospitalization [ 4 , 13 , 14 ]. Although previous studies determined pDDI as a cause of drug-related problems with HAM for pediatric patients, there is only limited knowledge about the frequency of pDDIs in pediatric intensive care [ 6 , 8 , 10 ]. In our study, we found more than 20,000 pDDIs involving HAM in 256 pediatric patients over the 1-year study period. A previous Brazilian study of adult intensive care patients reported 846 HAM-related pDDIs in 60 patients [ 15 ]. Compared with our research, the Brazilian study reported a considerably lower rate of HAM-related pDDIs per patient (79 versus 14). Part of this difference may be explained by the fact that pediatric patients requiring intensive care are more susceptible to drug–drug interactions [ 16 ]. However, it may also be related to the fact that the Brazilian study was performed on the basis of the database Micromedex 2.0 only [ 15 ]. Several studies recommended using at least two databases to determine pDDIs in daily routine [ 17 – 19 ] . Thus, we used the two databases, UpToDate and drugs.com, to avoid underestimating any potential risks. However, since the concordance between different databases is limited, comparing various studies can be challenging [ 20 , 21 ].

Physicians Should be Aware of Interaction-Associated Symptoms

For 2830 pDDIs, we observed 3203 symptoms occurring after the administration of the potentially interacting drug pairs. More than one in four detected symptoms were eventually associated with a DDI. Those interaction-associated symptoms comprised eight specific symptoms, mainly hemodynamic alterations or electrolyte and fluid balance disturbances. These symptoms were frequently reported in previous pediatric intensive care studies [ 3 , 22 – 24 ]. The study presented here shows that DDI involving HAM should be considered a likely trigger for symptoms in addition to other factors, such as the underlying disease or non-drug treatments, such as surgeries. It can also be assumed that various factors contribute to the occurrence of a symptom. When identifying DDIs and following interaction-associated symptoms, we did not distinguish between different severity grades of DDI or symptoms, as the main aim of our study was to identify drug pairs that are frequently associated with symptoms that are considered clinically relevant by the responsible physicians and nurses. Physicians usually receive a considerable number of alerts when using a database-related interaction checker. This may quickly lead to over-alerting. Therefore, we aimed to provide physicians with a concise overview of clinically relevant DDIs that occur frequently in a PICU. Our findings could be implemented in commonly used database-related interaction checkers to draw physicians’ attention to drug pairs involving HAM that are potentially associated with an increased risk of adverse events.

We identified eight specific drug pairs composed of eight different drugs that may lead to an increased risk of interaction-associated symptoms. By calculating the OR for a DDI and a respective symptom, we took into account how often a symptom was observed on patient days when the interacting drug pair was administered compared with days when the respective drug pair was not administered. In particular, this should minimize the risk that certain combinations of DDI and symptoms are over- or underestimated. For the interaction of fentanyl and furosemide, we found the highest OR for the symptom of decreased blood pressure. Both drugs have been shown to belong to the top ten of the most frequently administered drugs and to be among the drugs most commonly involved in pDDIs in the pediatric intensive care setting [ 4 ]. In our study, DDI was associated with a potential fivefold increased risk of decreased blood pressure. The second highest OR, indicating a potential fourfold increased risk, was found for the interaction of digoxin with hydrochlorothiazide and the observed symptom of hypokalemia. Consequently, when the administration of drug pairs associated with a potentially increased risk of interaction-associated symptoms is unavoidable, patients should be closely monitored for potential symptoms.

Until now, few studies have dealt with interaction-associated symptoms in the pediatric intensive care setting [ 14 , 25 , 26 ]. One of those studies only focused on cytochrome P450-mediated drug–drug interactions [ 25 ]. Two other studies concentrated on symptoms on the basis of clinical monitoring and laboratory results, as we did in our research. Both studies also identified hemodynamic alterations and electrolyte and fluid balance disturbances as symptoms following DDIs. However, neither of those studies noted specific interactions that increased the risk of the detected symptoms [ 14 , 26 ]. Our study went one step further by revealing eight interacting drug pairs that may increase the risk of the identified interaction-associated symptoms in clinical practice. We found symptoms that are widely known to follow the respective DDI, such as the association of hyponatremia with the DDI of potassium salts and furosemide, or the increased risk for hypokalemia associated with the DDI of digoxin and furosemide. However, we also observed symptoms after a DDI that we did not expect. For example, we unexpectedly found that the DDI of fentanyl and furosemide was associated with a potential risk increase for urinary retention, or that the DDI of vancomycin and furosemide was associated with edema. Especially for symptoms that unexpectedly are observed after a specific DDI, other factors, such as the state of illness or a surgery that could also lead to the symptom, should be critically evaluated.

Limitations

Some limitations have to be considered when interpreting our study results. First of all, the relevance of some drugs administered in our study can vary in different PICUs around the world. However, the 15 drugs defined as HAM that were in the focus of our study are used in many PICUs worldwide [ 4 , 27 – 31 ].

As recommended by previous studies [ 17 – 19 ], we used two databases to prevent failure to detect interactions that could lead to interaction-associated symptoms. However, we could not identify a database specializing in DDI for pediatrics. Previous studies did not find an age-related trend in the magnitude of DDIs, although it should be noted that there are insufficient data for children under 2 years of age [ 32 , 33 ]. In addition, extrapolating data from adults to children may over- or underestimate the severity of DDIs [ 34 ]. Additionally, as most databases are limited to the information on the interactions of two drugs, potential synergistic or antagonistic effects of combinations consisting of three or more drugs might be overlooked.

Furthermore, the allowed maximum time interval of 24 h between the administration of two drugs may be too long for an interaction for some drug pairs. According to a previous review by Bakker et al., the optimal time interval would consider the half-lives of interacting drugs [ 21 ]. However, due to the developmental variability of pharmacokinetics and pharmacodynamics in children, it is very challenging to determine standardized drug half-lives in the pediatric population [ 35 ]. In addition, the individual patients’ conditions, such as renal function, can also have significant influence on drugs’ half-lives [ 36 ]. In addition, a constant plasma concentration is aimed for with many drugs, which is why a longer-lasting interaction potential can be assumed, although the half-lives of the individual drugs are varying. To ensure a standardized approach for evaluating DDI, we established a 24-h time interval as described in the review by Bakker et al. if consideration of drug half-lives is not feasible [ 21 ]. This methodological approach might potentially increase the risk of overestimation.

The retrospective design is another limitation of this study, as using nurses’ and physicians’ daily documentation entails the risk of missing data. That could lead to information bias, as the documentation was not primarily compiled to answer research questions. Consequently, using the patient documentation as data basis may have an impact on the identification of symptoms themselves, and on the observed associations between interacting drugs pairs and subsequent symptoms. Furthermore, due to the retrospective design, we could not assess whether the physicians accepted certain expectable symptoms as an inevitable consequence of the chosen drug therapy because the patient’s state of health required the administration.

In addition, it should be kept in mind that the administration of a HAM alone and the underlying disease may also increase the risk of adverse events. However, we focused on acknowledged DDIs and interaction-associated symptoms reported in established databases. We endeavored to identify symptoms prone to being associated with a DDI by calculating ORs, as those interactions potentially contribute to evoking symptoms, or to prolonging or exacerbating existing symptoms. These drug combinations should therefore be given special consideration in the routine care of critically ill pediatric patients who are already at risk.

Our study sheds light on a topic about which knowledge is limited: symptoms associated with DDIs involving HAM. We showed that pDDIs involving HAM are very common in pediatric intensive care. More than one in four observed symptoms were associated with a DDI. These symptoms were mainly disturbances of electrolyte and fluid balance and hemodynamic alterations. Focusing on drug pairs with a potentially increased risk of triggering these symptoms, we identified eight specific drug pairs composed of eight different drugs. However, administration of these drug pairs may be unavoidable. In that case, patients should be carefully monitored for electrolyte and fluid balance disturbances and hemodynamic alterations, which were observed as the most frequent interaction-associated symptoms.

Below is the link to the electronic supplementary material.

Acknowledgements

We thank all the physicians and nurses in the participating PICU for their helpful collaboration.

Declarations

Open Access funding enabled and organized by Projekt DEAL.

A. Bertsche reports grants from UCB Pharma GmbH and honoraria for speaking engagements from Biogen GmbH, Desitin Arzneimittel GmbH, Eisai GmbH, GW Pharma GmbH, Neuraxpharm GmbH, Shire/Takeda GmbH, UCB Pharma GmbH, and ViroPharma GmbH. The other authors declare they have no conflicts of interests.

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to ethical and privacy considerations to protect the confidentiality of patients.

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of the Medical Faculty, Leipzig University, Germany (127/19-ek). The study was performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments.

As this was a retrospective study and data were collected from patient records without any influence on patients’ treatment, the ethics committee waived informed consent.

Not applicable.

Conceptualization: Lisa Marie Kiesel and Martina Patrizia Neininger; methodology: Lisa Marie Kiesel, Martina Patrizia Neininger, Astrid Bertsche, Thilo Bertsche, Manuela Siekmeyer, and Wieland Kiess; formal analysis: Lisa Marie Kiesel; investigation: Lisa Marie Kiesel and Martina Patrizia Neininger; writing—original draft preparation: Lisa Marie Kiesel and Martina Patrizia Neininger; writing—review and editing: Astrid Bertsche, Thilo Bertsche, Manuela Siekmeyer, and Wieland Kiess; supervision: Martina Patrizia Neininger; project administration: Lisa Marie Kiesel and Martina Patrizia Neininger. All authors read and approved the final version.

• Open access
• Published: 05 September 2024

A protocol for stakeholder engagement in head and neck cancer pragmatic trials

• Cameron Macdonald 1 ,
• Margaret Fitch 2 ,
• Katherine A. Hutcheson 3 ,
• Timothy M. McCulloch 4 &
• Rosemary Martino 5 , 6 , 7 , 8  

BMC Cancer volume  24 , Article number:  1109 ( 2024 ) Cite this article

Metrics details

Meaningful engagement with stakeholders in research demands intentional approaches. This paper describes the development of a framework to guide stakeholder engagement as research partners in a pragmatic trial proposed to evaluate behavioral interventions for dysphagia in head and neck cancer patients. We highlight the core principles of stakeholder engagement including representation of all perspectives, meaningful participation, respectful partnership with stakeholders, and accountability to stakeholders; and describe how these principles were operationalized to engage relevant stakeholders throughout the course of a large clinical trial.

Peer Review reports

Approaches to patient involvement and stakeholder engagement have blossomed in the past decade as important ways to improve quality of health care and research [ 1 , 2 , 3 , 4 , 5 , 6 ]. While patient refers to individuals living with an impaired health condition, stakeholder includes any individual who could be affected by or have a connection with a topic area as a clinician, knowledge user, caregiver, or decision-maker [ 7 ]. Stakeholder engagement has roots in the Community Based Participatory Research (CBPR) movement of the 1980s and 1990s, in which all individuals involved in the outcome of a community-based intervention have a say [ 8 , 9 , 10 ]. Early efforts towards expanding stakeholder engagement into clinical care focused primarily on quality improvement efforts where clinicians involved patients as partners in their own plan of care and engaged them as self-care or self-management agents [ 11 , 12 ]. These efforts expanded to including patients as partners in co-designing new programs and clinical care models aimed at improving patient experience and the quality of care [ 13 ]. Stakeholder engagement is now a key strategy in designing, investigating, and implementing person-centered philosophies or approaches in health care.

More recently, researchers have incorporated a range of stakeholders, including patients, in their initiatives [ 14 , 15 , 16 ]. For research endeavors, stakeholder engagement aims to increase the likelihood of investigating research questions important to patients and utilizing patient-centered research methods. Researchers have also included stakeholders to accomplish several objectives during the research project implementation: (1) effective participant recruitment, (2) collection of data relevant to stakeholders, and (3) and interpretation of results from multiple stakeholder perspectives [ 14 ]. Additionally, stakeholder engagement has also been used to ensure successful dissemination, uptake, and implementation of research findings. Ultimately, the impact of research could be enhanced by involving relevant stakeholders throughout the life cycles of the research effort (e.g., planning, study conduct, dissemination/implementation) rather than participating in only a limited part of the process (e.g., endorsement of a trial concept).

Literature describing how researchers engage stakeholders has grown steadily [ 17 , 18 ]. Specifically, levels of engagement range from consulting on priorities for investigations before research begins, to serving on steering or advisory committees, to full membership on a research team [ 19 , 20 ]. Likewise, the timing of engagement can vary. It may occur early, prior to proposal design with a workshop to identify research questions or, during the proposal development and writing. It may also occur only once funding has been awarded and implementation commences or after study completion to discuss findings and plans for action. Various challenges have been identified as researchers seek to engage stakeholders resulting in recommendations to avoid tokenism, to budget sufficient time and energy for each step in the engagement process, and to provide specific training and resources to stakeholders to support engagement [ 3 , 4 ]. One significant observation is the challenge for all involved to hold non-judgmental, open attitudes about engagement and find ways to interact respectfully and collaboratively. Another is the challenge of finding strategies to overcome the inherent power imbalance that can exist in groups where patients and healthcare clinicians are together [ 21 ].

For engagement work with clinical populations with distinct needs, based on our assessment of the existing literature, it is unclear whether general principles of stakeholder engagement are useful across all research initiatives or if special approaches are required for particular disease groups, such as certain types of cancer. Protocols demonstrating how to engage stakeholders successfully, what constitutes success for various levels of engagement, and systematic evaluation of engagement models remains sparsely reported in cancer trials overall [ 4 , 22 ]. Within cancer research, head and neck cancers (HNC) represent a unique population who experience functional changes that distinguish their experience from other survivors (e.g., changes in fundamental activities like eating, talking, and their appearance) – this distinction is empirically relevant to the composition and operationalization of stakeholder engagement for this cancer survivor population. Yet, there is only one published experience to guide investigators interested in conducting stakeholder engaged research in the HNC populations [ 23 ].

No uniform model exists for stakeholder engagement overall or in specific clinical populations, and few presented in the literature have evolved from empirical work [ 6 ]. There is growing agreement that it is important to involve stakeholders as early as possible in the research process and engage them in on-going evaluation and feedback about the process of engagement. Therefore, an urgent need exists for protocols that detail population specific models for stakeholder engaged clinical research.

The aim of this work is to describe the protocol for engaging stakeholders in the implementation of a pragmatic trial in a specific HNC clinical cancer population. [ 24 ] We will operationalize the model structure, the principles that will guide the model and the overall engagement plan across the various stages of the trial.

As the Engagement Team for an international, multi-site pragmatic trial aimed at comparing the effectiveness of swallowing interventions during radiotherapy (RT) for HNC (PRO-ACTIVE), we adopted four core principles of stakeholder engagement based on our interpretation of national guidelines for stakeholder engagement, like those from the Canadian Institutes of Health Research (CIHR) [ 7 ], National Institute for Health and Care Research (NIHR), [ 25 ] and in the existing literature [ 1 , 4 , 5 , 6 , 7 , 8 , 26 , 27 , 28 , 29 ]. We paid special attention to the stakeholder engagement guidelines provided by our funder, the Patient-Centered Outcomes Research Institute (PCORI) [ 26 , 27 ]. In particular, we focused on principles emphasizing that all involved in the outcomes should be represented, that mutuality in stakeholder relationships be established, that ongoing relations between the trial team and various stakeholders be reciprocal, and that our plan be guided by opportunities for mutual learning between stakeholders and trial leadership. We also needed to meet the complex and varied needs of a pragmatic, multi-site, randomized clinical trial for a behavioral intervention to be conducted in both the USA and Canada [ 24 ]. We adapted these principles for our purposes and intended outcomes in our plan for engaging with stakeholders throughout the duration of the trial in a HNC population.

The PRO-ACTIVE trial

Dysphagia is a major problem facing individuals undergoing RT for HNC. Questions remain about the most effective timing and intensity of swallowing interventions during RT. PRO-ACTIVE is an international multi-center 3-arm pragmatic randomized controlled trial (RCT) which aims to compare the effectiveness of PRO-ACTIVE versus RE-ACTIVE behavioral swallowing interventions among 952 patients with HNC scheduled to undergo radiotherapy [ 24 ]. Before commencing RT, eligible patients will be randomized to receive one of three therapy models delivered by oncology-specialized speech language pathologists (SLPs) during the 6–7 weeks of radiotherapy. Regardless of the Arm, all patients will receive a swallow assessment 3 and 12 months after RT completion. The 3 intervention arms are:

Arm 1 (RE-ACTIVE): Reactive intervention started promptly if/when dysphagia is identified;

Arm 2 (PRO-ACTIVE EAT): Early low intensity proactive intervention started before RT commences;

Arm 3 (PRO-ACTIVE EAT + EXERCISE): Early high intensity proactive intervention started before RT commences.

Dysphagia is common following RT, and swallowing function is of high priority for patients, caregivers, and therapy providers [ 30 , 31 , 32 ]. There are, however, differences in perspective on the manner in which dysphagia impacts health. Patients prioritize issues of depression/anxiety whereas caregivers and clinicians prioritize pneumonia/malnutrition [ 30 , 33 ]. The perspective of the payer and policy maker has not before been explored, but given that tube dependency affects costs, we expect payers and policy makers to endorse reduction in tube feeding utilization as a priority issue for quality improvement in the area of dysphagia [ 34 ]. Clearly, patients, caregivers, providers and (we suspect) policy makers have varying perspectives on the most relevant issues for HNC survivors with dysphagia.

This trial is focused on a behavioral intervention which is applied during a notoriously arduous course of treatment and a time when many patients are known to experience a number of toxicities including increasing difficulty swallowing and maintaining their nutritional status [ 35 ]. Numerous physicians and allied healthcare providers work closely with patients and their caregivers during this treatment period. Our approach to stakeholder engagement needed to ensure that these diverse perspectives would be optimally included, and that the complex issues relevant to supportive care in HNC in two distinct healthcare policy environments (Canada and the US) be considered. We also want to ensure policy/payer perspectives are included as future implementation of a successful intervention may include cost implications and require policy change.

The PRO-ACTIVE engagement protocol

Engagement principles.

We designed the PRO-ACTIVE stakeholder engagement model structure and plan for engagement around four key principles derived from the literature and from our collective understanding of the needs of the HNC patient population (see Table  1 ).

The engagement model

Our engagement protocol was designed to ensure that the key principles of representation, meaningful participation, respectful partnership, and accountability are integrated into all our exchanges with stakeholders. How we operationalized these principles or organized the structure and environments to integrate them in our approach is described below.

Representation

US and Canadian engagement sites : Because of the important differences in healthcare delivery, reimbursement approaches, and patient experiences between the US and Canada, we designed a comprehensive stakeholder engagement processes to occur at one site in each country. The two sites will work in tandem, with complementary stakeholder panels, under the leadership of the Engagement Team (CM, MF, KH, RM, TM).

Core and Periphery sites : In the US, where a significant proportion of cancer treatment occurs at non-academic settings, stakeholders representing stakeholder and satellite care facilities will be invited. The proximity to a major medical center is also a crucial factor in the feasibility and possible future uptake of therapies. Therefore, representatives from remote, rural areas will be included in each stakeholder panel.

Stakeholder selection

Identifying relevant stakeholders for this investigation requires thinking about both the conduct of the trial itself and the future uptake and utilization of the trial results in an interdisciplinary complex clinical environment. In our situation, relevant stakeholders will include patients/caregivers, physician providers, allied health providers, and policymaker/payers. (See Fig.  1 for target distributions for stakeholder samples.)

Target distributions of characteristics for each USA and Canadian stakeholder research partner panel

Meaningful participation

In order to ensure meaningful participation among all stakeholders, we will structure the engagement process to equalize differences in understanding of research processes, and to ensure that all stakeholders have a voice in each phase of engagement.

Training : In recognition that stakeholders have different backgrounds related to research, the Engagement Team will provide training regarding research methods as part of the early engagement process. Facilitators will have extensive experience in working with low-literacy stakeholders as well as with professional stakeholders and will be able to provide this training as needed at the initial focus group sessions.

A modular structure based on closed-loop communication : our engagement protocol is based on a series of five task-based modules, with all stakeholders brainstorming solutions to research-related problems, the SAB prioritizing those suggestions and recommending an approach to the research problem, and finally the Trial Executive Committee deciding on stakeholder recommendations and reporting back to stakeholders detailing the research decisions before the start of the next module. (See Fig.  2 )

Proposed Workflow for each six-month long stakeholder engagement module

Homogeneous Brainstorming Panels : Forming homogeneous stakeholder focus groups will empower the various participants to voice their unique perspectives in a safe and comfortable environment by ensuring that participants meet in sessions comprised of those with similar experiences of HNC. We will utilize the homogeneous groups for brainstorming sessions around research-related questions, allowing participants to contribute more freely and to build on common experiences more easily than is possible in mixed groups. Each site will convene four separate stakeholder panels (i.e., patients and family members, MDs, allied clinicians, and policymaker/payer groups). Each patient/caregiver, MD and AH group will meet once in each module to generate ideas around a common topic.

Heterogeneous Stakeholder Advisory Board : Following each brainstorming session, we will convene a stakeholder advisory board composed of 2–3 individuals from each of the homogeneous groups to review the recommendations from each of the brainstorming sessions and discuss their merits. They will develop consensus recommendations based on the discussions of the homogeneous panels which will ultimately be presented to the Trial Executive Committee.

Respectful partnership

Stakeholder Engagement throughout all phases of research process : Stakeholder consultation and collaboration are intentionally built into all phases of this study, from conception through dissemination. The last module will also include planning the future implementation of the successful dysphagia intervention, including stakeholder involvement. Stakeholder panels in both the USA and Canada will work in parallel with close collaboration by the facilitators who will share agendas, inquiry approaches (questions for the stakeholders to discuss), and experiences with their groups throughout the work. Five modules (See Table  2 ) will guide the dialogue with stakeholders and mirror the phases of the research study (see Fig.  3 for Workflow for Stakeholder Engagement Modules).

Fair compensation : Based on the guidelines outlined by PCORI, stakeholders will be offered stipends commensurate with their usual rates of pay in addition to compensation for travel and accommodations, and meals at engagement sites [ 27 ].

Meetings at neutral sites : To ensure a free exchange of ideas, stakeholder meetings will be held at non-medical, neutral sites, and scheduled to maximize participation. Members from more distant, rural areas will be given the option of participating via remote video conference if they are unable to join in person.

Independent , Professional facilitation : All stakeholder meetings will be facilitated by professional research methodologists, who specialize in focus group and stakeholder engagement facilitation. They will be skilled in ensuring that all voices are heard and accounted for. To ensure free and frank discussion, facilitators should be independent of the trial leadership. All meetings will be audiotaped, de-identified, transcribed and summarized by the facilitators following each session [ 36 ].

Accountability

Member validation : De-identified notes from each stakeholder panel will be shared among all stakeholders for their validation and further input. Feedback will also be shared from the SAB and the Trial Executive decisions back to the stakeholder panels to ensure closed-loop communication [ 37 ].

Investigator feedback : Once the SABs reach consensus, the Engagement Team will collate the recommendations, prepare a report on that process and recommendations for presentation to the Trial Executive Committee. The Executive Committee members will utilize the report to determine how to incorporate stakeholder recommendations into the trial. Once the final decision has been made, the facilitators will disseminate an accountability report to all stakeholders.

Model of information flow between stakeholders, facilitators, and trial leadership

This paper details a protocol for stakeholder engagement developed specifically for a pragmatic trial of a supportive care intervention during RT for HNC. A model structure and engagement plan were developed for the investigators’ trial design and an anticipated clinical environment for future implementation. Involving stakeholders in clinical trials and clarity about how stakeholder engagement principles will be operationalized in trials is increasingly necessary as funding agencies and investigators recognize the merit of this process by requiring stakeholder engagement in proposal submissions. This tailored protocol offers the first available template for other researchers designing clinical trials with an oncological patient population.

Stakeholder engagement in research is still an emerging phenomenon, especially in terms of how to operationalize engagement principles [ 3 , 38 ]. Questions remain regarding how best to organize and incorporate principles of fair and fruitful engagement of stakeholders in cases of specific trial designs in the context of specific clinical populations [ 11 ]. In planning for a pragmatic trial of a behavioral intervention, we posited that engagement of stakeholders would benefit the conduct of the trial as well as future dissemination and uptake. We anticipated the future uptake will require not only the evidence from the trial regarding the effectiveness of the intervention but also consideration of what is important to patients, how patients and caregivers are able to manage the intervention, and who would support utilization of the trial findings as clinicians or as payers. Therefore, it was important that we include clinicians in all potential sites of care across U.S. and Canada, as well as representatives of payer organizations in both national policy domains.

Meaningful stakeholder input needs to be defined in relation to the type of trial, the practice environment, and both the clinician and patient characteristics. Protocols are needed to guide the uptake and utilization of research results that outline the range of considerations for successful implementation [ 6 , 11 ]. A primary consideration for stakeholder engagement is the careful identification of who would be considered relevant as a stakeholder given the context of the work. Criticism has been made about using generic panels of individuals who are not experienced in the selected clinical setting or care intervention. Engaging individuals who have experience undergoing the treatment processes under consideration can offer unique insights regarding how interventions are most apt to be successful [ 39 , 40 , 41 ]. Behavioral interventions will require understanding both patient and clinician behaviors and motivations as well as the environment where the intervention will be taught and performed.

The clinical care environment for this HNC patient group is complex, with a range of clinicians who hold diverse, and often divergent, perspectives about supportive care and treatment priorities. Also, the behavioral intervention would need to be enacted by patients who are already burdened with increasing pain and difficulty swallowing. They may also be limited by socio-economic burdens and uncertainty. Their capacity to engage with the successful behavioral intervention itself is of paramount importance in the future. It is therefore essential that patients who have undergone RT for HNC and their family members advise on all aspects of the trial. Payers also must be consulted because supportive care interventions may not be seen as central to curative care and they may require additional costs and policy changes to implement the successful intervention in current health services models. This stakeholder engagement protocol, therefore, represents a very specific context of supportive care in cancer but may have relevance to clinical populations with similar interdisciplinary considerations and competing priorities at play.

Central to the our protocol was the composition and manner of engaging stakeholders to fit the context of the trial and follow best practices for engagement. By engaging the relevant stakeholders in the research itself and providing an environment where opinions can be shared respectfully without feelings of intimidation, and meaningful relevant input can be obtained to set the stage for success in the future implementation of study findings. Creating such environments requires intentional organization. Drawing from research in other arenas (e.g., psychology, social work, nursing) offers ideas about working with stakeholders effectively [ 42 , 43 , 44 ]. For example, grouping of individuals with similar experiences (i.e., homogeneous groups) and paying close attention to power dynamics (e.g., who is perceived to be influencers) are helpful. Especially in the world of health care, power dynamics can influence feelings of safety and open dialogue when patients, physicians and/or other health care professionals are grouped together. Separating the stakeholders into homogeneous groups for open brainstorming of ideas, but then organizing a way to build consensus through skilled facilitation, is one approach to overcome this challenge. However, we have made a commitment to report divergent perspectives if consensus cannot be reached.

It is also important to have an independent, trained facilitator to manage the group sessions, who understands the trial environment but does not express a preference for a particular trial outcome [ 45 ]. An added concern for the HNC patient population is the reality that some will have challenges speaking (e.g., because of tongue injury or dry mouth) while others may find it difficult to describe their experiences easily, or in a public situation [ 36 ]. Some may require added time to express their views clearly which could contribute to frustration within a group situation.

Finally, accountability to the stakeholders is not only respectful but also promotes added engagement. That accountability needs to be about how their input was utilized as well as asking them routinely about how the processes for engagement are working. Designing expectations for stakeholder roles and rules of engagement at the onset is an important step to establish the approaches for accountability. Taking the time for all to understand how the engagement principles are to be handled can truly enrich the research process, its outcomes, and the impact of the work.

Conclusions

Stakeholder engagement requires specific strategies to ensure relevant and meaningful engagement for all concerned. We designed a protocol of stakeholder engagement operationalizing basic principles and incorporating it as an integral component within the design of an ongoing clinical trial. Given that understanding how to operationalize these principles within specific contexts is still emerging, our protocol could be helpful to other research teams who wish to incorporate stakeholder engagement in meaningful, relevant ways in a pragmatic clinical trial particularly in the context of interdisciplinary care of head and neck cancers.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

Community Based Participatory Research

Head and Neck Cancer

Radiotherapy

Canadian Institutes of Health Research

National Institute for Health and Care Research

Patient-Centered Outcomes Research Institute

Randomized Controlled Trial

Speech Language Pathologist

Stakeholder Advisory Board

Doctor of Medicine

Allied Health

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Acknowledgements

The authors acknowledge Maisha Khan, Melissa Giamou, Veronica Rodriguez, Kieshan Amarakaran and Courtney Field for invaluable feedback to planning final protocol and/or preparing the manuscript.

Patient-Centered Outcomes Research Institute (PCORI), a United States-based non-governmental institute, has funded this project (ID: PCS-1609-36195). PCORI has no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

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Bloomberg Faculty of Nursing, University of Toronto, Toronto, ON, Canada

Margaret Fitch

Department of Head and Neck Surgery, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States of America

Katherine A. Hutcheson

Otolaryngology Head and Neck Surgery, Department of Surgery, University of Wisconsin – Madison School of Medicine and Public Health, Madison, WI, United States of America

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Department of Speech-Language Pathology, University of Toronto, Toronto, ON, Canada

Rosemary Martino

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Home » Research Methods – Types, Examples and Guide

Research Methods – Types, Examples and Guide

Table of Contents

Research Methods

Definition:

Research Methods refer to the techniques, procedures, and processes used by researchers to collect , analyze, and interpret data in order to answer research questions or test hypotheses. The methods used in research can vary depending on the research questions, the type of data that is being collected, and the research design.

Types of Research Methods

Types of Research Methods are as follows:

Qualitative research Method

Qualitative research methods are used to collect and analyze non-numerical data. This type of research is useful when the objective is to explore the meaning of phenomena, understand the experiences of individuals, or gain insights into complex social processes. Qualitative research methods include interviews, focus groups, ethnography, and content analysis.

Quantitative Research Method

Quantitative research methods are used to collect and analyze numerical data. This type of research is useful when the objective is to test a hypothesis, determine cause-and-effect relationships, and measure the prevalence of certain phenomena. Quantitative research methods include surveys, experiments, and secondary data analysis.

Mixed Method Research

Mixed Method Research refers to the combination of both qualitative and quantitative research methods in a single study. This approach aims to overcome the limitations of each individual method and to provide a more comprehensive understanding of the research topic. This approach allows researchers to gather both quantitative data, which is often used to test hypotheses and make generalizations about a population, and qualitative data, which provides a more in-depth understanding of the experiences and perspectives of individuals.

Key Differences Between Research Methods

The following Table shows the key differences between Quantitative, Qualitative and Mixed Research Methods

Examples of Research Methods

Examples of Research Methods are as follows:

Qualitative Research Example:

A researcher wants to study the experience of cancer patients during their treatment. They conduct in-depth interviews with patients to gather data on their emotional state, coping mechanisms, and support systems.

Quantitative Research Example:

A company wants to determine the effectiveness of a new advertisement campaign. They survey a large group of people, asking them to rate their awareness of the product and their likelihood of purchasing it.

Mixed Research Example:

A university wants to evaluate the effectiveness of a new teaching method in improving student performance. They collect both quantitative data (such as test scores) and qualitative data (such as feedback from students and teachers) to get a complete picture of the impact of the new method.

Applications of Research Methods

Research methods are used in various fields to investigate, analyze, and answer research questions. Here are some examples of how research methods are applied in different fields:

• Psychology : Research methods are widely used in psychology to study human behavior, emotions, and mental processes. For example, researchers may use experiments, surveys, and observational studies to understand how people behave in different situations, how they respond to different stimuli, and how their brains process information.
• Sociology : Sociologists use research methods to study social phenomena, such as social inequality, social change, and social relationships. Researchers may use surveys, interviews, and observational studies to collect data on social attitudes, beliefs, and behaviors.
• Medicine : Research methods are essential in medical research to study diseases, test new treatments, and evaluate their effectiveness. Researchers may use clinical trials, case studies, and laboratory experiments to collect data on the efficacy and safety of different medical treatments.
• Education : Research methods are used in education to understand how students learn, how teachers teach, and how educational policies affect student outcomes. Researchers may use surveys, experiments, and observational studies to collect data on student performance, teacher effectiveness, and educational programs.
• Business : Research methods are used in business to understand consumer behavior, market trends, and business strategies. Researchers may use surveys, focus groups, and observational studies to collect data on consumer preferences, market trends, and industry competition.
• Environmental science : Research methods are used in environmental science to study the natural world and its ecosystems. Researchers may use field studies, laboratory experiments, and observational studies to collect data on environmental factors, such as air and water quality, and the impact of human activities on the environment.
• Political science : Research methods are used in political science to study political systems, institutions, and behavior. Researchers may use surveys, experiments, and observational studies to collect data on political attitudes, voting behavior, and the impact of policies on society.

Purpose of Research Methods

Research methods serve several purposes, including:

• Identify research problems: Research methods are used to identify research problems or questions that need to be addressed through empirical investigation.
• Develop hypotheses: Research methods help researchers develop hypotheses, which are tentative explanations for the observed phenomenon or relationship.
• Collect data: Research methods enable researchers to collect data in a systematic and objective way, which is necessary to test hypotheses and draw meaningful conclusions.
• Analyze data: Research methods provide tools and techniques for analyzing data, such as statistical analysis, content analysis, and discourse analysis.
• Test hypotheses: Research methods allow researchers to test hypotheses by examining the relationships between variables in a systematic and controlled manner.
• Draw conclusions : Research methods facilitate the drawing of conclusions based on empirical evidence and help researchers make generalizations about a population based on their sample data.
• Enhance understanding: Research methods contribute to the development of knowledge and enhance our understanding of various phenomena and relationships, which can inform policy, practice, and theory.

When to Use Research Methods

Research methods are used when you need to gather information or data to answer a question or to gain insights into a particular phenomenon.

Here are some situations when research methods may be appropriate:

• To investigate a problem : Research methods can be used to investigate a problem or a research question in a particular field. This can help in identifying the root cause of the problem and developing solutions.
• To gather data: Research methods can be used to collect data on a particular subject. This can be done through surveys, interviews, observations, experiments, and more.
• To evaluate programs : Research methods can be used to evaluate the effectiveness of a program, intervention, or policy. This can help in determining whether the program is meeting its goals and objectives.
• To explore new areas : Research methods can be used to explore new areas of inquiry or to test new hypotheses. This can help in advancing knowledge in a particular field.
• To make informed decisions : Research methods can be used to gather information and data to support informed decision-making. This can be useful in various fields such as healthcare, business, and education.

Advantages of Research Methods

Research methods provide several advantages, including:

• Objectivity : Research methods enable researchers to gather data in a systematic and objective manner, minimizing personal biases and subjectivity. This leads to more reliable and valid results.
• Replicability : A key advantage of research methods is that they allow for replication of studies by other researchers. This helps to confirm the validity of the findings and ensures that the results are not specific to the particular research team.
• Generalizability : Research methods enable researchers to gather data from a representative sample of the population, allowing for generalizability of the findings to a larger population. This increases the external validity of the research.
• Precision : Research methods enable researchers to gather data using standardized procedures, ensuring that the data is accurate and precise. This allows researchers to make accurate predictions and draw meaningful conclusions.
• Efficiency : Research methods enable researchers to gather data efficiently, saving time and resources. This is especially important when studying large populations or complex phenomena.
• Innovation : Research methods enable researchers to develop new techniques and tools for data collection and analysis, leading to innovation and advancement in the field.

About the author

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Researcher, Academic Writer, Web developer

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GPT-fabricated scientific papers on Google Scholar: Key features, spread, and implications for preempting evidence manipulation

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Academic journals, archives, and repositories are seeing an increasing number of questionable research papers clearly produced using generative AI. They are often created with widely available, general-purpose AI applications, most likely ChatGPT, and mimic scientific writing. Google Scholar easily locates and lists these questionable papers alongside reputable, quality-controlled research. Our analysis of a selection of questionable GPT-fabricated scientific papers found in Google Scholar shows that many are about applied, often controversial topics susceptible to disinformation: the environment, health, and computing. The resulting enhanced potential for malicious manipulation of society’s evidence base, particularly in politically divisive domains, is a growing concern.

Swedish School of Library and Information Science, University of Borås, Sweden

Department of Arts and Cultural Sciences, Lund University, Sweden

Division of Environmental Communication, Swedish University of Agricultural Sciences, Sweden

Research Questions

• Where are questionable publications produced with generative pre-trained transformers (GPTs) that can be found via Google Scholar published or deposited?
• What are the main characteristics of these publications in relation to predominant subject categories?
• How are these publications spread in the research infrastructure for scholarly communication?
• How is the role of the scholarly communication infrastructure challenged in maintaining public trust in science and evidence through inappropriate use of generative AI?

research note Summary

• A sample of scientific papers with signs of GPT-use found on Google Scholar was retrieved, downloaded, and analyzed using a combination of qualitative coding and descriptive statistics. All papers contained at least one of two common phrases returned by conversational agents that use large language models (LLM) like OpenAI’s ChatGPT. Google Search was then used to determine the extent to which copies of questionable, GPT-fabricated papers were available in various repositories, archives, citation databases, and social media platforms.
• Roughly two-thirds of the retrieved papers were found to have been produced, at least in part, through undisclosed, potentially deceptive use of GPT. The majority (57%) of these questionable papers dealt with policy-relevant subjects (i.e., environment, health, computing), susceptible to influence operations. Most were available in several copies on different domains (e.g., social media, archives, and repositories).
• Two main risks arise from the increasingly common use of GPT to (mass-)produce fake, scientific publications. First, the abundance of fabricated “studies” seeping into all areas of the research infrastructure threatens to overwhelm the scholarly communication system and jeopardize the integrity of the scientific record. A second risk lies in the increased possibility that convincingly scientific-looking content was in fact deceitfully created with AI tools and is also optimized to be retrieved by publicly available academic search engines, particularly Google Scholar. However small, this possibility and awareness of it risks undermining the basis for trust in scientific knowledge and poses serious societal risks.

Implications

The use of ChatGPT to generate text for academic papers has raised concerns about research integrity. Discussion of this phenomenon is ongoing in editorials, commentaries, opinion pieces, and on social media (Bom, 2023; Stokel-Walker, 2024; Thorp, 2023). There are now several lists of papers suspected of GPT misuse, and new papers are constantly being added. 1 See for example Academ-AI, https://www.academ-ai.info/ , and Retraction Watch, https://retractionwatch.com/papers-and-peer-reviews-with-evidence-of-chatgpt-writing/ . While many legitimate uses of GPT for research and academic writing exist (Huang & Tan, 2023; Kitamura, 2023; Lund et al., 2023), its undeclared use—beyond proofreading—has potentially far-reaching implications for both science and society, but especially for their relationship. It, therefore, seems important to extend the discussion to one of the most accessible and well-known intermediaries between science, but also certain types of misinformation, and the public, namely Google Scholar, also in response to the legitimate concerns that the discussion of generative AI and misinformation needs to be more nuanced and empirically substantiated  (Simon et al., 2023).

Google Scholar, https://scholar.google.com , is an easy-to-use academic search engine. It is available for free, and its index is extensive (Gusenbauer & Haddaway, 2020). It is also often touted as a credible source for academic literature and even recommended in library guides, by media and information literacy initiatives, and fact checkers (Tripodi et al., 2023). However, Google Scholar lacks the transparency and adherence to standards that usually characterize citation databases. Instead, Google Scholar uses automated crawlers, like Google’s web search engine (Martín-Martín et al., 2021), and the inclusion criteria are based on primarily technical standards, allowing any individual author—with or without scientific affiliation—to upload papers to be indexed (Google Scholar Help, n.d.). It has been shown that Google Scholar is susceptible to manipulation through citation exploits (Antkare, 2020) and by providing access to fake scientific papers (Dadkhah et al., 2017). A large part of Google Scholar’s index consists of publications from established scientific journals or other forms of quality-controlled, scholarly literature. However, the index also contains a large amount of gray literature, including student papers, working papers, reports, preprint servers, and academic networking sites, as well as material from so-called “questionable” academic journals, including paper mills. The search interface does not offer the possibility to filter the results meaningfully by material type, publication status, or form of quality control, such as limiting the search to peer-reviewed material.

To understand the occurrence of ChatGPT (co-)authored work in Google Scholar’s index, we scraped it for publications, including one of two common ChatGPT responses (see Appendix A) that we encountered on social media and in media reports (DeGeurin, 2024). The results of our descriptive statistical analyses showed that around 62% did not declare the use of GPTs. Most of these GPT-fabricated papers were found in non-indexed journals and working papers, but some cases included research published in mainstream scientific journals and conference proceedings. 2 Indexed journals mean scholarly journals indexed by abstract and citation databases such as Scopus and Web of Science, where the indexation implies journals with high scientific quality. Non-indexed journals are journals that fall outside of this indexation. More than half (57%) of these GPT-fabricated papers concerned policy-relevant subject areas susceptible to influence operations. To avoid increasing the visibility of these publications, we abstained from referencing them in this research note. However, we have made the data available in the Harvard Dataverse repository.

The publications were related to three issue areas—health (14.5%), environment (19.5%) and computing (23%)—with key terms such “healthcare,” “COVID-19,” or “infection”for health-related papers, and “analysis,” “sustainable,” and “global” for environment-related papers. In several cases, the papers had titles that strung together general keywords and buzzwords, thus alluding to very broad and current research. These terms included “biology,” “telehealth,” “climate policy,” “diversity,” and “disrupting,” to name just a few.  While the study’s scope and design did not include a detailed analysis of which parts of the articles included fabricated text, our dataset did contain the surrounding sentences for each occurrence of the suspicious phrases that formed the basis for our search and subsequent selection. Based on that, we can say that the phrases occurred in most sections typically found in scientific publications, including the literature review, methods, conceptual and theoretical frameworks, background, motivation or societal relevance, and even discussion. This was confirmed during the joint coding, where we read and discussed all articles. It became clear that not just the text related to the telltale phrases was created by GPT, but that almost all articles in our sample of questionable articles likely contained traces of GPT-fabricated text everywhere.

Evidence hacking and backfiring effects

Generative pre-trained transformers (GPTs) can be used to produce texts that mimic scientific writing. These texts, when made available online—as we demonstrate—leak into the databases of academic search engines and other parts of the research infrastructure for scholarly communication. This development exacerbates problems that were already present with less sophisticated text generators (Antkare, 2020; Cabanac & Labbé, 2021). Yet, the public release of ChatGPT in 2022, together with the way Google Scholar works, has increased the likelihood of lay people (e.g., media, politicians, patients, students) coming across questionable (or even entirely GPT-fabricated) papers and other problematic research findings. Previous research has emphasized that the ability to determine the value and status of scientific publications for lay people is at stake when misleading articles are passed off as reputable (Haider & Åström, 2017) and that systematic literature reviews risk being compromised (Dadkhah et al., 2017). It has also been highlighted that Google Scholar, in particular, can be and has been exploited for manipulating the evidence base for politically charged issues and to fuel conspiracy narratives (Tripodi et al., 2023). Both concerns are likely to be magnified in the future, increasing the risk of what we suggest calling evidence hacking —the strategic and coordinated malicious manipulation of society’s evidence base.

The authority of quality-controlled research as evidence to support legislation, policy, politics, and other forms of decision-making is undermined by the presence of undeclared GPT-fabricated content in publications professing to be scientific. Due to the large number of archives, repositories, mirror sites, and shadow libraries to which they spread, there is a clear risk that GPT-fabricated, questionable papers will reach audiences even after a possible retraction. There are considerable technical difficulties involved in identifying and tracing computer-fabricated papers (Cabanac & Labbé, 2021; Dadkhah et al., 2023; Jones, 2024), not to mention preventing and curbing their spread and uptake.

However, as the rise of the so-called anti-vaxx movement during the COVID-19 pandemic and the ongoing obstruction and denial of climate change show, retracting erroneous publications often fuels conspiracies and increases the following of these movements rather than stopping them. To illustrate this mechanism, climate deniers frequently question established scientific consensus by pointing to other, supposedly scientific, studies that support their claims. Usually, these are poorly executed, not peer-reviewed, based on obsolete data, or even fraudulent (Dunlap & Brulle, 2020). A similar strategy is successful in the alternative epistemic world of the global anti-vaccination movement (Carrion, 2018) and the persistence of flawed and questionable publications in the scientific record already poses significant problems for health research, policy, and lawmakers, and thus for society as a whole (Littell et al., 2024). Considering that a person’s support for “doing your own research” is associated with increased mistrust in scientific institutions (Chinn & Hasell, 2023), it will be of utmost importance to anticipate and consider such backfiring effects already when designing a technical solution, when suggesting industry or legal regulation, and in the planning of educational measures.

Recommendations

Solutions should be based on simultaneous considerations of technical, educational, and regulatory approaches, as well as incentives, including social ones, across the entire research infrastructure. Paying attention to how these approaches and incentives relate to each other can help identify points and mechanisms for disruption. Recognizing fraudulent academic papers must happen alongside understanding how they reach their audiences and what reasons there might be for some of these papers successfully “sticking around.” A possible way to mitigate some of the risks associated with GPT-fabricated scholarly texts finding their way into academic search engine results would be to provide filtering options for facets such as indexed journals, gray literature, peer-review, and similar on the interface of publicly available academic search engines. Furthermore, evaluation tools for indexed journals 3 Such as LiU Journal CheckUp, https://ep.liu.se/JournalCheckup/default.aspx?lang=eng . could be integrated into the graphical user interfaces and the crawlers of these academic search engines. To enable accountability, it is important that the index (database) of such a search engine is populated according to criteria that are transparent, open to scrutiny, and appropriate to the workings of  science and other forms of academic research. Moreover, considering that Google Scholar has no real competitor, there is a strong case for establishing a freely accessible, non-specialized academic search engine that is not run for commercial reasons but for reasons of public interest. Such measures, together with educational initiatives aimed particularly at policymakers, science communicators, journalists, and other media workers, will be crucial to reducing the possibilities for and effects of malicious manipulation or evidence hacking. It is important not to present this as a technical problem that exists only because of AI text generators but to relate it to the wider concerns in which it is embedded. These range from a largely dysfunctional scholarly publishing system (Haider & Åström, 2017) and academia’s “publish or perish” paradigm to Google’s near-monopoly and ideological battles over the control of information and ultimately knowledge. Any intervention is likely to have systemic effects; these effects need to be considered and assessed in advance and, ideally, followed up on.

Our study focused on a selection of papers that were easily recognizable as fraudulent. We used this relatively small sample as a magnifying glass to examine, delineate, and understand a problem that goes beyond the scope of the sample itself, which however points towards larger concerns that require further investigation. The work of ongoing whistleblowing initiatives 4 Such as Academ-AI, https://www.academ-ai.info/ , and Retraction Watch, https://retractionwatch.com/papers-and-peer-reviews-with-evidence-of-chatgpt-writing/ . , recent media reports of journal closures (Subbaraman, 2024), or GPT-related changes in word use and writing style (Cabanac et al., 2021; Stokel-Walker, 2024) suggest that we only see the tip of the iceberg. There are already more sophisticated cases (Dadkhah et al., 2023) as well as cases involving fabricated images (Gu et al., 2022). Our analysis shows that questionable and potentially manipulative GPT-fabricated papers permeate the research infrastructure and are likely to become a widespread phenomenon. Our findings underline that the risk of fake scientific papers being used to maliciously manipulate evidence (see Dadkhah et al., 2017) must be taken seriously. Manipulation may involve undeclared automatic summaries of texts, inclusion in literature reviews, explicit scientific claims, or the concealment of errors in studies so that they are difficult to detect in peer review. However, the mere possibility of these things happening is a significant risk in its own right that can be strategically exploited and will have ramifications for trust in and perception of science. Society’s methods of evaluating sources and the foundations of media and information literacy are under threat and public trust in science is at risk of further erosion, with far-reaching consequences for society in dealing with information disorders. To address this multifaceted problem, we first need to understand why it exists and proliferates.

Finding 1: 139 GPT-fabricated, questionable papers were found and listed as regular results on the Google Scholar results page. Non-indexed journals dominate.

Most questionable papers we found were in non-indexed journals or were working papers, but we did also find some in established journals, publications, conferences, and repositories. We found a total of 139 papers with a suspected deceptive use of ChatGPT or similar LLM applications (see Table 1). Out of these, 19 were in indexed journals, 89 were in non-indexed journals, 19 were student papers found in university databases, and 12 were working papers (mostly in preprint databases). Table 1 divides these papers into categories. Health and environment papers made up around 34% (47) of the sample. Of these, 66% were present in non-indexed journals.

Finding 2: GPT-fabricated, questionable papers are disseminated online, permeating the research infrastructure for scholarly communication, often in multiple copies. Applied topics with practical implications dominate.

The 20 papers concerning health-related issues are distributed across 20 unique domains, accounting for 46 URLs. The 27 papers dealing with environmental issues can be found across 26 unique domains, accounting for 56 URLs.  Most of the identified papers exist in multiple copies and have already spread to several archives, repositories, and social media. It would be difficult, or impossible, to remove them from the scientific record.

As apparent from Table 2, GPT-fabricated, questionable papers are seeping into most parts of the online research infrastructure for scholarly communication. Platforms on which identified papers have appeared include ResearchGate, ORCiD, Journal of Population Therapeutics and Clinical Pharmacology (JPTCP), Easychair, Frontiers, the Institute of Electrical and Electronics Engineer (IEEE), and X/Twitter. Thus, even if they are retracted from their original source, it will prove very difficult to track, remove, or even just mark them up on other platforms. Moreover, unless regulated, Google Scholar will enable their continued and most likely unlabeled discoverability.

A word rain visualization (Centre for Digital Humanities Uppsala, 2023), which combines word prominences through TF-IDF 5 Term frequency–inverse document frequency , a method for measuring the significance of a word in a document compared to its frequency across all documents in a collection. scores with semantic similarity of the full texts of our sample of GPT-generated articles that fall into the “Environment” and “Health” categories, reflects the two categories in question. However, as can be seen in Figure 1, it also reveals overlap and sub-areas. The y-axis shows word prominences through word positions and font sizes, while the x-axis indicates semantic similarity. In addition to a certain amount of overlap, this reveals sub-areas, which are best described as two distinct events within the word rain. The event on the left bundles terms related to the development and management of health and healthcare with “challenges,” “impact,” and “potential of artificial intelligence”emerging as semantically related terms. Terms related to research infrastructures, environmental, epistemic, and technological concepts are arranged further down in the same event (e.g., “system,” “climate,” “understanding,” “knowledge,” “learning,” “education,” “sustainable”). A second distinct event further to the right bundles terms associated with fish farming and aquatic medicinal plants, highlighting the presence of an aquaculture cluster.  Here, the prominence of groups of terms such as “used,” “model,” “-based,” and “traditional” suggests the presence of applied research on these topics. The two events making up the word rain visualization, are linked by a less dominant but overlapping cluster of terms related to “energy” and “water.”

The bar chart of the terms in the paper subset (see Figure 2) complements the word rain visualization by depicting the most prominent terms in the full texts along the y-axis. Here, word prominences across health and environment papers are arranged descendingly, where values outside parentheses are TF-IDF values (relative frequencies) and values inside parentheses are raw term frequencies (absolute frequencies).

Finding 3: Google Scholar presents results from quality-controlled and non-controlled citation databases on the same interface, providing unfiltered access to GPT-fabricated questionable papers.

Google Scholar’s central position in the publicly accessible scholarly communication infrastructure, as well as its lack of standards, transparency, and accountability in terms of inclusion criteria, has potentially serious implications for public trust in science. This is likely to exacerbate the already-known potential to exploit Google Scholar for evidence hacking (Tripodi et al., 2023) and will have implications for any attempts to retract or remove fraudulent papers from their original publication venues. Any solution must consider the entirety of the research infrastructure for scholarly communication and the interplay of different actors, interests, and incentives.

We searched and scraped Google Scholar using the Python library Scholarly (Cholewiak et al., 2023) for papers that included specific phrases known to be common responses from ChatGPT and similar applications with the same underlying model (GPT3.5 or GPT4): “as of my last knowledge update” and/or “I don’t have access to real-time data” (see Appendix A). This facilitated the identification of papers that likely used generative AI to produce text, resulting in 227 retrieved papers. The papers’ bibliographic information was automatically added to a spreadsheet and downloaded into Zotero. 6 An open-source reference manager, https://zotero.org .

We employed multiple coding (Barbour, 2001) to classify the papers based on their content. First, we jointly assessed whether the paper was suspected of fraudulent use of ChatGPT (or similar) based on how the text was integrated into the papers and whether the paper was presented as original research output or the AI tool’s role was acknowledged. Second, in analyzing the content of the papers, we continued the multiple coding by classifying the fraudulent papers into four categories identified during an initial round of analysis—health, environment, computing, and others—and then determining which subjects were most affected by this issue (see Table 1). Out of the 227 retrieved papers, 88 papers were written with legitimate and/or declared use of GPTs (i.e., false positives, which were excluded from further analysis), and 139 papers were written with undeclared and/or fraudulent use (i.e., true positives, which were included in further analysis). The multiple coding was conducted jointly by all authors of the present article, who collaboratively coded and cross-checked each other’s interpretation of the data simultaneously in a shared spreadsheet file. This was done to single out coding discrepancies and settle coding disagreements, which in turn ensured methodological thoroughness and analytical consensus (see Barbour, 2001). Redoing the category coding later based on our established coding schedule, we achieved an intercoder reliability (Cohen’s kappa) of 0.806 after eradicating obvious differences.

The ranking algorithm of Google Scholar prioritizes highly cited and older publications (Martín-Martín et al., 2016). Therefore, the position of the articles on the search engine results pages was not particularly informative, considering the relatively small number of results in combination with the recency of the publications. Only the query “as of my last knowledge update” had more than two search engine result pages. On those, questionable articles with undeclared use of GPTs were evenly distributed across all result pages (min: 4, max: 9, mode: 8), with the proportion of undeclared use being slightly higher on average on later search result pages.

To understand how the papers making fraudulent use of generative AI were disseminated online, we programmatically searched for the paper titles (with exact string matching) in Google Search from our local IP address (see Appendix B) using the googlesearch – python library(Vikramaditya, 2020). We manually verified each search result to filter out false positives—results that were not related to the paper—and then compiled the most prominent URLs by field. This enabled the identification of other platforms through which the papers had been spread. We did not, however, investigate whether copies had spread into SciHub or other shadow libraries, or if they were referenced in Wikipedia.

We used descriptive statistics to count the prevalence of the number of GPT-fabricated papers across topics and venues and top domains by subject. The pandas software library for the Python programming language (The pandas development team, 2024) was used for this part of the analysis. Based on the multiple coding, paper occurrences were counted in relation to their categories, divided into indexed journals, non-indexed journals, student papers, and working papers. The schemes, subdomains, and subdirectories of the URL strings were filtered out while top-level domains and second-level domains were kept, which led to normalizing domain names. This, in turn, allowed the counting of domain frequencies in the environment and health categories. To distinguish word prominences and meanings in the environment and health-related GPT-fabricated questionable papers, a semantically-aware word cloud visualization was produced through the use of a word rain (Centre for Digital Humanities Uppsala, 2023) for full-text versions of the papers. Font size and y-axis positions indicate word prominences through TF-IDF scores for the environment and health papers (also visualized in a separate bar chart with raw term frequencies in parentheses), and words are positioned along the x-axis to reflect semantic similarity (Skeppstedt et al., 2024), with an English Word2vec skip gram model space (Fares et al., 2017). An English stop word list was used, along with a manually produced list including terms such as “https,” “volume,” or “years.”

• Artificial Intelligence
• / Search engines

Cite this Essay

Haider, J., Söderström, K. R., Ekström, B., & Rödl, M. (2024). GPT-fabricated scientific papers on Google Scholar: Key features, spread, and implications for preempting evidence manipulation. Harvard Kennedy School (HKS) Misinformation Review . https://doi.org/10.37016/mr-2020-156

• / Appendix B

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This research has been supported by Mistra, the Swedish Foundation for Strategic Environmental Research, through the research program Mistra Environmental Communication (Haider, Ekström, Rödl) and the Marcus and Amalia Wallenberg Foundation [2020.0004] (Söderström).

Competing Interests

The authors declare no competing interests.

The research described in this article was carried out under Swedish legislation. According to the relevant EU and Swedish legislation (2003:460) on the ethical review of research involving humans (“Ethical Review Act”), the research reported on here is not subject to authorization by the Swedish Ethical Review Authority (“etikprövningsmyndigheten”) (SRC, 2017).

This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided that the original author and source are properly credited.

Data Availability

All data needed to replicate this study are available at the Harvard Dataverse: https://doi.org/10.7910/DVN/WUVD8X

Acknowledgements

The authors wish to thank two anonymous reviewers for their valuable comments on the article manuscript as well as the editorial group of Harvard Kennedy School (HKS) Misinformation Review for their thoughtful feedback and input.

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Research on the methods for correcting helicopter position on deck using a carrier robot.

1. Introduction

2. mathematical model and kinematic characteristic analysis, 2.1. the structure and correction principle of helicopter deck traction system, 2.2. analysis of traction motion characteristics of srcr, 3. the stepwise method of helicopter deck position correction, 3.1. the principle of stepwise correction method, 3.2. the relationship of speed cooperative control in the stepwise correction method, 4. the continuous method of helicopter deck position correction, 4.1. the principle of continuous correction method, 4.1.1. parallel landing position, 4.1.2. cross landing position, 4.2. the relationship of speed cooperative control in the continuous correction method, 5. simulation verification of the correction method, 5.1. simulation of the stepwise correction method, 5.2. simulation of the continuous correction method, 5.3. comparative analysis of the efficiency of the stepwise correction method and the continuous correction method, 5.3.1. efficiency analysis of the stepwise correction method on the impact of initial yaw angle and traction speed, 5.3.2. efficiency analysis of the stepwise correction method on the impact of initial yaw angle and lateral offsets, 5.3.3. efficiency analysis of the continuous correction method on the impact of initial yaw angle and traction speed, 5.3.4. efficiency analysis of the continuous correction method on the impact of initial yaw angle and lateral offsets, 5.3.5. comparison of performance between stepwise and continuous correction methods, 6. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Zhong, Y.; Zhao, D.; Zhao, X. Research on the Methods for Correcting Helicopter Position on Deck Using a Carrier Robot. Actuators 2024 , 13 , 342. https://doi.org/10.3390/act13090342

Zhong Y, Zhao D, Zhao X. Research on the Methods for Correcting Helicopter Position on Deck Using a Carrier Robot. Actuators . 2024; 13(9):342. https://doi.org/10.3390/act13090342

Zhong, Yuhang, Dingxuan Zhao, and Xiaolong Zhao. 2024. "Research on the Methods for Correcting Helicopter Position on Deck Using a Carrier Robot" Actuators 13, no. 9: 342. https://doi.org/10.3390/act13090342

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Methodology

• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on June 19, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analyzing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

• How does social media shape body image in teenagers?
• How do children and adults interpret healthy eating in the UK?
• What factors influence employee retention in a large organization?
• How is anxiety experienced around the world?
• How can teachers integrate social issues into science curriculums?

Table of contents

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, other interesting articles, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography , action research , phenomenological research, and narrative research. They share some similarities, but emphasize different aims and perspectives.

Note that qualitative research is at risk for certain research biases including the Hawthorne effect , observer bias , recall bias , and social desirability bias . While not always totally avoidable, awareness of potential biases as you collect and analyze your data can prevent them from impacting your work too much.

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Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

• Observations: recording what you have seen, heard, or encountered in detailed field notes.
• Interviews:  personally asking people questions in one-on-one conversations.
• Focus groups: asking questions and generating discussion among a group of people.
• Surveys : distributing questionnaires with open-ended questions.
• Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
• You take field notes with observations and reflect on your own experiences of the company culture.
• You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
• You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves “instruments” in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analyzing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

• Prepare and organize your data. This may mean transcribing interviews or typing up fieldnotes.
• Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
• Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorize your data.
• Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
• Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analyzing qualitative data. Although these methods share similar processes, they emphasize different concepts.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

• Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

• Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

• Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

• Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

Researchers must consider practical and theoretical limitations in analyzing and interpreting their data. Qualitative research suffers from:

• Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

• Subjectivity

Due to the researcher’s primary role in analyzing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

• Limited generalizability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalizable conclusions because the data may be biased and unrepresentative of the wider population .

• Labor-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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• Volume 58, Issue 17
• Where is the research on sport-related concussion in Olympic athletes? A descriptive report and assessment of the impact of access to multidisciplinary care on recovery
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• http://orcid.org/0000-0002-3298-5719 Thomas Romeas 1 , 2 , 3 ,
• http://orcid.org/0000-0003-1748-7241 Félix Croteau 3 , 4 , 5 ,
• Suzanne Leclerc 3 , 4
• 1 Sport Sciences , Institut national du sport du Québec , Montreal , Quebec , Canada
• 2 School of Optometry , Université de Montréal , Montreal , Quebec , Canada
• 3 IOC Research Centre for Injury Prevention and Protection of Athlete Health , Réseau Francophone Olympique de la Recherche en Médecine du Sport , Montreal , Quebec , Canada
• 4 Sport Medicine , Institut national du sport du Québec , Montreal , Quebec , Canada
• 5 School of Physical and Occupational Therapy , McGill University , Montreal , Quebec , Canada
• Correspondence to Dr Thomas Romeas; thomas.romeas{at}umontreal.ca

Objectives This cohort study reported descriptive statistics in athletes engaged in Summer and Winter Olympic sports who sustained a sport-related concussion (SRC) and assessed the impact of access to multidisciplinary care and injury modifiers on recovery.

Methods 133 athletes formed two subgroups treated in a Canadian sport institute medical clinic: earlier (≤7 days) and late (≥8 days) access. Descriptive sample characteristics were reported and unrestricted return to sport (RTS) was evaluated based on access groups as well as injury modifiers. Correlations were assessed between time to RTS, history of concussions, the number of specialist consults and initial symptoms.

Results 160 SRC (median age 19.1 years; female=86 (54%); male=74 (46%)) were observed with a median (IQR) RTS duration of 34.0 (21.0–63.0) days. Median days to care access was different in the early (1; n SRC =77) and late (20; n SRC =83) groups, resulting in median (IQR) RTS duration of 26.0 (17.0–38.5) and 45.0 (27.5–84.5) days, respectively (p<0.001). Initial symptoms displayed a meaningful correlation with prognosis in this study (p<0.05), and female athletes (52 days (95% CI 42 to 101)) had longer recovery trajectories than male athletes (39 days (95% CI 31 to 65)) in the late access group (p<0.05).

Conclusions Olympic athletes in this cohort experienced an RTS time frame of about a month, partly due to limited access to multidisciplinary care and resources. Earlier access to care shortened the RTS delay. Greater initial symptoms and female sex in the late access group were meaningful modifiers of a longer RTS.

• Brain Concussion
• Cohort Studies
• Retrospective Studies

Data availability statement

Data are available on reasonable request. Due to the confidential nature of the dataset, it will be shared through a controlled access repository and made available on specific and reasonable requests.

https://doi.org/10.1136/bjsports-2024-108211

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WHAT IS ALREADY KNOWN ON THIS TOPIC

Most data regarding the impact of sport-related concussion (SRC) guidelines on return to sport (RTS) are derived from collegiate or recreational athletes. In these groups, time to RTS has steadily increased in the literature since 2005, coinciding with the evolution of RTS guidelines. However, current evidence suggests that earlier access to care may accelerate recovery and RTS time frames.

WHAT THIS STUDY ADDS

This study reports epidemiological data on the occurrence of SRC in athletes from several Summer and Winter Olympic sports with either early or late access to multidisciplinary care. We found the median time to RTS for Olympic athletes with an SRC was 34.0 days which is longer than that reported in other athletic groups such as professional or collegiate athletes. Time to RTS was reduced by prompt access to multidisciplinary care following SRC, and sex-influenced recovery in the late access group with female athletes having a longer RTS timeline. Greater initial symptoms, but not prior concussion history, were also associated with a longer time to RTS.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

Considerable differences exist in access to care for athletes engaged in Olympic sports, which impact their recovery. In this cohort, several concussions occurred during international competitions where athletes are confronted with poor access to organised healthcare. Pathways for prompt access to multidisciplinary care should be considered by healthcare authorities, especially for athletes who travel internationally and may not have the guidance or financial resources to access recommended care.

Introduction

After two decades of consensus statements, sport-related concussion (SRC) remains a high focus of research, with incidence ranging from 0.1 to 21.5 SRC per 1000 athlete exposures, varying according to age, sex, sport and level of competition. 1 2 Evidence-based guidelines have been proposed by experts to improve its identification and management, such as those from the Concussion in Sport Group. 3 Notably, they recommend specific strategies to improve SRC detection and monitoring such as immediate removal, 4 prompt access to healthcare providers, 5 evidence-based interventions 6 and multidisciplinary team approaches. 7 It is believed that these guidelines contribute to improving the early identification and management of athletes with an SRC, thereby potentially mitigating its long-term consequences.

Nevertheless, evidence regarding the impact of SRC guidelines implementation remains remarkably limited, especially within high-performance sport domains. In fact, most reported SRC data focus on adolescent student-athletes, collegiate and sometimes professional athletes in the USA but often neglect Olympians. 1 2 8–11 Athletes engaged in Olympic sports, often referred to as elite amateurs, are typically classified among the highest performers in elite sport, alongside professional athletes. 12 13 They train year-round and uniquely compete regularly on the international stage in sports that often lack professional leagues and rely on highly variable resources and facilities, mostly dependent on winning medals. 14 Unlike professional athletes, Olympians do not have access to large financial rewards. Although some Olympians work or study in addition to their intensive sports practice, they can devote more time to full-time sports practice compared with collegiate athletes. Competition calendars in Olympians differ from collegiate athletes, with periodic international competitions (eg, World Cups, World Championships) throughout the whole year rather than regular domestic competitions within a shorter season (eg, semester). Olympians outclass most collegiate athletes, and only the best collegiate athletes will have the chance to become Olympians and/or professionals. 12 13 15 In Canada, a primary reason for limited SRC data in Olympic sports is that the Canadian Olympic and Paralympic Sports Institute (COPSI) network only adopted official guidelines in 2018 to standardise care for athletes’ SRC nationwide. 16 17 The second reason could be the absence of a centralised medical structure and surveillance systems, identified as key factors contributing to the under-reporting and underdiagnosis of athletes with an SRC. 18

Among the available evidence on the evolution of SRC management, a 2023 systematic review and meta-analysis in athletic populations including children, adolescents and adults indicated that a full return to sport (RTS) could take up to a month but is estimated to require 19.8 days on average (15.4 days in adults), as opposed to the initial expectation of approximately 10.0 days based on studies published prior to 2005. 19 In comparison, studies focusing strictly on American collegiate athletes report median times to RTS of 16 days. 9 20 21 Notably, a recent study of military cadets reported an even longer return to duty times of 29.4 days on average, attributed to poorer access to care and fewer incentives to return to play compared with elite sports. 22 In addition, several modifiers have also been identified as influencing the time to RTS, such as the history of concussions, type of sport, sex, past medical problems (eg, preinjury modifiers), as well as the initial number of symptoms and their severity (eg, postinjury modifiers). 20 22 The evidence regarding the potential influence of sex on the time to RTS has yielded mixed findings in this area. 23–25 In fact, females are typically under-represented in SRC research, highlighting the need for additional studies that incorporate more balanced sample representation across sexes and control for known sources of bias. 26 Interestingly, a recent Concussion Assessment, Research and Education Consortium study, which included a high representation of concussed female athletes (615 out of 1071 patients), revealed no meaningful differences in RTS between females and males (13.5 and 11.8 days, respectively). 27 Importantly, findings in the sporting population suggested that earlier initiation of clinical care is linked to shorter recovery after concussion. 5 28 However, these factors affecting the time to RTS require a more thorough investigation, especially among athletes engaged in Olympic sports who may or may not have equal access to prompt, high-quality care.

Therefore, the primary objective of this study was to provide descriptive statistics among athletes with SRC engaged in both Summer and Winter Olympic sport programmes over a quadrennial, and to assess the influence of recommended guidelines of the COPSI network and the fifth International Consensus Conference on Concussion in Sport on the duration of RTS performance. 16 17 Building on available evidence, the international schedule constraints, variability in resources 14 and high-performance expectation among this elite population, 22 prolonged durations for RTS, compared with what is typically reported (eg, 16.0 or 15.4 days), were hypothesised in Olympians. 3 19 The secondary objective was to more specifically evaluate the impact of access to multidisciplinary care and injury modifiers on the time to RTS. Based on current evidence, 5 7 29 30 the hypothesis was formulated that athletes with earlier multidisciplinary access would experience a faster RTS. Regarding injury modifiers, it was expected that female and male athletes would show similar time to RTS despite presenting sex-specific characteristics of SRC. 31 The history of concussions, the severity of initial symptoms and the number of specialist consults were expected to be positively correlated to the time to RTS. 20 32

Participants

A total of 133 athletes (F=72; M=61; mean age±SD: 20.7±4.9 years old) who received medical care at the Institut national du sport du Québec, a COPSI training centre set up with a medical clinic, were included in this cohort study with retrospective analysis. They participated in 23 different Summer and Winter Olympic sports which were classified into six categories: team (soccer, water polo), middle distance/power (rowing, swimming), speed/strength (alpine skiing, para alpine skiing, short and long track speed skating), precision/skill-dependent (artistic swimming, diving, equestrian, figure skating, gymnastics, skateboard, synchronised skating, trampoline) and combat/weight-making (boxing, fencing, judo, para judo, karate, para taekwondo, wrestling) sports. 13 This sample consists of two distinct groups: (1) early access group in which athletes had access to a medical integrated support team of multidisciplinary experts within 7 days following their SRC and (2) late access group composed of athletes who had access to a medical integrated support team of multidisciplinary experts eight or more days following their SRC. 5 30 Inclusion criteria for the study were participation in a national or international-level sports programme 13 and having sustained at least one SRC diagnosed by an authorised healthcare practitioner (eg, physician and/or physiotherapist).

Clinical context

The institute clinic provides multidisciplinary services for care of patients with SRC including a broad range of recommended tests for concussion monitoring ( table 1 ). The typical pathway for the athletes consisted of an initial visit to either a sports medicine physician or their team sports therapist. A clinical diagnosis of SRC was then confirmed by a sports medicine physician, and referral for the required multidisciplinary assessments ensued based on the patient’s signs and symptoms. Rehabilitation progression was based on the evaluation of exercise tolerance, 33 priority to return to cognitive tasks and additional targeted support based on clinical findings of a cervical, visual or vestibular nature. 17 The expert team worked in an integrated manner with the athlete and their coaching staff for the rehabilitation phase, including regular round tables and ongoing communication. 34 For some athletes, access to recommended care was fee based, without a priori agreements with a third party payer (eg, National Sports Federation).

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Main evaluations performed to guide the return to sport following sport-related concussion

Data collection

Data were collected at the medical clinic using a standardised injury surveillance form based on International Olympic Committee guidelines. 35 All injury characteristics were extracted from the central injury database between 1 July 2018 and 31 July 2022. This period corresponds to a Winter Olympic sports quadrennial but also covers 3 years for Summer Olympic sports due to the postponing of the Tokyo 2020 Olympic Games. Therefore, the observation period includes a typical volume of competitions across sports and minimises differences in exposure based on major sports competition schedules. The information extracted from the database included: participant ID, sex, date of birth, sport, date of injury, type of injury, date of their visit at the clinic, clearance date of unrestricted RTS (eg, defined as step 6 of the RTS strategy with a return to normal gameplay including competitions), the number and type of specialist consults, mechanism of injury (eg, fall, hit), environment where the injury took place (eg, training, competition), history of concussions, history of modifiers (eg, previous head injury, migraines, learning disability, attention deficit disorder or attention deficit/hyperactivity disorder, depression, anxiety, psychotic disorder), as well as the number of symptoms and the total severity score from the first Sport Concussion Assessment Tool 5 (SCAT5) assessment following SRC. 17

Following a Shapiro-Wilk test, medians, IQR and non-parametric tests were used for the analyses because of the absence of normal distributions for all the variables in the dataset (all p<0.001). The skewness was introduced by the presence of individuals that required lengthy recovery periods. One participant was removed from the analysis because their time to consult with the multidisciplinary team was extremely delayed (>1 year).

Descriptive statistics were used to describe the participant’s demographics, SRC characteristics and risk factors in the total sample. Estimated incidences of SRC were also reported for seven resident sports at the institute for which it was possible to quantify a detailed estimate of training volume based on the annual number of training and competition hours as well as the number of athletes in each sport.

To assess if access to multidisciplinary care modified the time to RTS, we compared time to RTS between early and late access groups using a method based on median differences described elsewhere. 36 Wilcoxon rank sum tests were also performed to make between-group comparisons on single variables of age, time to first consult, the number of specialists consulted and medical visits. Fisher’s exact tests were used to compare count data between groups on variables of sex, history of concussion, time since the previous concussion, presence of injury modifiers, environment and mechanism of injury. Bonferroni corrections were applied for multiple comparisons in case of meaningful differences.

To assess if injury modifiers modified time to RTS in the total sample, we compared time to RTS between sexes, history of concussions, time since previous concussion or other injury modifiers using a method based on median differences described elsewhere. 36 Kaplan-Meier curves were drawn to illustrate time to RTS differences between sexes (origin and start time: date of injury; end time: clearance date of unrestricted RTS). Trajectories were then assessed for statistical differences using Cox proportional hazards model. Wilcoxon rank sum tests were employed for comparing the total number of symptoms and severity scores on the SCAT5. The association of multilevel variables on return to play duration was evaluated in the total sample with Kruskal-Wallis rank tests for environment, mechanism of injury, history of concussions and time since previous concussion. For all subsequent analyses of correlations between SCAT5 results and secondary variables, only data obtained from SCAT5 assessments within the acute phase of injury (≤72 hours) were considered (n=65 SRC episodes in the early access group). 37 Spearman rank correlations were estimated between RTS duration, history of concussions, number of specialist consults and total number of SCAT5 symptoms or total symptom severity. All statistical tests were performed using RStudio (R V.4.1.0, The R Foundation for Statistical Computing). The significance level was set to p<0.05.

Equity, diversity and inclusion statement

The study population is representative of the Canadian athletic population in terms of age, gender, demographics and includes a balanced representation of female and male athletes. The study team consists of investigators from different disciplines and countries, but with a predominantly white composition and under-representation of other ethnic groups. Our study population encompasses data from the Institut national du sport du Québec, covering individuals of all genders, ethnicities and geographical regions across Canada.

Patient and public involvement

The patients or the public were not involved in the design, conduct, reporting or dissemination plans of our research.

Sample characteristics

During the 4-year period covered by this retrospective chart review, a total of 160 SRC episodes were recorded in 132 athletes with a median (IQR) age of 19.1 (17.8–22.2) years old ( table 2 ). 13 female and 10 male athletes had multiple SRC episodes during this time. The sample had a relatively balanced number of females (53.8%) and males (46.2%) with SRC included. 60% of the sample reported a history of concussion, with 35.0% reporting having experienced more than two episodes. However, most of these concussions had occurred more than 1 year before the SRC for which they were being treated. Within this sample, 33.1% of participants reported a history of injury modifiers. Importantly, the median (IQR) time to first clinic consult was 10.0 (1.0–20.0) days and the median (IQR) time to RTS was 34.0 (21.0–63.0) days in this sample ( table 3 ). The majority of SRCs occurred during training (56.3%) rather than competition (33.1%) and were mainly due to a fall (63.7%) or a hit (31.3%). The median (IQR) number of follow-up consultations and specialists consulted after the SRC were, respectively, 9 (5.0–14.3) and 3 (2.0–4.0).

Participants demographics

Sport-related concussion characteristics

Among seven sports of the total sample (n=89 SRC), the estimated incidence of athletes with SRC was highest in short-track speed skating (0.47/1000 hours; 95% CI 0.3 to 0.6), and lower in boxing, trampoline, water polo, judo, artistic swimming, and diving (0.24 (95% CI 0.0 to 0.5), 0.16 (95% CI 0.0 to 0.5), 0.13 (95% CI 0.1 to 0.2), 0.11 (95% CI 0.1 to 0.2), 0.09 (95% CI 0.0 to 0.2) and 0.06 (95% CI 0.0 to 0.1)/1000, respectively ( online supplemental material ). Furthermore, most athletes sustained an SRC in training (66.5%; 95% CI 41.0 to 92.0) rather than competition (26.0%; 95% CI 0.0 to 55.0) except for judo athletes (20.0% (95% CI 4.1 to 62.0) and 80.0% (95% CI 38.0 to 96.0), respectively). Falls were the most common injury mechanism in speed skating, trampoline and judo while hits were the most common injury mechanism in boxing, water polo, artistic swimming and diving.

Supplemental material

Access to care.

The median difference in time to RTS was 19 days (95% CI 9.3 to 28.7; p<0.001) between the early (26 (IQR 17.0–38.5) days) and late (45 (IQR 27.5–84.5) days) access groups ( table 3 ; figure 1 ). Importantly, the distribution of SRC environments was different between both groups (p=0.008). The post hoc analysis demonstrated a meaningful difference in the distribution of SRC in training and competition environments between groups (p=0.029) but not for the other comparisons. There was a meaningful difference between the groups in time to first consult (p<0.001; 95% CI −23.0 to −15.0), but no meaningful differences between groups in median age (p=0.176; 95% CI −0.3 to 1.6), sex distribution (p=0.341; 95% CI 0.7 to 2.8), concussion history (p=0.210), time since last concussion (p=0.866), mechanisms of SRC (p=0.412), the presence of modifiers (p=0.313; 95% CI 0.3 to 1.4) and the number of consulted specialists (p=0.368; 95% CI −5.4 to 1.0) or medical visits (p=0.162; 95% CI −1.0 to 3.0).

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Time to return to sport following sport-related concussion as a function of group’s access to care and sex. Outliers: below=Q1−1.5×IQR; above=Q3+1.5×IQR.

The median difference in time to RTS was 6.5 days (95% CI −19.3 to 5.3; p=0.263; figure 1 ) between female (37.5 (IQR 22.0–65.3) days) and male (31.0 (IQR 20.0–48.0) days) athletes. Survival analyses highlighted an increased hazard of longer recovery trajectory in female compared with male athletes (HR 1.4; 95% CI 1.4 to 0.7; p=0.052; figure 2A ), which was mainly driven by the late (HR 1.8; 95% CI 1.8 to 0.6; p=0.019; figure 2C ) rather than the early (HR 1.1; 95% CI 1.1 to 0.9; p=0.700; figure 2B ) access group. Interestingly, a greater number of female athletes (n=15) required longer than 100 days for RTS as opposed to the male athletes (n=6). There were no meaningful differences between sexes for the total number of symptoms recorded on the SCAT5 (p=0.539; 95% CI −1.0 to 2.0) nor the total symptoms total severity score (p=0.989; 95% CI −5.0 to 5.0).

Time analysis of sex differences in the time to return to sport following sport-related concussion in the (A) total sample, as well as (B) early, and (C) late groups using survival curves with 95% confidence bands and tables of time-specific number of patients at risk (censoring proportion: 0%).

History of modifiers

SRC modifiers are presented in table 2 , and their influence on RTP is shown in table 4 . The median difference in time to RTS was 1.5 days (95% CI −10.6 to 13.6; p=0.807) between athletes with none and one episode of previous concussion, was 3.5 days (95% CI −13.9 to 19.9; p=0.728) between athletes with none and two or more episodes of previous concussion, and was 2 days (95% CI −12.4 to 15.4; p=0.832) between athletes with one and two or more episodes of previous concussion. The history of concussions (none, one, two or more) had no meaningful impact on the time to RTS (p=0.471). The median difference in time to RTS was 4.5 days (95% CI −21.0 to 30.0; p=0.729) between athletes with none and one episode of concussion in the previous year, was 2 days (95% CI −10.0 to 14.0; p=0.744) between athletes with none and one episode of concussion more than 1 year ago, and was 2.5 days (95% CI −27.7 to 22.7; p=0.846) between athletes with an episode of concussion in the previous year and more than 1 year ago. Time since the most recent concussion did not change the time to RTS (p=0.740). The longest time to RTS was observed in the late access group in which athletes had a concussion in the previous year, with a very large spread of durations (65.0 (IQR 33.0–116.5) days). The median difference in time to RTS was 3 days (95% CI −13.1 to 7.1; p=0.561) between athletes with and without other injury modifiers. The history of other injury modifiers had no meaningful influence on the time to RTS (95% CI −6.0 to 11.0; p=0.579).

Preinjury modifiers of time to return to sport following SRC

SCAT5 symptoms and severity scores

Positive associations were observed between the time to RTS and the number of initial symptoms (r=0.3; p=0.010; 95% CI 0.1 to 0.5) or initial severity score (r=0.3; p=0.008; 95% CI 0.1 to 0.5) from the SCAT5. The associations were not meaningful between the number of specialist consultations and the initial number of symptoms (r=−0.1; p=0.633; 95% CI −0.3 to 0.2) or initial severity score (r=−0.1; p=0.432; 95% CI −0.3 to 0.2). Anecdotally, most reported symptoms following SRC were ‘headache’ (86.2%) and ‘pressure in the head’ (80.0%), followed by ‘fatigue’ (72.3%), ‘neck pain’ (70.8%) and ‘not feeling right’ (67.7%; online supplemental material ).

This study is the first to report descriptive data on athletes with SRC collected across several sports during an Olympic quadrennial, including athletes who received the most recent evidence-based care at the time of data collection. Primarily, results indicate that the time to RTS in athletes engaged in Summer and Winter Olympic sports may require a median (IQR) of 34.0 (21.0–63.0) days. Importantly, findings demonstrated that athletes with earlier (≤7 days) access to multidisciplinary concussion care showed faster RTS compared with those with late access. Time to RTS exhibited large variability where sex had a meaningful influence on the recovery pathway in the late access group. Initial symptoms, but not history of concussion, were correlated with prognosis in this sample. The main reported symptoms were consistent with previous studies. 38 39

Time to RTS in Olympic sports

This study provides descriptive data on the impact of SRC monitoring programmes on recovery in elite athletes engaged in Olympic sports. As hypothesised, the median time to RTS found in this study (eg, 34.0 days) was about three times longer than those found in reports from before 2005, and 2 weeks longer than the typical median values (eg, 19.8 days) recently reported in athletic levels including youth (high heterogeneity, I 2 =99.3%). 19 These durations were also twice as long as the median unrestricted time to RTS observed among American collegiate athletes, which averages around 16 days. 9 20 21 However, they were more closely aligned with findings from collegiate athletes with slow recovery (eg, 34.7 days) and evidence from military cadets with poor access where return to duty duration was 29.4 days. 8 22 Several reasons could explain such extended time to RTS, but the most likely seems to be related to the diversity in access among these sports to multidisciplinary services (eg, 10.0 median days (1–20)), well beyond the delays experienced by collegiate athletes, for example (eg, 0.0 median days (0–2)). 40 In the total sample, the delays to first consult with the multidisciplinary clinic were notably mediated by the group with late access, whose athletes had more SRC during international competition. One of the issues for athletes engaged in Olympic sports is that they travel abroad year-round for competitions, in contrast with collegiate athletes who compete domestically. These circumstances likely make access to quality care very variable and make the follow-up of care less centralised. Also, access to resources among these sports is highly variable (eg, medal-dependant), 14 and at the discretion of the sport’s leadership (eg, sport federation), who may decide to prioritise more or fewer resources to concussion management considering the relatively low incidence of this injury. Another explanation for the longer recovery times in these athletes could be the lack of financial incentives to return to play faster, which are less prevalent among Olympic sports compared with professionals. However, the stakes of performance and return to play are still very high among these athletes.

Additionally, it is plausible that studies vary their outcome with shifting operational definitions such as resolution of symptoms, return to activities, graduated return to play or unrestricted RTS. 19 40 It is understood that resolution of symptoms may occur much earlier than return to preinjury performance levels. Finally, an aspect that has been little studied to date is the influence of the sport’s demands on the RTS. For example, acrobatic sports requiring precision/technical skills such as figure skating, trampoline and diving, which involve high visuospatial and vestibular demands, 41 might require more time to recover or elicit symptoms for longer times. Anecdotally, athletes who experienced a long time to RTS (>100 days) were mostly from precision/skill-dependent sports in this sample. The sports demand should be further considered as an injury modifier. More epidemiological reports that consider the latest guidelines are therefore necessary to gain a better understanding of the true time to RTS and impact following SRC in Olympians.

Supporting early multidisciplinary access to care

In this study, athletes who obtained early access to multidisciplinary care after SRC recovered faster than those with late access to multidisciplinary care. This result aligns with findings showing that delayed access to a healthcare practitioner delays recovery, 19 including previous evidence in a sample of patients from a sports medicine clinic (ages 12–22), indicating that the group with a delayed first clinical visit (eg, 8–20 days) was associated with a 5.8 times increased likelihood of a recovery longer than 30 days. 5 Prompt multidisciplinary approach for patients with SRC is suggested to yield greater effectiveness over usual care, 3 6 17 which is currently evaluated under randomised controlled trial. 42 Notably, early physical exercise and prescribed exercise (eg, 48 hours postinjury) are effective in improving recovery compared with strict rest or stretching. 43 44 In fact, preclinical and clinical studies have shown that exercise has the potential to improve neurotransmission, neuroplasticity and cerebral blood flow which supports that the physically trained brain enhanced recovery. 45 46 Prompt access to specialised healthcare professionals can be challenging in some contexts (eg, during international travel), and the cost of accessing medical care privately may prove further prohibitive. This barrier to recovery should be a priority for stakeholders in Olympic sports and given more consideration by health authorities.

Estimated incidences and implications

The estimated incidences of SRC were in the lower range compared with what is reported in other elite sport populations. 1 2 However, the burden of injury remained high for these sports, and the financial resources as well as expertise required to facilitate athletes’ rehabilitation was considerable (median number of consultations: 9.0). Notably, the current standard of public healthcare in Canada does not subsidise the level of support recommended following SRC as first-line care, and the financial subsidisation of this recommended care within each federation is highly dependent on the available funding, varying significantly between sports. 14 Therefore, the ongoing efforts to improve education, prevention and early recognition, modification of rules to make the environments safer and multidisciplinary care access for athletes remain crucial. 7

Strength and limitations

This unique study provides multisport characteristics following the evolution of concussion guidelines in Summer and Winter Olympic sports in North America. Notably, it features a balance between the number of female and male athletes, allowing the analysis of sex differences. 23 26 In a previous review of 171 studies informing consensus statements, samples were mostly composed of more than 80% of male participants, and more than 40% of these studies did not include female participants at all. 26 This study also included multiple non-traditional sports typically not encompassed in SRC research, feature previously identified as a key requirement of future epidemiological research. 47

However, it must be acknowledged that potential confounding factors could influence the results. For example, the number of SRC detected during the study period does not account for potentially unreported concussions. Nevertheless, this figure should be minimal because these athletes are supervised both in training and in competition by medical staff. Next, the sport types were heterogeneous, with inconsistent risk for head impacts or inconsistent sport demand which might have an influence on recovery. Furthermore, the number of participants or sex in each sport was not evenly distributed, with short-track speed skaters representing a large portion of the overall sample (32.5%), for example. Additionally, the number of participants with specific modifiers was too small in the current sample to conclude whether the presence of precise characteristics (eg, history of concussion) impacted the time to RTS. Also, the group with late access was more likely to consist of athletes who sought specialised care for persistent symptoms. These complex cases are often expected to require additional time to recover. 48 Furthermore, athletes in the late group may have sought support outside of the institute medical clinic, without a coordinated multidisciplinary approach. Therefore, the estimation of clinical consultations was tentative for this group and may represent a potential confounding factor in this study.

This is the first study to provide evidence of the prevalence of athletes with SRC and modifiers of recovery in both female and male elite-level athletes across a variety of Summer and Winter Olympic sports. There was a high variability in access to care in this group, and the median (IQR) time to RTS following SRC was 34.0 (21.0–63.0) days. Athletes with earlier access to multidisciplinary care took nearly half the time to RTS compared with those with late access. Sex had a meaningful influence on the recovery pathway in the late access group. Initial symptom number and severity score but not history of concussion were meaningful modifiers of recovery. Injury surveillance programmes targeting national sport organisations should be prioritised to help evaluate the efficacy of recommended injury monitoring programmes and to help athletes engaged in Olympic sports who travel a lot internationally have better access to care. 35 49

Ethics statements

Patient consent for publication.

Not applicable.

Ethics approval

This study involves human participants and was approved by the ethics board of Université de Montréal (certificate #2023-4052). Participants gave informed consent to participate in the study before taking part.

Acknowledgments

The authors would like to thank the members of the concussion interdisciplinary clinic of the Institut national du sport du Québec for collecting the data and for their unconditional support to the athletes.

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Supplementary materials

Supplementary data.

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• Data supplement 1

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Correction notice This article has been corrected since it published Online First. The ORCID details have been added for Dr Croteau.

Contributors TR, FC and SL were involved in planning, conducting and reporting the work. François Bieuzen and Magdalena Wojtowicz critically reviewed the manuscript. TR is guarantor.

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests None declared.

Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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