difference introduction and literature review

How to write a literature review introduction (+ examples)

The introduction to a literature review serves as your reader’s guide through your academic work and thought process. Explore the significance of literature review introductions in review papers, academic papers, essays, theses, and dissertations. We delve into the purpose and necessity of these introductions, explore the essential components of literature review introductions, and provide step-by-step guidance on how to craft your own, along with examples.

Why you need an introduction for a literature review

When you need an introduction for a literature review, what to include in a literature review introduction, examples of literature review introductions, steps to write your own literature review introduction.

A literature review is a comprehensive examination of the international academic literature concerning a particular topic. It involves summarizing published works, theories, and concepts while also highlighting gaps and offering critical reflections.

In academic writing , the introduction for a literature review is an indispensable component. Effective academic writing requires proper paragraph structuring to guide your reader through your argumentation. This includes providing an introduction to your literature review.

It is imperative to remember that you should never start sharing your findings abruptly. Even if there isn’t a dedicated introduction section .

Instead, you should always offer some form of introduction to orient the reader and clarify what they can expect.

There are three main scenarios in which you need an introduction for a literature review:

• Academic literature review papers: When your literature review constitutes the entirety of an academic review paper, a more substantial introduction is necessary. This introduction should resemble the standard introduction found in regular academic papers.
• Literature review section in an academic paper or essay: While this section tends to be brief, it’s important to precede the detailed literature review with a few introductory sentences. This helps orient the reader before delving into the literature itself.
• Literature review chapter or section in your thesis/dissertation: Every thesis and dissertation includes a literature review component, which also requires a concise introduction to set the stage for the subsequent review.

You may also like: How to write a fantastic thesis introduction (+15 examples)

It is crucial to customize the content and depth of your literature review introduction according to the specific format of your academic work.

In practical terms, this implies, for instance, that the introduction in an academic literature review paper, especially one derived from a systematic literature review , is quite comprehensive. Particularly compared to the rather brief one or two introductory sentences that are often found at the beginning of a literature review section in a standard academic paper. The introduction to the literature review chapter in a thesis or dissertation again adheres to different standards.

Here’s a structured breakdown based on length and the necessary information:

Academic literature review paper

The introduction of an academic literature review paper, which does not rely on empirical data, often necessitates a more extensive introduction than the brief literature review introductions typically found in empirical papers. It should encompass:

• The research problem: Clearly articulate the problem or question that your literature review aims to address.
• The research gap: Highlight the existing gaps, limitations, or unresolved aspects within the current body of literature related to the research problem.
• The research relevance: Explain why the chosen research problem and its subsequent investigation through a literature review are significant and relevant in your academic field.
• The literature review method: If applicable, describe the methodology employed in your literature review, especially if it is a systematic review or follows a specific research framework.
• The main findings or insights of the literature review: Summarize the key discoveries, insights, or trends that have emerged from your comprehensive review of the literature.
• The main argument of the literature review: Conclude the introduction by outlining the primary argument or statement that your literature review will substantiate, linking it to the research problem and relevance you’ve established.
• Preview of the literature review’s structure: Offer a glimpse into the organization of the literature review paper, acting as a guide for the reader. This overview outlines the subsequent sections of the paper and provides an understanding of what to anticipate.

By addressing these elements, your introduction will provide a clear and structured overview of what readers can expect in your literature review paper.

Regular literature review section in an academic article or essay

Most academic articles or essays incorporate regular literature review sections, often placed after the introduction. These sections serve to establish a scholarly basis for the research or discussion within the paper.

In a standard 8000-word journal article, the literature review section typically spans between 750 and 1250 words. The first few sentences or the first paragraph within this section often serve as an introduction. It should encompass:

• An introduction to the topic: When delving into the academic literature on a specific topic, it’s important to provide a smooth transition that aids the reader in comprehending why certain aspects will be discussed within your literature review.
• The core argument: While literature review sections primarily synthesize the work of other scholars, they should consistently connect to your central argument. This central argument serves as the crux of your message or the key takeaway you want your readers to retain. By positioning it at the outset of the literature review section and systematically substantiating it with evidence, you not only enhance reader comprehension but also elevate overall readability. This primary argument can typically be distilled into 1-2 succinct sentences.

In some cases, you might include:

• Methodology: Details about the methodology used, but only if your literature review employed a specialized method. If your approach involved a broader overview without a systematic methodology, you can omit this section, thereby conserving word count.

By addressing these elements, your introduction will effectively integrate your literature review into the broader context of your academic paper or essay. This will, in turn, assist your reader in seamlessly following your overarching line of argumentation.

Introduction to a literature review chapter in thesis or dissertation

The literature review typically constitutes a distinct chapter within a thesis or dissertation. Often, it is Chapter 2 of a thesis or dissertation.

Some students choose to incorporate a brief introductory section at the beginning of each chapter, including the literature review chapter. Alternatively, others opt to seamlessly integrate the introduction into the initial sentences of the literature review itself. Both approaches are acceptable, provided that you incorporate the following elements:

• Purpose of the literature review and its relevance to the thesis/dissertation research: Explain the broader objectives of the literature review within the context of your research and how it contributes to your thesis or dissertation. Essentially, you’re telling the reader why this literature review is important and how it fits into the larger scope of your academic work.
• Primary argument: Succinctly communicate what you aim to prove, explain, or explore through the review of existing literature. This statement helps guide the reader’s understanding of the review’s purpose and what to expect from it.
• Preview of the literature review’s content: Provide a brief overview of the topics or themes that your literature review will cover. It’s like a roadmap for the reader, outlining the main areas of focus within the review. This preview can help the reader anticipate the structure and organization of your literature review.
• Methodology: If your literature review involved a specific research method, such as a systematic review or meta-analysis, you should briefly describe that methodology. However, this is not always necessary, especially if your literature review is more of a narrative synthesis without a distinct research method.

By addressing these elements, your introduction will empower your literature review to play a pivotal role in your thesis or dissertation research. It will accomplish this by integrating your research into the broader academic literature and providing a solid theoretical foundation for your work.

Comprehending the art of crafting your own literature review introduction becomes significantly more accessible when you have concrete examples to examine. Here, you will find several examples that meet, or in most cases, adhere to the criteria described earlier.

Example 1: An effective introduction for an academic literature review paper

To begin, let’s delve into the introduction of an academic literature review paper. We will examine the paper “How does culture influence innovation? A systematic literature review”, which was published in 2018 in the journal Management Decision.

The entire introduction spans 611 words and is divided into five paragraphs. In this introduction, the authors accomplish the following:

• In the first paragraph, the authors introduce the broader topic of the literature review, which focuses on innovation and its significance in the context of economic competition. They underscore the importance of this topic, highlighting its relevance for both researchers and policymakers.
• In the second paragraph, the authors narrow down their focus to emphasize the specific role of culture in relation to innovation.
• In the third paragraph, the authors identify research gaps, noting that existing studies are often fragmented and disconnected. They then emphasize the value of conducting a systematic literature review to enhance our understanding of the topic.
• In the fourth paragraph, the authors introduce their specific objectives and explain how their insights can benefit other researchers and business practitioners.
• In the fifth and final paragraph, the authors provide an overview of the paper’s organization and structure.

In summary, this introduction stands as a solid example. While the authors deviate from previewing their key findings (which is a common practice at least in the social sciences), they do effectively cover all the other previously mentioned points.

Example 2: An effective introduction to a literature review section in an academic paper

The second example represents a typical academic paper, encompassing not only a literature review section but also empirical data, a case study, and other elements. We will closely examine the introduction to the literature review section in the paper “The environmentalism of the subalterns: a case study of environmental activism in Eastern Kurdistan/Rojhelat”, which was published in 2021 in the journal Local Environment.

The paper begins with a general introduction and then proceeds to the literature review, designated by the authors as their conceptual framework. Of particular interest is the first paragraph of this conceptual framework, comprising 142 words across five sentences:

“ A peripheral and marginalised nationality within a multinational though-Persian dominated Iranian society, the Kurdish people of Iranian Kurdistan (a region referred by the Kurds as Rojhelat/Eastern Kurdi-stan) have since the early twentieth century been subject to multifaceted and systematic discriminatory and exclusionary state policy in Iran. This condition has left a population of 12–15 million Kurds in Iran suffering from structural inequalities, disenfranchisement and deprivation. Mismanagement of Kurdistan’s natural resources and the degradation of its natural environmental are among examples of this disenfranchisement. As asserted by Julian Agyeman (2005), structural inequalities that sustain the domination of political and economic elites often simultaneously result in environmental degradation, injustice and discrimination against subaltern communities. This study argues that the environmental struggle in Eastern Kurdistan can be asserted as a (sub)element of the Kurdish liberation movement in Iran. Conceptually this research is inspired by and has been conducted through the lens of ‘subalternity’ ” ( Hassaniyan, 2021, p. 931 ).

In this first paragraph, the author is doing the following:

• The author contextualises the research
• The author links the research focus to the international literature on structural inequalities
• The author clearly presents the argument of the research
• The author clarifies how the research is inspired by and uses the concept of ‘subalternity’.

Thus, the author successfully introduces the literature review, from which point onward it dives into the main concept (‘subalternity’) of the research, and reviews the literature on socio-economic justice and environmental degradation.

While introductions to a literature review section aren’t always required to offer the same level of study context detail as demonstrated here, this introduction serves as a commendable model for orienting the reader within the literature review. It effectively underscores the literature review’s significance within the context of the study being conducted.

Examples 3-5: Effective introductions to literature review chapters

The introduction to a literature review chapter can vary in length, depending largely on the overall length of the literature review chapter itself. For example, a master’s thesis typically features a more concise literature review, thus necessitating a shorter introduction. In contrast, a Ph.D. thesis, with its more extensive literature review, often includes a more detailed introduction.

Numerous universities offer online repositories where you can access theses and dissertations from previous years, serving as valuable sources of reference. Many of these repositories, however, may require you to log in through your university account. Nevertheless, a few open-access repositories are accessible to anyone, such as the one by the University of Manchester . It’s important to note though that copyright restrictions apply to these resources, just as they would with published papers.

Master’s thesis literature review introduction

The first example is “Benchmarking Asymmetrical Heating Models of Spider Pulsar Companions” by P. Sun, a master’s thesis completed at the University of Manchester on January 9, 2024. The author, P. Sun, introduces the literature review chapter very briefly but effectively:

PhD thesis literature review chapter introduction

The second example is Deep Learning on Semi-Structured Data and its Applications to Video-Game AI, Woof, W. (Author). 31 Dec 2020, a PhD thesis completed at the University of Manchester . In Chapter 2, the author offers a comprehensive introduction to the topic in four paragraphs, with the final paragraph serving as an overview of the chapter’s structure:

PhD thesis literature review introduction

The last example is the doctoral thesis Metacognitive strategies and beliefs: Child correlates and early experiences Chan, K. Y. M. (Author). 31 Dec 2020 . The author clearly conducted a systematic literature review, commencing the review section with a discussion of the methodology and approach employed in locating and analyzing the selected records.

Having absorbed all of this information, let’s recap the essential steps and offer a succinct guide on how to proceed with creating your literature review introduction:

• Contextualize your review : Begin by clearly identifying the academic context in which your literature review resides and determining the necessary information to include.
• Outline your structure : Develop a structured outline for your literature review, highlighting the essential information you plan to incorporate in your introduction.
• Literature review process : Conduct a rigorous literature review, reviewing and analyzing relevant sources.
• Summarize and abstract : After completing the review, synthesize the findings and abstract key insights, trends, and knowledge gaps from the literature.
• Craft the introduction : Write your literature review introduction with meticulous attention to the seamless integration of your review into the larger context of your work. Ensure that your introduction effectively elucidates your rationale for the chosen review topics and the underlying reasons guiding your selection.

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Home » Education » Difference Between Introduction and Literature Review

Difference Between Introduction and Literature Review

Main difference – introduction vs literature review.

Although introduction and literature review are found towards the beginning of a text, there is a difference between them in terms of their function and purpose. The main difference between introduction and literature review is their purpose; the purpose of an introduction is to briefly introduce the text to the readers whereas the purpose of a literature review is to review and critically evaluate the existing research on a selected research area. 

In this article, we will be discussing,

     1. What is an Introduction?           – Definition, Features, Characteristics

     2. What is a Literature Review?           – Definition, Features, Characteristics

What is an Introduction

An introduction is the first part of an article, paper, book or a study that briefly introduces what will be found in the following sections. An introduction basically introduces the text to the readers. It may contain various types of information, but given below some common elements that can be found in the introduction section.

• Background/context to the paper
• Outline of key issues
• Thesis statement
• Aims and purpose of the paper
• Definition of terms and concepts

Note that some introductions may not have all these elements. For example, an introduction to a short essay will only have several lines. Introductions can be found in nonfiction books, essays, research articles, thesis, etc. There can be slight variations in these various genres, but all these introductions will provide a basic outline of the whole text. 

Introduction of a thesis or dissertation will describe the background of the research, your rationale for the thesis topic, what exactly are you trying to answer, and the importance of your research.

What is a Literature Review

A literature review, which is written at the start of a research study, is essential to a research project. A literature review is an evaluation of the existing research material on a selected research area. This involves reading the major published work (both printed and online work) in a chosen research area and reviewing and critically evaluating them. A literature review should show the researcher’s awareness and insight of contrasting arguments, theories, and approaches. According to Caulley (1992) a good literature review should do the following:

• Compare and contrast different researchers’ views
• Identify areas in which researchers are in disagreement
• Group researchers who have similar conclusions
• Criticize the research methodology
• Highlight exemplary studies
• Highlight gaps in research
• Indicate the connection between your study and previous studies
• Indicate how your study will contribute to the literature in general
• Conclude by summarizing what the literature says

Literature reviews help researchers to evaluate the existing literature, to identify a gap in the research area, to place their study in the existing research and identify future research.

Introduction is at the beginning of a text.

Literature Review is located after the introduction or background.

Introduction introduces the main text to the readers.

Literature Review critically evaluates the existing research on the selected research area and identifies the research gap.

Introduction will have information such as background/context to the paper, outline of key issues, thesis statement, aims, and purpose of the paper and definition of terms and concepts. 

Literature Review will have summaries, reviews, critical evaluations, and comparisons of selected research studies.

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• UConn Library
• Literature Review: The What, Why and How-to Guide
• Introduction

Literature Review: The What, Why and How-to Guide — Introduction

• Getting Started
• How to Pick a Topic
• Strategies to Find Sources
• Evaluating Sources & Lit. Reviews
• Tips for Writing Literature Reviews
• Writing Literature Review: Useful Sites
• Citation Resources
• Other Academic Writings

What are Literature Reviews?

So, what is a literature review? "A literature review is an account of what has been published on a topic by accredited scholars and researchers. In writing the literature review, your purpose is to convey to your reader what knowledge and ideas have been established on a topic, and what their strengths and weaknesses are. As a piece of writing, the literature review must be defined by a guiding concept (e.g., your research objective, the problem or issue you are discussing, or your argumentative thesis). It is not just a descriptive list of the material available, or a set of summaries." Taylor, D.  The literature review: A few tips on conducting it . University of Toronto Health Sciences Writing Centre.

Goals of Literature Reviews

What are the goals of creating a Literature Review?  A literature could be written to accomplish different aims:

• To develop a theory or evaluate an existing theory
• To summarize the historical or existing state of a research topic
• Identify a problem in a field of research 

Baumeister, R. F., & Leary, M. R. (1997). Writing narrative literature reviews .  Review of General Psychology , 1 (3), 311-320.

What kinds of sources require a Literature Review?

• A research paper assigned in a course
• A thesis or dissertation
• A grant proposal
• An article intended for publication in a journal

All these instances require you to collect what has been written about your research topic so that you can demonstrate how your own research sheds new light on the topic.

Types of Literature Reviews

What kinds of literature reviews are written?

Narrative review: The purpose of this type of review is to describe the current state of the research on a specific topic/research and to offer a critical analysis of the literature reviewed. Studies are grouped by research/theoretical categories, and themes and trends, strengths and weakness, and gaps are identified. The review ends with a conclusion section which summarizes the findings regarding the state of the research of the specific study, the gaps identify and if applicable, explains how the author's research will address gaps identify in the review and expand the knowledge on the topic reviewed.

• Example : Predictors and Outcomes of U.S. Quality Maternity Leave: A Review and Conceptual Framework:  10.1177/08948453211037398  

Systematic review : "The authors of a systematic review use a specific procedure to search the research literature, select the studies to include in their review, and critically evaluate the studies they find." (p. 139). Nelson, L. K. (2013). Research in Communication Sciences and Disorders . Plural Publishing.

• Example : The effect of leave policies on increasing fertility: a systematic review:  10.1057/s41599-022-01270-w

Meta-analysis : "Meta-analysis is a method of reviewing research findings in a quantitative fashion by transforming the data from individual studies into what is called an effect size and then pooling and analyzing this information. The basic goal in meta-analysis is to explain why different outcomes have occurred in different studies." (p. 197). Roberts, M. C., & Ilardi, S. S. (2003). Handbook of Research Methods in Clinical Psychology . Blackwell Publishing.

• Example : Employment Instability and Fertility in Europe: A Meta-Analysis:  10.1215/00703370-9164737

Meta-synthesis : "Qualitative meta-synthesis is a type of qualitative study that uses as data the findings from other qualitative studies linked by the same or related topic." (p.312). Zimmer, L. (2006). Qualitative meta-synthesis: A question of dialoguing with texts .  Journal of Advanced Nursing , 53 (3), 311-318.

• Example : Women’s perspectives on career successes and barriers: A qualitative meta-synthesis:  10.1177/05390184221113735

Literature Reviews in the Health Sciences

• UConn Health subject guide on systematic reviews Explanation of the different review types used in health sciences literature as well as tools to help you find the right review type
• << Previous: Getting Started
• Next: How to Pick a Topic >>
• Last Updated: Sep 21, 2022 2:16 PM
• URL: https://guides.lib.uconn.edu/literaturereview

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What is a Literature Review? How to Write It (with Examples)

A literature review is a critical analysis and synthesis of existing research on a particular topic. It provides an overview of the current state of knowledge, identifies gaps, and highlights key findings in the literature. 1 The purpose of a literature review is to situate your own research within the context of existing scholarship, demonstrating your understanding of the topic and showing how your work contributes to the ongoing conversation in the field. Learning how to write a literature review is a critical tool for successful research. Your ability to summarize and synthesize prior research pertaining to a certain topic demonstrates your grasp on the topic of study, and assists in the learning process. 

Table of Contents

• What is the purpose of literature review? 
• a. Habitat Loss and Species Extinction: 
• b. Range Shifts and Phenological Changes: 
• c. Ocean Acidification and Coral Reefs: 
• d. Adaptive Strategies and Conservation Efforts: 

How to write a good literature review 

• Choose a Topic and Define the Research Question: 
• Decide on the Scope of Your Review: 
• Select Databases for Searches: 
• Conduct Searches and Keep Track: 
• Review the Literature: 
• Organize and Write Your Literature Review: 
• How to write a literature review faster with Paperpal? 
• Frequently asked questions 

What is a literature review?

A well-conducted literature review demonstrates the researcher’s familiarity with the existing literature, establishes the context for their own research, and contributes to scholarly conversations on the topic. One of the purposes of a literature review is also to help researchers avoid duplicating previous work and ensure that their research is informed by and builds upon the existing body of knowledge.

What is the purpose of literature review?

A literature review serves several important purposes within academic and research contexts. Here are some key objectives and functions of a literature review: 2  

1. Contextualizing the Research Problem: The literature review provides a background and context for the research problem under investigation. It helps to situate the study within the existing body of knowledge. 

2. Identifying Gaps in Knowledge: By identifying gaps, contradictions, or areas requiring further research, the researcher can shape the research question and justify the significance of the study. This is crucial for ensuring that the new research contributes something novel to the field. 

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3. Understanding Theoretical and Conceptual Frameworks: Literature reviews help researchers gain an understanding of the theoretical and conceptual frameworks used in previous studies. This aids in the development of a theoretical framework for the current research. 

4. Providing Methodological Insights: Another purpose of literature reviews is that it allows researchers to learn about the methodologies employed in previous studies. This can help in choosing appropriate research methods for the current study and avoiding pitfalls that others may have encountered. 

5. Establishing Credibility: A well-conducted literature review demonstrates the researcher’s familiarity with existing scholarship, establishing their credibility and expertise in the field. It also helps in building a solid foundation for the new research. 

6. Informing Hypotheses or Research Questions: The literature review guides the formulation of hypotheses or research questions by highlighting relevant findings and areas of uncertainty in existing literature. 

Literature review example

Let’s delve deeper with a literature review example: Let’s say your literature review is about the impact of climate change on biodiversity. You might format your literature review into sections such as the effects of climate change on habitat loss and species extinction, phenological changes, and marine biodiversity. Each section would then summarize and analyze relevant studies in those areas, highlighting key findings and identifying gaps in the research. The review would conclude by emphasizing the need for further research on specific aspects of the relationship between climate change and biodiversity. The following literature review template provides a glimpse into the recommended literature review structure and content, demonstrating how research findings are organized around specific themes within a broader topic. 

Literature Review on Climate Change Impacts on Biodiversity:

Climate change is a global phenomenon with far-reaching consequences, including significant impacts on biodiversity. This literature review synthesizes key findings from various studies: 

a. Habitat Loss and Species Extinction:

Climate change-induced alterations in temperature and precipitation patterns contribute to habitat loss, affecting numerous species (Thomas et al., 2004). The review discusses how these changes increase the risk of extinction, particularly for species with specific habitat requirements. 

b. Range Shifts and Phenological Changes:

Observations of range shifts and changes in the timing of biological events (phenology) are documented in response to changing climatic conditions (Parmesan & Yohe, 2003). These shifts affect ecosystems and may lead to mismatches between species and their resources. 

c. Ocean Acidification and Coral Reefs:

The review explores the impact of climate change on marine biodiversity, emphasizing ocean acidification’s threat to coral reefs (Hoegh-Guldberg et al., 2007). Changes in pH levels negatively affect coral calcification, disrupting the delicate balance of marine ecosystems. 

d. Adaptive Strategies and Conservation Efforts:

Recognizing the urgency of the situation, the literature review discusses various adaptive strategies adopted by species and conservation efforts aimed at mitigating the impacts of climate change on biodiversity (Hannah et al., 2007). It emphasizes the importance of interdisciplinary approaches for effective conservation planning. 

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Writing a literature review involves summarizing and synthesizing existing research on a particular topic. A good literature review format should include the following elements. 

Introduction: The introduction sets the stage for your literature review, providing context and introducing the main focus of your review. 

• Opening Statement: Begin with a general statement about the broader topic and its significance in the field. 
• Scope and Purpose: Clearly define the scope of your literature review. Explain the specific research question or objective you aim to address. 
• Organizational Framework: Briefly outline the structure of your literature review, indicating how you will categorize and discuss the existing research. 
• Significance of the Study: Highlight why your literature review is important and how it contributes to the understanding of the chosen topic. 
• Thesis Statement: Conclude the introduction with a concise thesis statement that outlines the main argument or perspective you will develop in the body of the literature review. 

Body: The body of the literature review is where you provide a comprehensive analysis of existing literature, grouping studies based on themes, methodologies, or other relevant criteria. 

• Organize by Theme or Concept: Group studies that share common themes, concepts, or methodologies. Discuss each theme or concept in detail, summarizing key findings and identifying gaps or areas of disagreement. 
• Critical Analysis: Evaluate the strengths and weaknesses of each study. Discuss the methodologies used, the quality of evidence, and the overall contribution of each work to the understanding of the topic. 
• Synthesis of Findings: Synthesize the information from different studies to highlight trends, patterns, or areas of consensus in the literature. 
• Identification of Gaps: Discuss any gaps or limitations in the existing research and explain how your review contributes to filling these gaps. 
• Transition between Sections: Provide smooth transitions between different themes or concepts to maintain the flow of your literature review. 

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Conclusion: The conclusion of your literature review should summarize the main findings, highlight the contributions of the review, and suggest avenues for future research. 

• Summary of Key Findings: Recap the main findings from the literature and restate how they contribute to your research question or objective. 
• Contributions to the Field: Discuss the overall contribution of your literature review to the existing knowledge in the field. 
• Implications and Applications: Explore the practical implications of the findings and suggest how they might impact future research or practice. 
• Recommendations for Future Research: Identify areas that require further investigation and propose potential directions for future research in the field. 
• Final Thoughts: Conclude with a final reflection on the importance of your literature review and its relevance to the broader academic community. 

Conducting a literature review

Conducting a literature review is an essential step in research that involves reviewing and analyzing existing literature on a specific topic. It’s important to know how to do a literature review effectively, so here are the steps to follow: 1  

Choose a Topic and Define the Research Question:

• Select a topic that is relevant to your field of study. 
• Clearly define your research question or objective. Determine what specific aspect of the topic do you want to explore? 

Decide on the Scope of Your Review:

• Determine the timeframe for your literature review. Are you focusing on recent developments, or do you want a historical overview? 
• Consider the geographical scope. Is your review global, or are you focusing on a specific region? 
• Define the inclusion and exclusion criteria. What types of sources will you include? Are there specific types of studies or publications you will exclude? 

Select Databases for Searches:

• Identify relevant databases for your field. Examples include PubMed, IEEE Xplore, Scopus, Web of Science, and Google Scholar. 
• Consider searching in library catalogs, institutional repositories, and specialized databases related to your topic. 

Conduct Searches and Keep Track:

• Develop a systematic search strategy using keywords, Boolean operators (AND, OR, NOT), and other search techniques. 
• Record and document your search strategy for transparency and replicability. 
• Keep track of the articles, including publication details, abstracts, and links. Use citation management tools like EndNote, Zotero, or Mendeley to organize your references. 

Review the Literature:

• Evaluate the relevance and quality of each source. Consider the methodology, sample size, and results of studies. 
• Organize the literature by themes or key concepts. Identify patterns, trends, and gaps in the existing research. 
• Summarize key findings and arguments from each source. Compare and contrast different perspectives. 
• Identify areas where there is a consensus in the literature and where there are conflicting opinions. 
• Provide critical analysis and synthesis of the literature. What are the strengths and weaknesses of existing research? 

Organize and Write Your Literature Review:

• Literature review outline should be based on themes, chronological order, or methodological approaches. 
• Write a clear and coherent narrative that synthesizes the information gathered. 
• Use proper citations for each source and ensure consistency in your citation style (APA, MLA, Chicago, etc.). 
• Conclude your literature review by summarizing key findings, identifying gaps, and suggesting areas for future research. 

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How to write a literature review faster with Paperpal?

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Here’s how to use the Research feature:  

• Ask a question: Get started with a new document on paperpal.com. Click on the “Research” feature and type your question in plain English. Paperpal will scour over 250 million research articles, including conference papers and preprints, to provide you with accurate insights and citations. 
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• Cite with Confidence: Paperpal makes it easy to incorporate relevant citations and references into your writing, ensuring your arguments are well-supported by credible sources. This translates to a polished, well-researched literature review. 

The literature review sample and detailed advice on writing and conducting a review will help you produce a well-structured report. But remember that a good literature review is an ongoing process, and it may be necessary to revisit and update it as your research progresses. By combining effortless research with an easy citation process, Paperpal Research streamlines the literature review process and empowers you to write faster and with more confidence. Try Paperpal Research now and see for yourself.  

Frequently asked questions

A literature review is a critical and comprehensive analysis of existing literature (published and unpublished works) on a specific topic or research question and provides a synthesis of the current state of knowledge in a particular field. A well-conducted literature review is crucial for researchers to build upon existing knowledge, avoid duplication of efforts, and contribute to the advancement of their field. It also helps researchers situate their work within a broader context and facilitates the development of a sound theoretical and conceptual framework for their studies.

Literature review is a crucial component of research writing, providing a solid background for a research paper’s investigation. The aim is to keep professionals up to date by providing an understanding of ongoing developments within a specific field, including research methods, and experimental techniques used in that field, and present that knowledge in the form of a written report. Also, the depth and breadth of the literature review emphasizes the credibility of the scholar in his or her field.  

Before writing a literature review, it’s essential to undertake several preparatory steps to ensure that your review is well-researched, organized, and focused. This includes choosing a topic of general interest to you and doing exploratory research on that topic, writing an annotated bibliography, and noting major points, especially those that relate to the position you have taken on the topic. 

Literature reviews and academic research papers are essential components of scholarly work but serve different purposes within the academic realm. 3 A literature review aims to provide a foundation for understanding the current state of research on a particular topic, identify gaps or controversies, and lay the groundwork for future research. Therefore, it draws heavily from existing academic sources, including books, journal articles, and other scholarly publications. In contrast, an academic research paper aims to present new knowledge, contribute to the academic discourse, and advance the understanding of a specific research question. Therefore, it involves a mix of existing literature (in the introduction and literature review sections) and original data or findings obtained through research methods. 

Literature reviews are essential components of academic and research papers, and various strategies can be employed to conduct them effectively. If you want to know how to write a literature review for a research paper, here are four common approaches that are often used by researchers.  Chronological Review: This strategy involves organizing the literature based on the chronological order of publication. It helps to trace the development of a topic over time, showing how ideas, theories, and research have evolved.  Thematic Review: Thematic reviews focus on identifying and analyzing themes or topics that cut across different studies. Instead of organizing the literature chronologically, it is grouped by key themes or concepts, allowing for a comprehensive exploration of various aspects of the topic.  Methodological Review: This strategy involves organizing the literature based on the research methods employed in different studies. It helps to highlight the strengths and weaknesses of various methodologies and allows the reader to evaluate the reliability and validity of the research findings.  Theoretical Review: A theoretical review examines the literature based on the theoretical frameworks used in different studies. This approach helps to identify the key theories that have been applied to the topic and assess their contributions to the understanding of the subject.  It’s important to note that these strategies are not mutually exclusive, and a literature review may combine elements of more than one approach. The choice of strategy depends on the research question, the nature of the literature available, and the goals of the review. Additionally, other strategies, such as integrative reviews or systematic reviews, may be employed depending on the specific requirements of the research.

The literature review format can vary depending on the specific publication guidelines. However, there are some common elements and structures that are often followed. Here is a general guideline for the format of a literature review:  Introduction:   Provide an overview of the topic.  Define the scope and purpose of the literature review.  State the research question or objective.  Body:   Organize the literature by themes, concepts, or chronology.  Critically analyze and evaluate each source.  Discuss the strengths and weaknesses of the studies.  Highlight any methodological limitations or biases.  Identify patterns, connections, or contradictions in the existing research.  Conclusion:   Summarize the key points discussed in the literature review.  Highlight the research gap.  Address the research question or objective stated in the introduction.  Highlight the contributions of the review and suggest directions for future research.

Both annotated bibliographies and literature reviews involve the examination of scholarly sources. While annotated bibliographies focus on individual sources with brief annotations, literature reviews provide a more in-depth, integrated, and comprehensive analysis of existing literature on a specific topic. The key differences are as follows: 

References 

• Denney, A. S., & Tewksbury, R. (2013). How to write a literature review.  Journal of criminal justice education ,  24 (2), 218-234. 
• Pan, M. L. (2016).  Preparing literature reviews: Qualitative and quantitative approaches . Taylor & Francis. 
• Cantero, C. (2019). How to write a literature review.  San José State University Writing Center . 

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Introductions and Literature Reviews

• Author By Troy Mikanovich
• Publication date December 16, 2022
• Categories: Academic Publication , Research Writing
• Categories: academic journal , CARS , introduction , literature review , research , research question

Writing literature reviews is one of the trickiest things you’ll have to do in graduate school.  It is even more tricky because a lot of professors will want you to do things that are pedagogically valuable but so tailored to the specific class they are teaching that it can be hard to generalize the lessons you are meant to take away.

This page is meant to be a general overview to the goals and purposes of introductions and literature reviews (or an introduction that contains a literature review–we’ll talk about that), so even if it doesn’t exactly match what you have been asked to do in an assignment, I hope it’ll be helpful.

What is the difference between an introduction and a literature review?

As of writing this, the year is 2022 and words mean nothing. Rather than getting caught up on what these things are in some kind of objective sense, let’s look at what they are supposed to do.

The introduction and the literature review of your paper have the same job. Both are supposed to justify the question(s) you are asking about your topic and to demonstrate to your audience that the thing you are writing about is interesting and of some importance.  However, while they have the same job, they do it in two different ways.

An introduction should demonstrate that there is some broader real-world significance to the thing that you are writing about. You can do this by establishing a problem or a puzzle or by giving some background information on your topic to show why it is important.  Here’s an example from Brian E. Bride’s “Prevalence of Secondary Traumatic Stress among Social Workers” (2007, link below), where he begins by establishing a problem:

“ In the United States, the lifetime prevalence of exposure to traumatic events ranges from 40 percent to 81 percent, with 60.7 percent of men and 51.2 percent of women having been exposed to one or more traumas and 19.7 percent of men and 11.4 percent of women reporting exposure to three or more such events (Breslau, Davis, Peter-son, & Schultz, 1997; Kessler, Sonnega, Bromet, & Nelson, 1995; Stein, walker, Hazen, & Forde, 1997). Although exposure to traumatic events is high in the general population, it is even higher in subpopulations to whom social workers are likely to provide services…

Although not exhaustive of the populations with whom social workers practice, these examples illustrate that social workers face a high rate of professional contact with traumatized people. Social workers are increasingly being called on to assist survivors of childhood abuse, domestic violence, violent crime, disasters, and war and terrorism. It has become increasingly apparent that the psychological effects of traumatic events extend beyond those directly affected.”

So, Bride (2007) starts with a broad problem (lots of people with exposure to traumatic events) and narrows it to a more specific problem (social workers who work with those people are exposed to secondary trauma as they assist them) .

A literature review should demonstrate that there is some academic significance to the thing you are writing about. You can do this by establishing a scholarly problem (i.e. a “research gap”) and by demonstrating that the state of the existing scholarship on your topic needs to develop in a particular way.

As Bride (2007) transitions to talking about the scholarship on the topic of social workers and secondary trauma, he establishes what scholarship has done and identifies what it has not done .

“Figley (1999) defined secondary traumatic stress as “the natural, consequent behaviors and emotions resulting from knowledge about a traumatizing event experienced by a significant other. It is the stress resulting from helping or wanting to help a traumatized or suffering person” (p. 10). Chrestman (1999) noted that secondary traumatization includes symptoms parallel to those observed in people di-rectly exposed to trauma such as intrusive imagery related to clients’ traumatic disclosures (Courtois, 1988; Danieli, 1988; Herman, 1992; McCann & Pearlman, 1990); avoidant responses (Courtois; Haley, 1974); and physiological arousal (Figley, 1995; McCann & Pearlman, 1990). Thus, STS is a syndrome of symptoms identical to those of PTSD, the characteristic symptoms of which are intrusion, avoidance, and arousal (Figley, 1999)…

Collectively, these studies have provided empirical evidence that individuals who provide services to traumatized populations are at risk of experiencing symptoms of traumatic stress (Bride). However, the extant literature fails to document the prevalence of individual STS symptoms and the extent to which diagnostic criteria for PTSD are met as a result of work with traumatized populations.”

Taken together, Bride (2007) justifies its existence–the research that the author has undertaken in order to read the article that you are now reading–like this:

Broad real world background: Lots of people are suffering from traumatic stress.

Narrowed real world background: People who have suffered traumatic experiences often work with social workers.

Real world problem: Many social workers may through their work suffer from secondary exposure to traumatic experiences.

Broad academic background: There has been a lot of research on secondary traumatic stress

Narrowed academic background: Particularly, this research has shown that social workers are at risk of experiencing symptoms of secondary traumatic stress.

Academic problem/gap : We don’t know how prevalent individual symptoms of secondary traumatic stress are.

Introductions, then, give you space to explain why you are writing about the thing you are writing about, and literature reviews are where you explain what prior scholarship has said about the topic and what the consequences of that prior scholarship are. In an introduction you are writing about the topic; in a literature review you are writing about people writing about the topic.

So does a literature review need to be a separate section from an introduction? Or is a literature review part of an introduction?

It depends on your field, tbh. And on the expectations of the assignment/journal/outlet that you are writing for.

For instance, in the above example (Bride, 2007) the literature review is a part of the introduction. Here’s that paper and some other examples of other places where this is the case. Notice that they do not differentiate between an introductory section and a distinct “Literature Review” as they outline their topic/questions before describing their methodology:

Bride, B. E. (2007) Prevalence of secondary traumatic stress among social workers.  Social Work, 52 (1), 63-70. https://doi.org/10.1093/sw/52.1.63

Wei, X., Teng, X., Bai, J., & Ren, F. (2022). Intergenerational transmission of depression during adolescence: The mediating roles of hostile attribution bias, empathetic concern, and social self-concept.  The Journal of Psychology, 157 (1), 13-31. https://doi.org/10.1080/00223980.2022.2134276

Stephens, R., Dowber, H., Barrie, A., Sannida, A., & Atkins, K. 2022) Effect of swearing on strength: Disinhibition as a potential mediator.  Quarterly Journal of Experimental Psychology . Advance online publication. https://doi.org/10.1177/17470218221082657

However, plenty of other articles have distinct “Literature Review” sections separate from their introductions. The first two examples name it as such, while the third organizes its literature review with thematic sub-sections:

Schraedley, M.K., & Dougherty, D.S. (2021). Creating and disrupting othering during policymaking in a polarized context.  Journal of Communication, 72 (1), 111-140. https://doi.org/10.1093/joc/jqab042

Gil de Zúñiga, H., Cheng, Z., & González-González, P. (2022). Effects of the news finds me perception on algorithmic news attitudes and social media political homophily. Journal of Communication, 72 (5), 578-591. https://doi.org/10.1093/joc/jqac025

Brandão, T., Brites, R., Hipólito, J., & Nunes O. (2022) Attachment orientations and family functioning: The mediating role of emotion regulation. The Journal of Psychology , 157 (1), 1-12. https://doi.org/10.1080/00223980.2022.2128284

Whether you separate your literature review into its own distinct section is mostly a function of what you’ve been asked to do (if you are writing for a class) or what the conventions and constraints are of your field.

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Writing a Literature Review

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A literature review is a document or section of a document that collects key sources on a topic and discusses those sources in conversation with each other (also called synthesis ). The lit review is an important genre in many disciplines, not just literature (i.e., the study of works of literature such as novels and plays). When we say “literature review” or refer to “the literature,” we are talking about the research ( scholarship ) in a given field. You will often see the terms “the research,” “the scholarship,” and “the literature” used mostly interchangeably.

Where, when, and why would I write a lit review?

There are a number of different situations where you might write a literature review, each with slightly different expectations; different disciplines, too, have field-specific expectations for what a literature review is and does. For instance, in the humanities, authors might include more overt argumentation and interpretation of source material in their literature reviews, whereas in the sciences, authors are more likely to report study designs and results in their literature reviews; these differences reflect these disciplines’ purposes and conventions in scholarship. You should always look at examples from your own discipline and talk to professors or mentors in your field to be sure you understand your discipline’s conventions, for literature reviews as well as for any other genre.

A literature review can be a part of a research paper or scholarly article, usually falling after the introduction and before the research methods sections. In these cases, the lit review just needs to cover scholarship that is important to the issue you are writing about; sometimes it will also cover key sources that informed your research methodology.

Lit reviews can also be standalone pieces, either as assignments in a class or as publications. In a class, a lit review may be assigned to help students familiarize themselves with a topic and with scholarship in their field, get an idea of the other researchers working on the topic they’re interested in, find gaps in existing research in order to propose new projects, and/or develop a theoretical framework and methodology for later research. As a publication, a lit review usually is meant to help make other scholars’ lives easier by collecting and summarizing, synthesizing, and analyzing existing research on a topic. This can be especially helpful for students or scholars getting into a new research area, or for directing an entire community of scholars toward questions that have not yet been answered.

What are the parts of a lit review?

Most lit reviews use a basic introduction-body-conclusion structure; if your lit review is part of a larger paper, the introduction and conclusion pieces may be just a few sentences while you focus most of your attention on the body. If your lit review is a standalone piece, the introduction and conclusion take up more space and give you a place to discuss your goals, research methods, and conclusions separately from where you discuss the literature itself.

Introduction:

• An introductory paragraph that explains what your working topic and thesis is
• A forecast of key topics or texts that will appear in the review
• Potentially, a description of how you found sources and how you analyzed them for inclusion and discussion in the review (more often found in published, standalone literature reviews than in lit review sections in an article or research paper)
• Summarize and synthesize: Give an overview of the main points of each source and combine them into a coherent whole
• Analyze and interpret: Don’t just paraphrase other researchers – add your own interpretations where possible, discussing the significance of findings in relation to the literature as a whole
• Critically Evaluate: Mention the strengths and weaknesses of your sources
• Write in well-structured paragraphs: Use transition words and topic sentence to draw connections, comparisons, and contrasts.

Conclusion:

• Summarize the key findings you have taken from the literature and emphasize their significance
• Connect it back to your primary research question

How should I organize my lit review?

Lit reviews can take many different organizational patterns depending on what you are trying to accomplish with the review. Here are some examples:

• Chronological : The simplest approach is to trace the development of the topic over time, which helps familiarize the audience with the topic (for instance if you are introducing something that is not commonly known in your field). If you choose this strategy, be careful to avoid simply listing and summarizing sources in order. Try to analyze the patterns, turning points, and key debates that have shaped the direction of the field. Give your interpretation of how and why certain developments occurred (as mentioned previously, this may not be appropriate in your discipline — check with a teacher or mentor if you’re unsure).
• Thematic : If you have found some recurring central themes that you will continue working with throughout your piece, you can organize your literature review into subsections that address different aspects of the topic. For example, if you are reviewing literature about women and religion, key themes can include the role of women in churches and the religious attitude towards women.
• Qualitative versus quantitative research
• Empirical versus theoretical scholarship
• Divide the research by sociological, historical, or cultural sources
• Theoretical : In many humanities articles, the literature review is the foundation for the theoretical framework. You can use it to discuss various theories, models, and definitions of key concepts. You can argue for the relevance of a specific theoretical approach or combine various theorical concepts to create a framework for your research.

What are some strategies or tips I can use while writing my lit review?

Any lit review is only as good as the research it discusses; make sure your sources are well-chosen and your research is thorough. Don’t be afraid to do more research if you discover a new thread as you’re writing. More info on the research process is available in our "Conducting Research" resources .

As you’re doing your research, create an annotated bibliography ( see our page on the this type of document ). Much of the information used in an annotated bibliography can be used also in a literature review, so you’ll be not only partially drafting your lit review as you research, but also developing your sense of the larger conversation going on among scholars, professionals, and any other stakeholders in your topic.

Usually you will need to synthesize research rather than just summarizing it. This means drawing connections between sources to create a picture of the scholarly conversation on a topic over time. Many student writers struggle to synthesize because they feel they don’t have anything to add to the scholars they are citing; here are some strategies to help you:

• It often helps to remember that the point of these kinds of syntheses is to show your readers how you understand your research, to help them read the rest of your paper.
• Writing teachers often say synthesis is like hosting a dinner party: imagine all your sources are together in a room, discussing your topic. What are they saying to each other?
• Look at the in-text citations in each paragraph. Are you citing just one source for each paragraph? This usually indicates summary only. When you have multiple sources cited in a paragraph, you are more likely to be synthesizing them (not always, but often
• Read more about synthesis here.

The most interesting literature reviews are often written as arguments (again, as mentioned at the beginning of the page, this is discipline-specific and doesn’t work for all situations). Often, the literature review is where you can establish your research as filling a particular gap or as relevant in a particular way. You have some chance to do this in your introduction in an article, but the literature review section gives a more extended opportunity to establish the conversation in the way you would like your readers to see it. You can choose the intellectual lineage you would like to be part of and whose definitions matter most to your thinking (mostly humanities-specific, but this goes for sciences as well). In addressing these points, you argue for your place in the conversation, which tends to make the lit review more compelling than a simple reporting of other sources.

Literature reviews

What this guide covers, what is a literature review, literature review resources, types of literature reviews, what is the difference between a literature review and a systematic review, related information and guides, further help.

• Conducting your search
• Store and organise the literature
• Evaluate and critique the literature
• Different subject areas
• Find literature reviews

Reusing content from this guide

Attribute our work under a Creative Commons Attribution-NonCommercial 4.0 International License.

The literature review process involves a number of steps.

This guide focuses on:

• evaluating.

A literature review is a survey and critical analysis of what has been written on a particular topic, theory, question or method.

"In writing the literature review, the purpose is to explore what knowledge and ideas have been established on a topic, what approaches and viewpoints have been adopted, and what are their strengths and weaknesses."

Source: "Focus and frame". (2008). In Eriksson, P. & Kovalainen, A. Introducing Qualitative Methods: Qualitative methods in business research (pp. 44) . London: SAGE Publications Ltd. doi: 10.4135/9780857028044.

Get an overview on doing a literature review:

• Sage research methods online - Literature review methods map Information on the literature review methodology with links to further resources - the Project Planner, books, articles, videos and more.
• Ten simple rules for writing a literature review Gives 10 tips on how to approach and carry out a literature review. By Pautasso M (2013) Ten Simple Rules for Writing a Literature Review. PLoS Comput Biol9(7): e1003149.
• The literature review. In: Doing your undergraduate program This chapter looks at the purpose of literature reviews, how it is done, setting the boundaries of your search and more.

• More books on literature reviews A selection of literature review books available via UQ Library Search.

The type of literature review you do will depend on a variety of factors:

• Your discipline
• The purpose - undergraduate assessment, PHD thesis, journal article?
• Your lecturer or supervisor's requirements

Always follow the guidelines outlined by your lecturer or supervisor or consult the instructions for authors (for journal articles), when conducting your literature review.

• is an overview of the significant literature on a topic
• typically includes a critical analysis of each work included
• demonstrates the reviewers knowledge of the topic
• is a list of citations of research sources (books, journal articles, websites etc) on a topic
• includes a brief summary and analysis or evaluation of each citation = the annotation
• a critical assessment of all research studies on a particular research question
• has specific criteria for collecting and evaluating the literature
• includes a synthesis of the findings of the included studies
• This method developed by Griffith University's School of Environment bridges the gap between traditional narrative review methods and meta-analyses to enable students to produce results that are reliable, quantifiable and reproducible.

The requirements of narrative literature reviews are usually quite different than systematic reviews . However, you may be required to adopt some of the characteristics of a systematic approach when doing your literature review. Check the guidelines or criteria that have been set by your supervisor so you know what is expected of you.

Characteristics of reviews

• Meeting the review family: Exploring review types and associated information retrieval requirements This article defines different review types and discusses appropriate search methods for each type.
• Writing literature reviews - Student Support Student Support provides information on how to write effective literature reviews.
• Writing skills Learn strategies for good writing from the Graduate School.
• Systematic reviews An overview of systematic reviews and resources to support producing one.
• Subject guides See recommended resources in different subject areas.
• Grey literature Find literature that is not available in traditional channels of publishing and distribution.
• How to find guides Techniques and resources to find specific information formats.

Contact the Librarian team .

Phone: + 617 334 64312 during opening hours

Email: [email protected]

• Next: Conducting your search >>
• Last Updated: Dec 15, 2023 12:09 PM
• URL: https://guides.library.uq.edu.au/research-techniques/literature-reviews

What Is A Literature Review?

A plain-language explainer (with examples).

By:  Derek Jansen (MBA) & Kerryn Warren (PhD) | June 2020 (Updated May 2023)

If you’re faced with writing a dissertation or thesis, chances are you’ve encountered the term “literature review” . If you’re on this page, you’re probably not 100% what the literature review is all about. The good news is that you’ve come to the right place.

Literature Review 101

• What (exactly) is a literature review
• What’s the purpose of the literature review chapter
• How to find high-quality resources
• How to structure your literature review chapter
• Example of an actual literature review

What is a literature review?

The word “literature review” can refer to two related things that are part of the broader literature review process. The first is the task of  reviewing the literature  – i.e. sourcing and reading through the existing research relating to your research topic. The second is the  actual chapter  that you write up in your dissertation, thesis or research project. Let’s look at each of them:

Reviewing the literature

The first step of any literature review is to hunt down and  read through the existing research  that’s relevant to your research topic. To do this, you’ll use a combination of tools (we’ll discuss some of these later) to find journal articles, books, ebooks, research reports, dissertations, theses and any other credible sources of information that relate to your topic. You’ll then  summarise and catalogue these  for easy reference when you write up your literature review chapter. 

The literature review chapter

The second step of the literature review is to write the actual literature review chapter (this is usually the second chapter in a typical dissertation or thesis structure ). At the simplest level, the literature review chapter is an  overview of the key literature  that’s relevant to your research topic. This chapter should provide a smooth-flowing discussion of what research has already been done, what is known, what is unknown and what is contested in relation to your research topic. So, you can think of it as an  integrated review of the state of knowledge  around your research topic. 

What’s the purpose of a literature review?

The literature review chapter has a few important functions within your dissertation, thesis or research project. Let’s take a look at these:

Purpose #1 – Demonstrate your topic knowledge

The first function of the literature review chapter is, quite simply, to show the reader (or marker) that you  know what you’re talking about . In other words, a good literature review chapter demonstrates that you’ve read the relevant existing research and understand what’s going on – who’s said what, what’s agreed upon, disagreed upon and so on. This needs to be  more than just a summary  of who said what – it needs to integrate the existing research to  show how it all fits together  and what’s missing (which leads us to purpose #2, next). 

Purpose #2 – Reveal the research gap that you’ll fill

The second function of the literature review chapter is to  show what’s currently missing  from the existing research, to lay the foundation for your own research topic. In other words, your literature review chapter needs to show that there are currently “missing pieces” in terms of the bigger puzzle, and that  your study will fill one of those research gaps . By doing this, you are showing that your research topic is original and will help contribute to the body of knowledge. In other words, the literature review helps justify your research topic.  

Purpose #3 – Lay the foundation for your conceptual framework

The third function of the literature review is to form the  basis for a conceptual framework . Not every research topic will necessarily have a conceptual framework, but if your topic does require one, it needs to be rooted in your literature review. 

For example, let’s say your research aims to identify the drivers of a certain outcome – the factors which contribute to burnout in office workers. In this case, you’d likely develop a conceptual framework which details the potential factors (e.g. long hours, excessive stress, etc), as well as the outcome (burnout). Those factors would need to emerge from the literature review chapter – they can’t just come from your gut! 

So, in this case, the literature review chapter would uncover each of the potential factors (based on previous studies about burnout), which would then be modelled into a framework. 

Purpose #4 – To inform your methodology

The fourth function of the literature review is to  inform the choice of methodology  for your own research. As we’ve  discussed on the Grad Coach blog , your choice of methodology will be heavily influenced by your research aims, objectives and questions . Given that you’ll be reviewing studies covering a topic close to yours, it makes sense that you could learn a lot from their (well-considered) methodologies.

So, when you’re reviewing the literature, you’ll need to  pay close attention to the research design , methodology and methods used in similar studies, and use these to inform your methodology. Quite often, you’ll be able to  “borrow” from previous studies . This is especially true for quantitative studies , as you can use previously tried and tested measures and scales. 

How do I find articles for my literature review?

Finding quality journal articles is essential to crafting a rock-solid literature review. As you probably already know, not all research is created equally, and so you need to make sure that your literature review is  built on credible research . 

We could write an entire post on how to find quality literature (actually, we have ), but a good starting point is Google Scholar . Google Scholar is essentially the academic equivalent of Google, using Google’s powerful search capabilities to find relevant journal articles and reports. It certainly doesn’t cover every possible resource, but it’s a very useful way to get started on your literature review journey, as it will very quickly give you a good indication of what the  most popular pieces of research  are in your field.

One downside of Google Scholar is that it’s merely a search engine – that is, it lists the articles, but oftentimes  it doesn’t host the articles . So you’ll often hit a paywall when clicking through to journal websites. 

Thankfully, your university should provide you with access to their library, so you can find the article titles using Google Scholar and then search for them by name in your university’s online library. Your university may also provide you with access to  ResearchGate , which is another great source for existing research. 

Remember, the correct search keywords will be super important to get the right information from the start. So, pay close attention to the keywords used in the journal articles you read and use those keywords to search for more articles. If you can’t find a spoon in the kitchen, you haven’t looked in the right drawer. 

Need a helping hand?

How should I structure my literature review?

Unfortunately, there’s no generic universal answer for this one. The structure of your literature review will depend largely on your topic area and your research aims and objectives.

You could potentially structure your literature review chapter according to theme, group, variables , chronologically or per concepts in your field of research. We explain the main approaches to structuring your literature review here . You can also download a copy of our free literature review template to help you establish an initial structure.

In general, it’s also a good idea to start wide (i.e. the big-picture-level) and then narrow down, ending your literature review close to your research questions . However, there’s no universal one “right way” to structure your literature review. The most important thing is not to discuss your sources one after the other like a list – as we touched on earlier, your literature review needs to synthesise the research , not summarise it .

Ultimately, you need to craft your literature review so that it conveys the most important information effectively – it needs to tell a logical story in a digestible way. It’s no use starting off with highly technical terms and then only explaining what these terms mean later. Always assume your reader is not a subject matter expert and hold their hand through a journe y of the literature while keeping the functions of the literature review chapter (which we discussed earlier) front of mind.

Example of a literature review

In the video below, we walk you through a high-quality literature review from a dissertation that earned full distinction. This will give you a clearer view of what a strong literature review looks like in practice and hopefully provide some inspiration for your own. 

Wrapping Up

In this post, we’ve (hopefully) answered the question, “ what is a literature review? “. We’ve also considered the purpose and functions of the literature review, as well as how to find literature and how to structure the literature review chapter. If you’re keen to learn more, check out the literature review section of the Grad Coach blog , as well as our detailed video post covering how to write a literature review . 

Psst… there’s more!

This post is an extract from our bestselling short course, Literature Review Bootcamp . If you want to work smart, you don't want to miss this .

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Literature Reviews

What this handout is about.

This handout will explain what literature reviews are and offer insights into the form and construction of literature reviews in the humanities, social sciences, and sciences.

Introduction

OK. You’ve got to write a literature review. You dust off a novel and a book of poetry, settle down in your chair, and get ready to issue a “thumbs up” or “thumbs down” as you leaf through the pages. “Literature review” done. Right?

Wrong! The “literature” of a literature review refers to any collection of materials on a topic, not necessarily the great literary texts of the world. “Literature” could be anything from a set of government pamphlets on British colonial methods in Africa to scholarly articles on the treatment of a torn ACL. And a review does not necessarily mean that your reader wants you to give your personal opinion on whether or not you liked these sources.

What is a literature review, then?

A literature review discusses published information in a particular subject area, and sometimes information in a particular subject area within a certain time period.

A literature review can be just a simple summary of the sources, but it usually has an organizational pattern and combines both summary and synthesis. A summary is a recap of the important information of the source, but a synthesis is a re-organization, or a reshuffling, of that information. It might give a new interpretation of old material or combine new with old interpretations. Or it might trace the intellectual progression of the field, including major debates. And depending on the situation, the literature review may evaluate the sources and advise the reader on the most pertinent or relevant.

But how is a literature review different from an academic research paper?

The main focus of an academic research paper is to develop a new argument, and a research paper is likely to contain a literature review as one of its parts. In a research paper, you use the literature as a foundation and as support for a new insight that you contribute. The focus of a literature review, however, is to summarize and synthesize the arguments and ideas of others without adding new contributions.

Why do we write literature reviews?

Literature reviews provide you with a handy guide to a particular topic. If you have limited time to conduct research, literature reviews can give you an overview or act as a stepping stone. For professionals, they are useful reports that keep them up to date with what is current in the field. For scholars, the depth and breadth of the literature review emphasizes the credibility of the writer in his or her field. Literature reviews also provide a solid background for a research paper’s investigation. Comprehensive knowledge of the literature of the field is essential to most research papers.

Who writes these things, anyway?

Literature reviews are written occasionally in the humanities, but mostly in the sciences and social sciences; in experiment and lab reports, they constitute a section of the paper. Sometimes a literature review is written as a paper in itself.

Let’s get to it! What should I do before writing the literature review?

If your assignment is not very specific, seek clarification from your instructor:

• Roughly how many sources should you include?
• What types of sources (books, journal articles, websites)?
• Should you summarize, synthesize, or critique your sources by discussing a common theme or issue?
• Should you evaluate your sources?
• Should you provide subheadings and other background information, such as definitions and/or a history?

Find models

Look for other literature reviews in your area of interest or in the discipline and read them to get a sense of the types of themes you might want to look for in your own research or ways to organize your final review. You can simply put the word “review” in your search engine along with your other topic terms to find articles of this type on the Internet or in an electronic database. The bibliography or reference section of sources you’ve already read are also excellent entry points into your own research.

Narrow your topic

There are hundreds or even thousands of articles and books on most areas of study. The narrower your topic, the easier it will be to limit the number of sources you need to read in order to get a good survey of the material. Your instructor will probably not expect you to read everything that’s out there on the topic, but you’ll make your job easier if you first limit your scope.

Keep in mind that UNC Libraries have research guides and to databases relevant to many fields of study. You can reach out to the subject librarian for a consultation: https://library.unc.edu/support/consultations/ .

And don’t forget to tap into your professor’s (or other professors’) knowledge in the field. Ask your professor questions such as: “If you had to read only one book from the 90’s on topic X, what would it be?” Questions such as this help you to find and determine quickly the most seminal pieces in the field.

Consider whether your sources are current

Some disciplines require that you use information that is as current as possible. In the sciences, for instance, treatments for medical problems are constantly changing according to the latest studies. Information even two years old could be obsolete. However, if you are writing a review in the humanities, history, or social sciences, a survey of the history of the literature may be what is needed, because what is important is how perspectives have changed through the years or within a certain time period. Try sorting through some other current bibliographies or literature reviews in the field to get a sense of what your discipline expects. You can also use this method to consider what is currently of interest to scholars in this field and what is not.

Strategies for writing the literature review

Find a focus.

A literature review, like a term paper, is usually organized around ideas, not the sources themselves as an annotated bibliography would be organized. This means that you will not just simply list your sources and go into detail about each one of them, one at a time. No. As you read widely but selectively in your topic area, consider instead what themes or issues connect your sources together. Do they present one or different solutions? Is there an aspect of the field that is missing? How well do they present the material and do they portray it according to an appropriate theory? Do they reveal a trend in the field? A raging debate? Pick one of these themes to focus the organization of your review.

Convey it to your reader

A literature review may not have a traditional thesis statement (one that makes an argument), but you do need to tell readers what to expect. Try writing a simple statement that lets the reader know what is your main organizing principle. Here are a couple of examples:

The current trend in treatment for congestive heart failure combines surgery and medicine. More and more cultural studies scholars are accepting popular media as a subject worthy of academic consideration.

Consider organization

You’ve got a focus, and you’ve stated it clearly and directly. Now what is the most effective way of presenting the information? What are the most important topics, subtopics, etc., that your review needs to include? And in what order should you present them? Develop an organization for your review at both a global and local level:

First, cover the basic categories

Just like most academic papers, literature reviews also must contain at least three basic elements: an introduction or background information section; the body of the review containing the discussion of sources; and, finally, a conclusion and/or recommendations section to end the paper. The following provides a brief description of the content of each:

• Introduction: Gives a quick idea of the topic of the literature review, such as the central theme or organizational pattern.
• Body: Contains your discussion of sources and is organized either chronologically, thematically, or methodologically (see below for more information on each).
• Conclusions/Recommendations: Discuss what you have drawn from reviewing literature so far. Where might the discussion proceed?

Organizing the body

Once you have the basic categories in place, then you must consider how you will present the sources themselves within the body of your paper. Create an organizational method to focus this section even further.

To help you come up with an overall organizational framework for your review, consider the following scenario:

You’ve decided to focus your literature review on materials dealing with sperm whales. This is because you’ve just finished reading Moby Dick, and you wonder if that whale’s portrayal is really real. You start with some articles about the physiology of sperm whales in biology journals written in the 1980’s. But these articles refer to some British biological studies performed on whales in the early 18th century. So you check those out. Then you look up a book written in 1968 with information on how sperm whales have been portrayed in other forms of art, such as in Alaskan poetry, in French painting, or on whale bone, as the whale hunters in the late 19th century used to do. This makes you wonder about American whaling methods during the time portrayed in Moby Dick, so you find some academic articles published in the last five years on how accurately Herman Melville portrayed the whaling scene in his novel.

Now consider some typical ways of organizing the sources into a review:

• Chronological: If your review follows the chronological method, you could write about the materials above according to when they were published. For instance, first you would talk about the British biological studies of the 18th century, then about Moby Dick, published in 1851, then the book on sperm whales in other art (1968), and finally the biology articles (1980s) and the recent articles on American whaling of the 19th century. But there is relatively no continuity among subjects here. And notice that even though the sources on sperm whales in other art and on American whaling are written recently, they are about other subjects/objects that were created much earlier. Thus, the review loses its chronological focus.
• By publication: Order your sources by publication chronology, then, only if the order demonstrates a more important trend. For instance, you could order a review of literature on biological studies of sperm whales if the progression revealed a change in dissection practices of the researchers who wrote and/or conducted the studies.
• By trend: A better way to organize the above sources chronologically is to examine the sources under another trend, such as the history of whaling. Then your review would have subsections according to eras within this period. For instance, the review might examine whaling from pre-1600-1699, 1700-1799, and 1800-1899. Under this method, you would combine the recent studies on American whaling in the 19th century with Moby Dick itself in the 1800-1899 category, even though the authors wrote a century apart.
• Thematic: Thematic reviews of literature are organized around a topic or issue, rather than the progression of time. However, progression of time may still be an important factor in a thematic review. For instance, the sperm whale review could focus on the development of the harpoon for whale hunting. While the study focuses on one topic, harpoon technology, it will still be organized chronologically. The only difference here between a “chronological” and a “thematic” approach is what is emphasized the most: the development of the harpoon or the harpoon technology.But more authentic thematic reviews tend to break away from chronological order. For instance, a thematic review of material on sperm whales might examine how they are portrayed as “evil” in cultural documents. The subsections might include how they are personified, how their proportions are exaggerated, and their behaviors misunderstood. A review organized in this manner would shift between time periods within each section according to the point made.
• Methodological: A methodological approach differs from the two above in that the focusing factor usually does not have to do with the content of the material. Instead, it focuses on the “methods” of the researcher or writer. For the sperm whale project, one methodological approach would be to look at cultural differences between the portrayal of whales in American, British, and French art work. Or the review might focus on the economic impact of whaling on a community. A methodological scope will influence either the types of documents in the review or the way in which these documents are discussed. Once you’ve decided on the organizational method for the body of the review, the sections you need to include in the paper should be easy to figure out. They should arise out of your organizational strategy. In other words, a chronological review would have subsections for each vital time period. A thematic review would have subtopics based upon factors that relate to the theme or issue.

Sometimes, though, you might need to add additional sections that are necessary for your study, but do not fit in the organizational strategy of the body. What other sections you include in the body is up to you. Put in only what is necessary. Here are a few other sections you might want to consider:

• Current Situation: Information necessary to understand the topic or focus of the literature review.
• History: The chronological progression of the field, the literature, or an idea that is necessary to understand the literature review, if the body of the literature review is not already a chronology.
• Methods and/or Standards: The criteria you used to select the sources in your literature review or the way in which you present your information. For instance, you might explain that your review includes only peer-reviewed articles and journals.

Questions for Further Research: What questions about the field has the review sparked? How will you further your research as a result of the review?

Begin composing

Once you’ve settled on a general pattern of organization, you’re ready to write each section. There are a few guidelines you should follow during the writing stage as well. Here is a sample paragraph from a literature review about sexism and language to illuminate the following discussion:

However, other studies have shown that even gender-neutral antecedents are more likely to produce masculine images than feminine ones (Gastil, 1990). Hamilton (1988) asked students to complete sentences that required them to fill in pronouns that agreed with gender-neutral antecedents such as “writer,” “pedestrian,” and “persons.” The students were asked to describe any image they had when writing the sentence. Hamilton found that people imagined 3.3 men to each woman in the masculine “generic” condition and 1.5 men per woman in the unbiased condition. Thus, while ambient sexism accounted for some of the masculine bias, sexist language amplified the effect. (Source: Erika Falk and Jordan Mills, “Why Sexist Language Affects Persuasion: The Role of Homophily, Intended Audience, and Offense,” Women and Language19:2).

Use evidence

In the example above, the writers refer to several other sources when making their point. A literature review in this sense is just like any other academic research paper. Your interpretation of the available sources must be backed up with evidence to show that what you are saying is valid.

Be selective

Select only the most important points in each source to highlight in the review. The type of information you choose to mention should relate directly to the review’s focus, whether it is thematic, methodological, or chronological.

Use quotes sparingly

Falk and Mills do not use any direct quotes. That is because the survey nature of the literature review does not allow for in-depth discussion or detailed quotes from the text. Some short quotes here and there are okay, though, if you want to emphasize a point, or if what the author said just cannot be rewritten in your own words. Notice that Falk and Mills do quote certain terms that were coined by the author, not common knowledge, or taken directly from the study. But if you find yourself wanting to put in more quotes, check with your instructor.

Summarize and synthesize

Remember to summarize and synthesize your sources within each paragraph as well as throughout the review. The authors here recapitulate important features of Hamilton’s study, but then synthesize it by rephrasing the study’s significance and relating it to their own work.

Keep your own voice

While the literature review presents others’ ideas, your voice (the writer’s) should remain front and center. Notice that Falk and Mills weave references to other sources into their own text, but they still maintain their own voice by starting and ending the paragraph with their own ideas and their own words. The sources support what Falk and Mills are saying.

Use caution when paraphrasing

When paraphrasing a source that is not your own, be sure to represent the author’s information or opinions accurately and in your own words. In the preceding example, Falk and Mills either directly refer in the text to the author of their source, such as Hamilton, or they provide ample notation in the text when the ideas they are mentioning are not their own, for example, Gastil’s. For more information, please see our handout on plagiarism .

Revise, revise, revise

Draft in hand? Now you’re ready to revise. Spending a lot of time revising is a wise idea, because your main objective is to present the material, not the argument. So check over your review again to make sure it follows the assignment and/or your outline. Then, just as you would for most other academic forms of writing, rewrite or rework the language of your review so that you’ve presented your information in the most concise manner possible. Be sure to use terminology familiar to your audience; get rid of unnecessary jargon or slang. Finally, double check that you’ve documented your sources and formatted the review appropriately for your discipline. For tips on the revising and editing process, see our handout on revising drafts .

Works consulted

We consulted these works while writing this handout. This is not a comprehensive list of resources on the handout’s topic, and we encourage you to do your own research to find additional publications. Please do not use this list as a model for the format of your own reference list, as it may not match the citation style you are using. For guidance on formatting citations, please see the UNC Libraries citation tutorial . We revise these tips periodically and welcome feedback.

Anson, Chris M., and Robert A. Schwegler. 2010. The Longman Handbook for Writers and Readers , 6th ed. New York: Longman.

Jones, Robert, Patrick Bizzaro, and Cynthia Selfe. 1997. The Harcourt Brace Guide to Writing in the Disciplines . New York: Harcourt Brace.

Lamb, Sandra E. 1998. How to Write It: A Complete Guide to Everything You’ll Ever Write . Berkeley: Ten Speed Press.

Rosen, Leonard J., and Laurence Behrens. 2003. The Allyn & Bacon Handbook , 5th ed. New York: Longman.

Troyka, Lynn Quittman, and Doug Hesse. 2016. Simon and Schuster Handbook for Writers , 11th ed. London: Pearson.

You may reproduce it for non-commercial use if you use the entire handout and attribute the source: The Writing Center, University of North Carolina at Chapel Hill

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Research Skills

Introduction and literature review.

This section is the beginning of the article, but don’t expect it to contain any sort of position or argument. In academic articles, this section has one, overarching purpose: to demonstrate that the authors are familiar with all previous relevant research on the issue they are writing about. Therefore, this section is usually the most “citation-heavy” section of the paper. It is not uncommon to have one or more citations at the end of each sentence. You will likely also encounter a number of compound citations: parentheticals in which not one source, but two or more are cited at one time. Each sentence that precedes a citation in this section is typically a very brief paraphrase of the relevant methods or applicable findings of the other articles that have come before. This review of prior studies is a very important exercise for scholars because it demonstrates the depth of their understanding. None of the articles you read occur in a vacuum; they are usually part of an evolving web of scholarship. Each new article picks up the thread (or, usually, several threads) left by articles published recently. Another important thing to realize is that, in a very real sense, the authors have not really begun; they do not make an argument or say much that is new in this section. It is designed to provide an academic history and theoretical context for the topic of discussion.

At the very end of every literature review section, however, the authors do something important. After having demonstrated their familiarity with previous research, authors indicate that, even though much research has been done, there are still gaps in the research that need filling. You should try to find language such as, “While many studies have examined this subject, no one has looked at this particular issue in this way.” The authors then announce their intention to address that gap in knowledge with the research that follows. This rhetorical move always appears at the end of this section, and often gives the reader the clearest and most detailed description of what exactly the authors are looking at—and why. This is not a thesis, however. Academic articles are not like the essays you may be used to writing, in which the thesis appears at the end of the introduction. The research gap is more akin to a hypothesis than a thesis. It does not make an argument, which comes much later—usually in the discussion or conclusion.

There are also articles that are stand-alone literature reviews; these are sometimes called “Review Articles” or “Meta-analyses.” Rather than engaging in original research, these articles, if they are recent and on point, can provide you with the bibliographic information of all the important, recent sources on your topic. There are many ways to find sources that don’t involve a search engine of any kind. Look at your articles’ references lists to see if they contain any relevant-sounding articles that you haven’t found by other means. You can save a great deal of time this way.

• Parts of An Article. Authored by : Kerry Bowers. Provided by : The University of Mississippi. Project : WRIT 250 Committee OER Project. License : CC BY-SA: Attribution-ShareAlike

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How do I Write a Literature Review?: #5 Writing the Review

• Step #1: Choosing a Topic
• Step #2: Finding Information
• Step #3: Evaluating Content
• Step #4: Synthesizing Content
• #5 Writing the Review
• Citing Your Sources

WRITING THE REVIEW 

You've done the research and now you're ready to put your findings down on paper. When preparing to write your review, first consider how will you organize your review.

The actual review generally has 5 components:

Abstract  -  An abstract is a summary of your literature review. It is made up of the following parts:

• A contextual sentence about your motivation behind your research topic
• Your thesis statement
• A descriptive statement about the types of literature used in the review
• Summarize your findings
• Conclusion(s) based upon your findings

Introduction :   Like a typical research paper introduction, provide the reader with a quick idea of the topic of the literature review:

• Define or identify the general topic, issue, or area of concern. This provides the reader with context for reviewing the literature.
• Identify related trends in what has already been published about the topic; or conflicts in theory, methodology, evidence, and conclusions; or gaps in research and scholarship; or a single problem or new perspective of immediate interest.
• Establish your reason (point of view) for reviewing the literature; explain the criteria to be used in analyzing and comparing literature and the organization of the review (sequence); and, when necessary, state why certain literature is or is not included (scope)  - 

Body :  The body of a literature review contains your discussion of sources and can be organized in 3 ways-

• Chronological -  by publication or by trend
• Thematic -  organized around a topic or issue, rather than the progression of time
• Methodical -  the focusing factor usually does not have to do with the content of the material. Instead, it focuses on the "methods" of the literature's researcher or writer that you are reviewing

You may also want to include a section on "questions for further research" and discuss what questions the review has sparked about the topic/field or offer suggestions for future studies/examinations that build on your current findings.

Conclusion :  In the conclusion, you should:

Conclude your paper by providing your reader with some perspective on the relationship between your literature review's specific topic and how it's related to it's parent discipline, scientific endeavor, or profession.

Bibliography :   Since a literature review is composed of pieces of research, it is very important that your correctly cite the literature you are reviewing, both in the reviews body as well as in a bibliography/works cited. To learn more about different citation styles, visit the " Citing Your Sources " tab.

• Writing a Literature Review: Wesleyan University
• Literature Review: Edith Cowan University
• << Previous: Step #4: Synthesizing Content
• Next: Citing Your Sources >>
• Last Updated: Aug 22, 2023 1:35 PM
• URL: https://libguides.eastern.edu/literature_reviews

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Introduction vs. Literature Review — What's the Difference?

Difference Between Introduction and Literature Review

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Literature Review for Introduction Vs. Discussion

A literature review presents a summary of studies related to a particular area of research. It identifies and summarizes all the relevant research conducted on a particular topic. Literature reviews are used in the  introduction  and  discussion sections of your  manuscripts . However, there are differences in how you can present literature reviews in each section. This smartshort describes how to effectively use literature reviews in these sections. You can also read a detailed article on this topic here.

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Rapid literature review: definition and methodology

Beata smela.

a Assignity, Cracow, Poland

Mondher Toumi

b Public Health Department, Aix-Marseille University, Marseille, France

Karolina Świerk

Clement francois, małgorzata biernikiewicz.

c Studio Slowa, Wroclaw, Poland

Emilie Clay

d Clever-Access, Paris, France

Laurent Boyer

Introduction: A rapid literature review (RLR) is an alternative to systematic literature review (SLR) that can speed up the analysis of newly published data. The objective was to identify and summarize available information regarding different approaches to defining RLR and the methodology applied to the conduct of such reviews.

Methods: The Medline and EMBASE databases, as well as the grey literature, were searched using the set of keywords and their combination related to the targeted and rapid review, as well as design, approach, and methodology. Of the 3,898 records retrieved, 12 articles were included.

Results: Specific definition of RLRs has only been developed in 2021. In terms of methodology, the RLR should be completed within shorter timeframes using simplified procedures in comparison to SLRs, while maintaining a similar level of transparency and minimizing bias. Inherent components of the RLR process should be a clear research question, search protocol, simplified process of study selection, data extraction, and quality assurance.

Conclusions: There is a lack of consensus on the formal definition of the RLR and the best approaches to perform it. The evidence-based supporting methods are evolving, and more work is needed to define the most robust approaches.

Introduction

A systematic literature review (SLR) summarizes the results of all available studies on a specific topic and provides a high level of evidence. Authors of the SLR have to follow an advanced plan that covers defining a priori information regarding the research question, sources they are going to search, inclusion criteria applied to choose studies answering the research question, and information regarding how they are going to summarize findings [ 1 ].

The rigor and transparency of SLRs make them the most reliable form of literature review [ 2 ], providing a comprehensive, objective summary of the evidence for a given topic [ 3 , 4 ]. On the other hand, the SLR process is usually very time-consuming and requires a lot of human resources. Taking into account a high increase of newly published data and a growing need to analyze information in the fastest possible way, rapid literature reviews (RLRs) often replace standard SLRs.

There are several guidelines on the methodology of RLRs [ 5–11 ]; however, only recently, one publication from 2021 attempted to construct a unified definition [ 11 ]. Generally, by RLRs, researchers understand evidence synthesis during which some of the components of the systematic approach are being used to facilitate answering a focused research question; however, scope restrictions and a narrower search strategy help to make the project manageable in a shorter time and to get the key conclusions faster [ 4 ].

The objective of this research was to collect and summarize available information on different approaches to the definition and methodology of RLRs. An RLR has been run to capture publications providing data that fit the project objective.

To find publications reporting information on the methodology of RLRs, searches were run in the Medline and EMBASE databases in November 2022. The following keywords were searched for in titles and abstracts: ‘targeted adj2 review’ OR ‘focused adj2 review’ OR ‘rapid adj2 review’, and ‘methodology’ OR ‘design’ OR ‘scheme’ OR ‘approach’. The grey literature was identified using Google Scholar with keywords including ‘targeted review methodology’ OR ‘focused review methodology’ OR ‘rapid review methodology’. Only publications in English were included, and the date of publication was restricted to year 2016 onward in order to identify the most up-to-date literature. The reference lists of each included article were searched manually to obtain the potentially eligible articles. Titles and abstracts of the retrieved records were first screened to exclude articles that were evidently irrelevant. The full texts of potentially relevant papers were further reviewed to examine their eligibility.

A pre-defined Excel grid was developed to extract the following information related to the methodology of RLR from guidelines:

• Definition,
• Research question and searches,
• Studies selection,
• Data extraction and quality assessment,
• Additional information.

There was no restriction on the study types to be analyzed; any study reporting on the methodology of RLRs could be included: reviews, practice guidelines, commentaries, and expert opinions on RLR relevant to healthcare policymakers or practitioners. The data extraction and evidence summary were conducted by one analyst and further examined by a senior analyst to ensure that relevant information was not omitted. Disagreements were resolved by discussion and consensus.

Studies selection

A total of 3,898 records (3,864 articles from a database search and 34 grey literature from Google Scholar) were retrieved. After removing duplicates, titles and abstracts of 3,813 articles were uploaded and screened. The full texts of 43 articles were analyzed resulting in 12 articles selected for this review, including 7 guidelines [ 5–11 ] on the methodology of RLRs, together with 2 papers summarizing the results of the Delphi consensus on the topic [ 12 , 13 ], and 3 publications analyzing and assessing different approaches to RLRs [ 4 , 14 , 15 ].

Overall, seven guidelines were identified: from the World Health Organization (WHO) [ 5 ], National Collaborating Centre for Methods and Tools (NCCMT) [ 7 ], the UK government [ 8 ], the Oxford Centre for Evidence Based Medicine [ 9 ], the Cochrane group [ 6 , 11 ], and one multi-national review [ 10 ]. Among the papers that did not describe the guidelines, Gordon et al. [ 4 ] proposed 12 tips for conducting a rapid review in the right settings and discussed why these reviews may be more beneficial in some circumstances. The objective of work conducted by Tricco et al. [ 13 ] and Pandor et al. [ 12 ] was to collect and compare perceptions of rapid reviews from stakeholders, including researchers, policymakers, industry, journal editors, and healthcare providers, and to reach a consensus outlining the domains to consider when deciding on approaches for RLRs. Haby et al. [ 14 ] run a rapid review of systematic reviews and primary studies to find out the best way to conduct an RLR in health policy and practice. In Tricco et al. (2022) [ 15 ], JBI position statement for RLRs is presented.

From all the seven identified guidelines information regarding definitions the authors used for RLRs, approach to the PICOS criteria and search strategy development, studies selection, data extractions, quality assessment, and reporting were extracted.

Cochrane Rapid Reviews Methods Group developed methods guidance based on scoping review of the underlying evidence, primary methods studies conducted, as well as surveys sent to Cochrane representative and discussion among those with expertise [ 11 ]. They analyzed over 300 RLRs or RLR method papers and based on the methodology of those studies, constructed a broad definition RLR, one that meets a minimum set of requirements identified in the thematic analysis: ‘ A rapid review is a form of knowledge synthesis that accelerates the process of conducting a traditional systematic review through streamlining or omitting a variety of methods to produce evidence in a resource-efficient manner .’ This interpretation aligns with more than 50% of RLRs identified in this study. The authors additionally provided several other definitions, depending on specific situations or requirements (e.g., when RLR is produced on stakeholder’s request). It was additionally underlined that RLRs should be driven by the need of timely evidence for decision-making purposes [ 11 ].

Rapid reviews vary in their objective, format, and methods used for evidence synthesis. This is a quite new area, and still no agreement on optimal methods can be found [ 5 ]. All of the definitions are highlighting that RLRs are completed within shorter timeframes than SLRs, and also lack of time is one of the main reasons they are conducted. It has been suggested that most rapid reviews are conducted within 12 weeks; however, some of the resources suggest time between a few weeks to no more than 6 months [ 5 , 6 ]. Some of the definitions are highlighting that RLRs follow the SLR process, but certain phases of the process are simplified or omitted to retrieve information in a time-saving way [ 6 , 7 ]. Different mechanisms are used to enhance the timeliness of reviews. They can be used independently or concurrently: increasing the intensity of work by intensifying the efforts of multiple analysts by parallelization of tasks, using review shortcuts whereby one or more systematic review steps may be reduced, automatizing review steps by using new technologies [ 5 ]. The UK government report [ 8 ] referred to two different RLRs: in the form of quick scoping reviews (QSR) or rapid evidence assessments (REA). While being less resource and time-consuming compared to standard SLRs, QSRs and REAs are designed to be similarly transparent and to minimize bias. QSRs can be applied to rather open-ended questions, e.g., ‘what do we know about something’ but both, QSRs and REAs, provide an understanding of the volume and characteristics of evidence on a specific topic, allowing answering questions by maximizing the use of existing data, and providing a clear picture of the adequacy of existing evidence [ 8 ].

Research questions and searches

The guidelines suggest creating a clear research question and search protocol at the beginning of the project. Additionally, to not duplicate RLRs, the Cochrane Rapid Reviews Methods Group encourages all people working on RLRs to consider registering their search protocol with PROSPERO, the international prospective register of reviews; however, so far they are not formally registered in most cases [ 5 , 6 ]. They also recommend involving key stakeholders (review users) to set and refine the review question, criteria, and outcomes, as well as consulting them through the entire process [ 11 ].

Regarding research questions, it is better to structure them in a neutral way rather than focus on a specific direction for the outcome. By doing so, the researcher is in a better position to identify all the relevant evidence [ 7 ]. Authors can add a second, supportive research question when needed [ 8 ]. It is encouraged to limit the number of interventions, comparators and outcomes, to focus on the ones that are most important for decision-making [ 11 ]. Useful could be also reviewing additional materials, e.g., SLRs on the topic, as well as conducting a quick literature search to better understand the topic before starting with RLRs [ 7 ]. In SLRs researchers usually do not need to care a lot about time spent on creating PICOS, they need to make sure that the scope is broad enough, and they cannot use many restrictions. When working on RLRs, a reviewer may spend more or less time defining each of the components of the study question, and the main step is making sure that PICOS addresses the needs of those who requested the rapid review, and at the same time, it is feasible within the required time frame [ 7 ]. Search protocol should contain an outline of how the following review steps are to be carried out, including selected search keywords and a full strategy, a list of data sources, precise inclusion and exclusion criteria, a strategy for data extraction and critical appraisal, and a plan of how the information will be synthesized [ 8 ].

In terms of searches running, in most cases, an exhaustive process will not be feasible. Researchers should make sure that the search is effective and efficient to produce results in a timely manner. Cochrane Rapid Reviews Methods Group recommends involving an information specialist and conducting peer review of at least one search strategy [ 11 ]. According to the rapid review guidebook by McMaster University [ 7 ], it is important that RLRs, especially those that support policy and program decisions, are being fed by the results of a body of literature, rather than single studies, when possible. It would result in more generalizable findings applied at the level of a population and serve more realistic findings for program decisions [ 7 ]. It is important to document the search strategy, together with a record of the date and any date limits of the search, so that it can easily be run again, modified, or updated. Furthermore, the information on the individual databases included in platform services should always be reported, as this depends on organizations’ subscriptions and must be included for transparency and repeatability [ 7 , 8 ]. Good solution for RLRs is narrowing the scope or searching a limited number of databases and other sources [ 7 ]. Often, the authors use the PubMed/MEDLINE, Cochrane Library, and Embase databases. In most reviews, two or more databases are searched, and common limits are language (usually restricted to English), date, study design, and geographical area. Some RLRs include searching of grey literature; however, contact with authors is rather uncommon [ 5 , 8 ]. According to the flexible framework for restricted systematic review published by the University of Oxford, the search should be run in at least one major scientific database such as PubMed, and one other source, e.g., Google Scholar [ 9 ]. Grey literature and unpublished evidence may be particularly needed and important for intervention questions. It is related to the fact that studies that do not report the effects of interventions are less likely to be published [ 8 ]. If there is any type of evidence that will not be considered by the RLRs, e.g., reviews or theoretical and conceptual studies, it should also be stated in the protocol together with justification [ 8 ]. Additionally, authors of a practical guide published by WHO suggest using a staged search to identify existing SLRs at the beginning, and then focusing on studies with other designs [ 5 ]. If a low number of citations have been retrieved, it is acceptable to expand searches, remove some of the limits, and add additional databases and sources [ 7 ].

Searching for RLRs is an iterative process, and revising the approach is usually needed [ 7 ]. Changes should be confirmed with stakeholders and should be tracked and reflected in the final report [ 5 ].

The next step in the rapid review is the selection of studies consisting of two phases: screening of titles and abstracts, and analysis of full texts. Prior to screening initiation, it is recommended to conduct a pilot exercise using the same 30–50 abstracts and 5–10 full-texts for the entire screening team in order to calibrate and test the review form [ 11 ]. In contrast to SLRs, it can be done by one reviewer with or without verification by a second one. If verification is performed, usually the second reviewer checks only a subset of records and compares them. Cochrane Group, in contrast, recommends a stricter approach: at least 20% of references should be double-screened at titles and abstracts stage, and while the rest of the references may be screened by one reviewer, the excluded items need to be re-examined by second reviewer; similar approach is used in full-text screening [ 11 ]. This helps to ensure that bias was reduced and that the PICOS criteria are applied in a relevant way [ 5 , 8 , 9 , 11 ]. During the analysis of titles and abstracts, there is no need to report reasons for exclusion; however, they should be tracked for all excluded full texts [ 7 ].

Data extraction and quality assessment

According to the WHO guide, the most common method for data extraction in RLRs is extraction done by a single reviewer with or without partial verification. The authors point out that a reasonable approach is to use a second reviewer to check a random sample of at least 10% of the extractions for accuracy. Dual performance is more necessary for the extraction of quantitative results than for descriptive study information. In contrast, Cochrane group recommends that second reviewer should check the correctness and completeness of all data [ 11 ]. When possible, extractions should be limited to key characteristics and outcomes of the study. The same approach to data extraction is also suggested for a quality assessment process within rapid reviews [ 5 , 9 , 11 ]. Authors of the guidebook from McMaster University highlight that data extraction should be done ideally by two reviewers independently and consensus on the discrepancies should always be reached [ 7 ]. The final decision on the approach to this important step of review should depend on the available time and should also reflect the complexity of the research question [ 9 ].

For screening, analysis of full texts, extractions, and quality assessments, researchers can use information technologies to support them by making these review steps more efficient [ 5 ].

Before data reporting, a reviewer should prepare a document with key message headings, executive summary, background related to the topic and status of the current knowledge, project question, synthesis of findings, conclusions, and recommendations. According to the McMaster University guidebook, a report should be structured in a 1:2:20 format, that is, one page for key messages, two pages for an executive summary, and a full report of up to 20 pages [ 7 ]. All the limitations of the RLRs should be analyzed, and conclusions should be drawn with caution [ 5 ]. The quality of the accumulated evidence and the strength of recommendations can be assessed using, e.g., the GRADE system [ 5 ]. When working on references quoting, researchers should remember to use a primary source, not secondary references [ 7 ]. It would be worth considering the support of some software tools to automate reporting steps. Additionally, any standardization of the process and the usage of templates can support report development and enhance the transparency of the review [ 5 ].

Ideally, all the review steps should be completed during RLRs; however, often some steps may need skipping or will not be completed as thoroughly as should because of time constraints. It is always crucial to decide which steps may be skipped, and which are the key ones, depending on the project [ 7 ]. Guidelines suggest that it may be helpful to invite researchers with experience in the operations of SLRs to participate in the rapid review development [ 5 , 9 ]. As some of the steps will be completed by one reviewer only, it is important to provide them with relevant training at the beginning of the process, as well as during the review, to minimize the risk of mistakes [ 5 ].

Additional information

Depending on the policy goal and available resources and deadlines, methodology of the RLRs may be modified. Wilson et al. [ 10 ] provided extensive guidelines for performing RLR within days (e.g., to inform urgent internal policy discussions and/or management decisions), weeks (e.g., to inform public debates), or months (e.g., to inform policy development cycles that have a longer timeline, but that cannot wait for a traditional full systematic review). These approaches vary in terms of data synthesis, types of considered evidence and project management considerations.

In shortest timeframes, focused questions and subquestions should be formulated, typically to conduct a policy analysis; the report should consist of tables along with a brief narrative summary. Evidence from SLRs is often considered, as well as key informant interviews may be conducted to identify additional literature and insights about the topic, while primary studies and other types of evidence are not typically feasible due to time restrictions. The review would be best conducted with 1–2 reviewers sharing the work, enabling rapid iterations of the review. As for RLRs with longer timeline (weeks), these may use a mix of policy, systems and political analysis. Structure of the review would be similar to shorter RLRs – tabular with short narrative summary, as the timeline does not allow for comprehensive synthesis of data. Besides SLRs, primary studies and other evidence may be feasible in this timeframe, if obtained using the targeted searches in the most relevant databases. The review team should be larger, and standardized procedures for reviewing of the results and data extraction should be applied. In contrast to previous timeframe, merit review process may be feasible. For both timeframes, brief consultations with small transdisciplinary team should be conducted at the beginning and in the final stage of the review to discuss important matters.

For RLRs spanning several months, more comprehensive methodology may be adapted in terms of data synthesis and types of evidence. However, authors advise that review may be best conducted with a small review team in order to allow for more in-depth interpretation and iteration.

Studies analyzing methodology

There have been two interesting publications summarizing the results of Delphi consensus on the RLR methodology identified and included in this review [ 12 , 13 ].

Tricco et al. [ 13 ] first conducted an international survey and scoping review to collect information on the possible approaches to the running of rapid reviews, based on which, they employed a modified Delphi method that included inputs from 113 stakeholders to explore the most optimized approach. Among the six most frequent rapid review approaches (not all detailed here) being evaluated, the approach that combines inclusion of published literature only, a search of more than one database and limitations by date and language, study selection by one analyst, data extraction, and quality assessment by one analyst and one verifier, was perceived as the most feasible approach (72%, 81/113 responses) with the potentially lowest risk of bias (12%, 12/103). The approach ranked as the first one when considering timelines assumes updating of the search from a previously published review, no additional limits on search, studies selection and data extraction done by one reviewer, and no quality assessment. Finally, based on the publication, the most comprehensive RLRs can be made by moving on with the following rules: searching more than one database and grey literature and using date restriction, and assigning one reviewer working on screening, data extraction, and risk of bias assessment ( Table 1 ). Pandor et al. [ 12 ] introduced a decision tool for SelecTing Approaches for Rapid Reviews (STARR) that were produced through the Delphi consensus of international experts through an iterative and rigorous process. Participants were asked to assess the importance of predefined items in four domains related to the rapid review process: interaction with commissioners, understanding the evidence base, data extraction and synthesis methods, and reporting of rapid review methods. All items assigned to four domains achieved > 70% of consensus, and in that way, the first consensus-driven tool has been created that supports authors of RLRs in planning and deciding on approaches.

Six most frequent approaches to RLRs (adapted from Tricco et al. [ 13 ]).

Haby et al. [ 14 ] run searches of 11 databases and two websites and developed a comprehensive overview of the methodology of RLRs. With five SLRs and one RCT being finally included, they identified the following approaches used in RLRs to make them faster than full SLRs: limiting the number and scope of questions, searching fewer databases, limited searching of grey literature, restrictions on language and date (e.g., English only, most recent publications), updating the existing SLRs, eliminating or limiting hand searches of reference lists, noniterative search strategies, eliminating consultation with experts, limiting dual study selection, data extraction and quality assessment, minimal data synthesis with short concise conclusions or recommendations. All the SLRs included in this review were consistent in stating that no agreed definition of rapid reviews is available, and there is still no final agreement on the best methodological rules to be followed.

Gordon et al. [ 4 ] explained the advantages of performing a focused review and provided 12 tips for its conduction. They define focused reviews as ‘a form of knowledge synthesis in which the components of the systematic process are applied to facilitate the analysis of a focused research question’. The first tip presented by the authors is related to deciding if a focused review is a right solution for the considered project. RLRs will suit emerging topics, approaches, or assessments where early synthesis can support doctors, policymakers, etc., but also can direct future research. The second, third, and fourth tips highlight the importance of running preliminary searches and considering narrowing the results by using reasonable constraints taking into account the local context, problems, efficiency perspectives, and available time. Further tips include creating a team of experienced reviewers working on the RLRs, thinking about the target journal from the beginning of work on the rapid review, registering the search protocol on the PROSPERO registry, and the need for contacting authors of papers when data available in publications are missing or incongruent. The last three tips are related to the choice of evidence synthesis method, using the visual presentation of data, and considering and describing all the limitations of the focused review.

Finally, a new publication by Tricco et al. from 2022, describing JBI position statement [ 15 ] underlined that for the time being, there is no specific tool for critical appraisal of the RLR’s methodological quality. Instead, reviewers may use available tools to assess the risk of bias or quality of SLRs, like ROBIS, the JBI critical appraisal tools, or the assessment of multiple systematic reviews (AMSTAR).

Inconsistency in the definitions and methodologies of RLR

Although RLR was broadly perceived as an approach to quicken the conduct of conventional SLR, there is a lack of consensus on the formal definition of the RLR, so as to the best approaches to perform it. Only in 2021, a study proposing unified definition was published; however, it is important to note that the most accurate definition was only matching slightly over 50% of papers analysed by the authors, which underlines the lack of homogeneity in the field [ 11 ]. The evidence-based supporting methods are evolving, and more evidence is needed to define the most robust approaches [ 5 ].

Diverse terms are used to describe the RLR, including ‘rapid review’, focused systematic review’, ‘quick scoping reviews’, and ‘rapid evidence assessments’. Although the general principles of conducting RLR are to accelerate the whole process, complexity was seen in the methodologies used for RLRs, as reflected in this study. Also, inconsistencies related to the scope of the questions, search strategies, inclusion criteria, study screening, full-text review, quality assessment, and evidence presentation were implied. All these factors may hamper decision-making about optimal methodologies for conducting rapid reviews, and as a result, the efficiency of RLR might be decreased. Additionally, researchers may tend to report the methodology of their reviews without a sufficient level of detail, making it difficult to appraise the quality and robustness of their work.

Advantages and weaknesses of RLR

Although RLR used simplified approaches for evidence synthesis compared with SLR, the methodologies for RLR should be replicable, rigorous, and transparent to the greatest extent [ 16 ]. When time and resources are limited, RLR could be a practical and efficient tool to provide the summary of evidence that is critical for making rapid clinical or policy-related decisions [ 5 ]. Focusing on specific questions that are of controversy or special interest could be powerful in reaffirming whether the existing recommendation statements are still appropriate [ 17 ].

The weakness of RLR should also be borne in mind, and the trade-off of using RLR should be carefully considered regarding the thoroughness of the search, breadth of a research question, and depth of analysis [ 18 ]. If allowed, SLR is preferred over RLR considering that some relevant studies might be omitted with narrowed search strategies and simplified screening process [ 14 ]. Additionally, omitting the quality assessment of included studies could result in an increased risk of bias, making the comprehensiveness of RLR compromised [ 13 ]. Furthermore, in situations that require high accuracy, for example, where a small relative difference in an intervention has great impacts, for the purpose of drafting clinical guidelines, or making licensing decisions, a comprehensive SLR may remain the priority [ 19 ]. Therefore, clear communications with policymakers are recommended to reach an agreement on whether an RLR is justified and whether the methodologies of RLR are acceptable to address the unanswered questions [ 18 ].

Disclosure statement

No potential conflict of interest was reported by the author(s).

Solving partial differential equations using large-data models: a literature review

• Open access
• Published: 24 May 2024
• Volume 57 , article number  152 , ( 2024 )

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• Abdul Mueed Hafiz 1 ,
• Irfan Faiq 2 &
• M. Hassaballah 3 , 4  

Mathematics lies at the heart of engineering science and is very important for capturing and modeling of diverse processes. These processes may be naturally-occurring or man-made. One important engineering problem in this regard is the modeling of advanced mathematical problems and their analysis. Partial differential equations (PDEs) are important and useful tools to this end. However, solving complex PDEs for advanced problems requires extensive computational resources and complex techniques. Neural networks provide a way to solve complex PDEs reliably. In this regard, large-data models are new generation of techniques, which have large dependency capturing capabilities. Hence, they can richly model and accurately solve such complex PDEs. Some common large-data models include Convolutional neural networks (CNNs) and their derivatives, transformers, etc. In this literature survey, the mathematical background is introduced. A gentle introduction to the area of solving PDEs using large-data models is given. Various state-of-the-art large-data models for solving PDEs are discussed. Also, the major issues and future scope of the area are identified. Through this literature survey, it is hoped that readers will gain an insight into the area of solving PDEs using large-data models and pursue future research in this interesting area.

Avoid common mistakes on your manuscript.

1 Introduction

For scientific computing, differential equations (DEs) are efficient for description of various engineering problems (Boussange et al. 2023 ; Mallikarjunaiah 2023 ). Differential equations are first-order or higher derivatives of anonymous functions and are classified accordingly as ordinary differential equations (ODEs) or partial differential equations (PDEs) (Taylor et al. 2023 ; Farlow 2006 ). Unlike algebraic techniques, the approach establishes an equational relationship between unknown expressions and the related derivatives. Hence, the solutions should conform to the relationship. Practically, DEs are very important for depiction of complex problems occurring daily in nature (Namaki et al. 2023 ; Soldatenko and Yusupov 2017 ; Rodkina and Kelly 2011 ). Since PDEs are a very important type of DEs which relate to the issues addressed in this paper, we focus on PDEs. Some simplified PDEs have solutions using common operators (Melchers et al. 2023 ; Farlow 2006 ). Some popular techniques for PDE solving are the Finite element method (FEM) (Zienkiewicz and Taylor 2000 ), the Finite volume method (FVM) (Versteeg and Malalasekera 2011 ), Particle-based methods (Oñate and Owen 2014 ), and the Finite cell method (FCM) (Kollmannsberger 2019 ).

For higher order PDEs, a finite element method (FEM) may be conveniently used for PDE solving and can give an accurate solution using extensive computational resources (Innerberger and Praetorius 2023 ). Also, the multi-iteration solution limits practicality. As of now, solving PDEs is chiefly used for advanced applications like design of aircrafts using fluid dynamics, forecast of weather,... etc. For improving the PDE solving capability, some spline functions have been used (Kolman et al. 2017 ; Qin et al. 2019 ). On similar lines, FEM basically discretizes and approximates the PDE solution numerically. Hence, introducing Fourier transforms or Laplace transforms with higher efficiency for expressing PDEs has better feasibility (Mugler and Scott 1988 ; El-Ajou 2021 ). Large-data models have robust fitting abilities for functions which are multi-variate and high-dimensional, and have been developed for light-weight and rapid solving of PDEs. Before proceeding further, we will give a brief introduction to FEM, and discuss briefly the relevance and importance of neural network techniques in this regard. Next, we will enumerate the large-data models used for PDE solving. These models will be discussed in detail in the paper subsequently.

1.1 Finite element method (FEM)

The numerical approximation of the continuous field u of any PDE can be given by Eq. 1 on a certain domain and can be solved by different techniques including the Finite element method (FEM) (Zienkiewicz and Taylor 2000 ). FEM is discussed here with emphasis on the Galerkin-based FEM.

Let the PDE be given by Eq. 1 , where \(L(\cdot )\) is an arbitrary function of the continuous field u , and let it be defined on the domain \(\Omega \in \mathbb {R}^n\) , which is a set of all possible inputs for the PDE equation, along with boundary conditions (BCs) given by Eqs. 2 and 3 . Let \(y_d\) and g be the Dirichlet and Neumann BCs, respectively. The Dirichlet BC gives the numerical value that the variable u at the domain boundary assumes when solving the PDE. The Neumann BC assumes the derivative value of the variable u applied at the domain boundary \(\omega\) , as against to the variable u itself as in the Dirichlet BC. The Finite element formulation of Eq. 1 on a discrete domain having m elements and n nodes, including the BCs, will give the next system Eq. 4 .

In Eq. 4 , \(K(u^h)\) is the left-hand side matrix and is non-linear, and is known as the stiffness matrix . The stiffness matrix is a matrix that gives the system of linear equations to be solved for ascertaining the approximate solution to the PDE. \(u^h\) is the discretized solution field, and \(F \in \mathbb {R}^n\) is the right-hand side vector giving the forces applied, where \(F_i\) is the force at the \(i^{th}\) node. The equation system can be reduced to be as:

For obtaining the solution \(u^h\) , the Newton-Ralphson method can be used by linearizing \(r(u^h)\) and its tangent. This technique needs solving per iteration, an equation-system which is linear. The iterations keep proceeding till the residual norm \(||r||_n\) adjusts to the tolerance. For a linear operator, convergence is achieved in only one iteration. For excessive elements and nodes, the most computationally expensive FEM step is the one for finding the linear equation-system solution. For applications with critical computational efficiency like real-time models, digital twins, etc. This step needs to be avoided at all costs. Applications of techniques like model-order reduction, build a surrogate to significantly reduce the computational cost. Large-data based techniques like deep networks can do away with this cost completely. Large-data models like Convolutional neural networks have some notable merits for solving PDEs (Willard et al. 2022 ).

1.2 Large-data models for solving PDEs

With time, the popular large-data models like CNNs (Lecun et al. 1998 ; Krizhevsky et al. 2012 ; Hafiz et al. 2021 ) used in deep learning (Hassaballah and Awad 2020 ; Minaee et al. 2023 ; Xu et al. 2023 ; Xiang et al. 2023 ; Hafiz et al. 2022 ; Hafiz and Hassaballah 2021 ), Recurrent neural networks (RNNs) (Ren et al. 2022 ), Long short term memory (LSTM) neural networks (José et al. 2021 ), Generative adversarial networks (GANs) (Gao and Ng 2022 ; Yang 2019 ), and the attention-based Transformers (Cao 2021 ) have also been applied for solving PDEs. Deep learning is an area wherein neural networks with large number of layers are used for classification, regression, etc. Introduced by Lecun et al. ( 1998 ), Convolutional neural networks (CNNs) rose to popularity with Krizhevsky et al. ( 2012 ). AlexNet was a CNN that gave outstanding performance on the ImageNet dataset classification challenge (Vakalopoulou et al. 2023 ; Deng et al. 2009 ). At that time, obtaining a high classification accuracy on the ImageNet images dataset was considered a tough computer vision task. Since then, CNNs and deep learning have shattered many records on applications like computer vision (Hassaballah and Awad 2020 ; Hafiz and Bhat 2020 ; Hafiz et al. 2020 , 2023 ), speech recognition (Jean et al. 2022 ; Bhangale and Kothandaraman 2022 ), financial market forecasting (Zhao and Yang 2023 ; Ashtiani and Raahemi 2023 ), and for developing intelligent chatbots like the popular ChatGPT (Gordijn and ten Have 2023 ). Given the prowess of CNNs, it was only a matter of time before they were applied to tasks like solving PDEs, and demonstrated promising results. This success of CNN based PDE solving was due to their unique strengths like implementation simplicity for supervised learning, and consistency (Smets et al. 2023 ; Alt et al. 2023 ; Jiang et al. 2023 ).

CNNs have both strengths as well as weaknesses for solving PDEs (Michoski et al. 2020 ; Peng et al. 2023 ; Choi et al. 2023 ). The strengths of CNNs in this regard are:

Significant ease of implementing PDEs.

Convenience of using large data.

Consistent solutions over the full space of parameters.

As for (1), it can be said that highly complicated PDEs systems with a very large number of parameters and high dimensionality, can be implemented in Python Language using TensorFlow, and PyTorch in hundreds of code lines in a couple of days (Yiqi and Ng 2023 ; Quan and Huynh 2023 ). TensorFlow and PyTorch are CNN based Python Language code-libraries offering a rich set of functions encapsulating the state-of-the-art CNNs and their required data processing related programming sub-routines. This ease of implementation of CNNs as of now offers a convenient and accurate solution for PDEs. This is much easier than using many legacy solvers for PDEs (Kiyani et al. 2022 ). For (2), using data in the PDEs in supervised learning for CNNs is simple and so is the empirical integration (Jiagang et al. 2022 ; Fang et al. 2023 ; Fuhg et al. 2023 ). As for (3), one more important advantage is that the exploration of the parameter space , requires basic solution-domain augmentation (Ren et al. 2022 ) (i.e., the space-time parameter space, denoting the possible parameter values of: i) the active PDE variable, and ii) the time parameter) with more parameters like \((x,t, p_1, \cdots , p_n)\) , followed by optimization of the CNN for solving the PDE as a function of the input parameters. By augmenting the input-space, much lesser computational complexity is added to the algorithm as compared to solving the PDE in space-time ( x ,  t ) parameter values at only one parameter point \((p_1, \cdots , p_n)\) which is much easier than the sequential exploration of the parameter space points (Boussif et al. 2022 ; Tanyu et al. 2023 ). It must be noted that space-time refers to the ( x ,  t ) values, where x may be a PDE input variable like displacement, and t refers to the input time variable.

On the other side, some of the contemporary weaknesses of CNN-based PDE solvers are:

Absence of a guarantee for theoretical convergence of residuals for non-convex PDE minimization.

Overall slower run-time for each forward-solve.

Weak grounding of theoretical methods in analysis of PDEs.

As for (1), there is the challenge of optimization convergence in non-convex domains, wherein the solution may be trapped inside local minima (Shaban et al. 2023 ; Mowlavi and Nabi 2023 ). It may be noted that for a convex function, the global minimum is unique and any local minimum is also the global minimum. However, a non-convex function can have multiple local minima, which are function solutions where the function reaches a low value but this minimum may not be the globally lowest value. Hence, other minima or potentially better solutions may exist. As for (2) it is much more subtle as compared to its appearance and strongly depends on the different aspects of the CNN architecture used, such as hyperparameters optimization, ultimate simulation goal, etc (Tang et al. 2023b ; Grohs et al. 2023 ). With respect to (3), it merely indicates that CNNs have only now been seriously used for solving PDEs, and hence are theoretically untouched at large (Chen et al. 2023 ). In spite of the above weaknesses, having been inspired by the strengths of deep learning, large-data models have also been used for solving PDEs (Hou et al. 2023 ). Examples of CNNs (Lagaris et al. 1998 ) used for solving PDEs are Physics-informed neural networks (PINNs) (Raissi et al. 2019 ; Baydin et al. 2018 ), DeepONet (Lu et al. 2021b ), etc. Examples of other variants of large-data neural networks used are RNNs like PhyCRNet (Ren et al. 2022 ), LSTMs (José et al. 2021 ), GANs (Gao and Ng 2022 ), etc. Also, recently developed large-data models like Transformers (Cao 2021 ), and Deep reinforcement learning neural networks (DRLNNs) (Han et al. 2018 ) have also been used for solving PDEs. Through this literature survey it is hoped that the readers will get some insight into the area of using state-of-the-art large-data models and that they will be encouraged to engage in research in this interesting field. It is also hoped that by this discussion, future inroads into the merger of high-level mathematical modeling and large-data model based simulation and prediction will be laid.

The main contributions of this paper are summarized as follows.

A comprehensive survey paper is presented in the domain of solving PDEs, to help researchers review, summarize, solve challenges, and plan for future.

An overview of current trends and related techniques for solving PDEs using large-data models is given.

The major issues and future scope of using large-data models are also discussed.

The rest of the paper is organized as follows. Section 2 discusses the works related to using large-data models. Section 3 presents the current trends in the area. Section 4 provides the issues and future directions. Finally, the conclusion is given in Sect. 5 .

2 Related work

Solving differential equations with neural networks has been going on for some time (Huang et al. 2022 ). Traditionally, shallow neural networks were used. Shallow neural networks are the older generation of neural networks which have a few layers, and approximate a small number of parameters. One of the first works can be traced to the year 1990 (Hyuk Lee and In Seok 1990 ). In the same work, the first- and higher-order DEs were quantified by finitesimal approaches followed by using an energy function for the transformed algebraic functions. This energy function was subsequently minimized by using Hopfield networks. Since then there has been a lot of research on solving DEs using various models like neural networks, CNNs and recently other large-data models (Boussange et al. 2023 ). As reported by the dimensions online database (Hook et al. 2018 ) the total number of publications is 337 till date for the phrase search: ‘ PDE solving using neural networks OR CNNs OR deep learning OR RNN OR LSTM OR GANs OR Transformers OR DRL’ . The year-wise breakup of the number of publications obtained from the Dimensions online database, for the same phrase, is shown in Fig. 1 . Out of the search results, the important and ground-breaking works with notable impact, and citations, were considered for the current work. Also, those works were included in the current paper which had novelty, and a significant contribution to the field of PDE solving.

Total number of publications year-wise till date for the phrase search: ’PDE solving using neural networks OR CNNs OR deep learning OR RNN OR LSTM OR GANs OR Transformers OR DRL’ on the dimensions online database (Hook et al. 2018 )

2.1 Shallow neural networks for PDE solving

The mathematical modeling of physical problems can be efficiently incorporated by neural networks. The works of Meade Jr and Fernandez ( 1994a , b ) proved to be important for solving PDEs. In the work (Gobovic and Zaghloul 1993 ), a technique using local connections of neurons was proposed for solving PDEs of heat flow processes. Energy functions are formulated for the PDEs having constant parameters. The energy functions are minimized by using Very large scale integrated (VLSI) Complementary metal oxide (CMOS) circuits. The design of the CMOS circuits was implemented as a neural network with each neuron representing a CMOS cell. Later works like (Gobovic and Zaghloul 1994 ; Yentis and Zaghloul 1994 , 1996 ) used local neural networks for obtaining solutions of the PDEs by parallelization, and by Neural integrated circuits (NICs). The shallow neural networks initially paved the way for more work in the area of PDE solving. However, their weak approximation capabilities due to lesser number of hidden layers, led to the use of CNNs whose capability was better at capturing the numerous dependencies in PDEs. Hence the CNNs performed better than their shallow counterparts in PDE solving.

2.2 Deep neural networks for PDE solving

Deep learning using CNNs is a popular technique for computer vision tasks (Hassaballah and Awad 2020 ; Girshick et al. 2014 ; Girshick 2015 ; Ren et al. 2017 ; Sajid et al. 2021 ; Shelhamer et al. 2017 ; Chen et al. 2018 ; Zhao et al. 2017 ; Hafiz and Bhat 2020 ; Vinyals et al. 2015 ). Deep learning has also been used for tasks like Natural Language Processing (NLP) (Amanat et al. 2022 ). CNNs pre-trained on large datasets like ImageNet (Deng et al. 2009 ) are used after fine-tuning for two notable reasons (Jing and Tian 2021 ). First, the feature maps learned by CNNs from the large datasets help them to generalize better and faster. Second, pre-trained CNNs are adept at avoiding over-fitting during fine-tuning for smaller down-stream applications.

The accuracy of CNNs depends on their architecture (Hafiz et al. 2022 ; Hafiz and Hassaballah 2021 ) and the training technique (Hafiz et al. 2021 ). Many CNNs have been developed with huge numbers of parameters. For training these parameters, huge datasets are required. Some popular CNNs include AlexNet (Krizhevsky et al. 2012 ), VGG (Simonyan and Zisserman 2014 ), GoogLeNet (Szegedy et al. 2015 ), ResNet (He et al. 2016 ), and DenseNet (Huang et al. 2017 ). Popular CNN training datasets for computer vision include ImageNet (Deng et al. 2009 ) and OpenImage (Kuznetsova 2020 ). CNNs have achieved state-of-the-art classification performance for many computer vision tasks (Girshick et al. 2014 ; Shelhamer et al. 2017 ; Vinyals et al. 2015 ; Hassaballah and Hosny 2019 ; Ledig et al. 2017 ; Tran et al. 2015 ; Hafiz et al. 2020 , 2023 ).

2.2.1 CNNs for solving non-linear equations

As per the work of Lagaris et al. ( 1998 ), the Differential Equations can be broken down into sub-components using the Dirichlet and Neumann expressions. By using neural networks (Lagaris et al. 1998 ), non-linear equations were solved up to the seventh decimal digit. Since (Lagaris et al. 1998 ) is the first work to use CNNs for solving PDEs, it is worthy of being explained briefly. Considering a general differential equation given by Eq. 6 which needs a solution:

Here \({x} = (x_1, x_2, ..., x_n) \in \mathbb {R}^n\) for certain boundary conditions as per an arbitrary boundary S , \(B \subset \mathbb {R}^n\) is the defining domain and y ( x ,  t ) is the solution needed. It should be noted that we do not define the boundary conditions here because we are defining a general equation above.

For obtaining a solution to Eq. 6 , first the domain B has to be discretized into a set of points \(\hat{B}\) . Also the arbitrary boundary S (given here) of the general equation has to discretized into a set of points \(\hat{S}\) . Then, the DE may be expressed as a system which has constraints of the generally defined boundary conditions as per Eq. 7 :

Here y ( x ,  t ) is the solution. It can be obtained from two components given in Eq. 8 :

Here A ( x ) has fixed parameters, p is the parameter set, and N ( x ,  p ) is the neural network for minimization.

Although the initial CNNs used for solving PDEs gave promising results, they had certain issues. These included a lack of interpretability, weak adaptation of their structure to problems, and average performance (Ruthotto and Haber 2020 ; Uriarte et al. 2023 ).

2.2.2 Physics-informed neural networks (PINNs) for PDE solving

After taking inspiration from the works of Lagaris et al. ( 1998 ) and Hornik ( 1991 ), wherein CNNs were used for universal approximation, a new genre of CNNs was introduced wherein the physical constraints in the form of PDEs were added to the loss function, hence the name Physics-informed neural networks (PINNs) (Raissi et al. 2019 ; Baydin et al. 2018 ). More specifically, the technique involved applying the laws of physics expressed by PDEs as CNN loss functions. And in turn, the loss functions could be optimized for finding solutions (Maziar and George 2018 ). PINNs do not need discretization of the domains. They are also quite practical as the heavy computation is avoided. PINNs use minimization techniques for non-linear parametric PDEs of the form given by Eq. 9 :

Here y ( x ,  t ) is the solution which is hidden. \(N[\cdot ; \lambda ]\) is an operator of \(\lambda\) . \(\Omega\) belongs to \({\mathbb {R}^D}\) where D is the number of dimensions.

Using PINNs, Raissi et al. ( 2019 ) extensively studied complex dynamic processes like post-cylindrical flow, and aneurysm in Raissi et al. ( 2019 ),Raissi et al. ( 2020 ). Figure 2 shows the schematic for the PINN. Although PINN was a PDE-dedicated CNN model which led to better performance, it suffered from issues of other CNNs like lack of interpretability, need for large data and long training times (Mowlavi and Nabi 2023 ; Meng et al. 2023 ). In spite of these, it was partly successful as an expert system for solving PDEs. This was due to its PDE-dedicated framework (Tang et al. 2023a ; Jia et al. 2022 ).

Schematic of the Physics-informed neural network (PINN) used for solving PDEs. Huang et al. ( 2022 )

2.2.3 DeepONet CNN for PDE solving

Due to improved computing capability and availability of high-performance computing (HPC) systems, CNN implementation and training became convenient. Subsequently, different techniques which were previously difficult to implement, were implemented. Lu et al. considered Chen et al.’s non-linear operators (Chen and Chen 1995a , b ) as a theoretical basis for justifying the use of neural networks for operator learning. They came up with a robust CNN called DeepONet (Lu et al. 2021b ). As CNNs have superior expressibility and many special advantages e.g. in computer vision and in sequential analysis respectively, the authors used many CNNs in the DeepONet model for diversifying and targeting various net assemblies. Also, Lu et al. exploited a bifurcated parallel structure in their proposed CNN. Figure 3 shows the schematic for the DeepONet model. FEA-Net (Yao et al. 2019 ), Hierarchical deep-learning neural network (Lei et al. 2021 ), etc. are other examples of CNNs used for PDE solving.

Schematic of the DeepONet CNN model for approximation of the nonlinear operator G ( u )( v ) where u ( x ) and v are the variables of the PDE to be solved (Huang et al. 2022 )

2.2.4 Recurrent neural networks (RNNs) for PDE solving

Ren et al. ( 2022 ) proposed a PINN called physics-informed convolutional recurrent network (PhyCRNet) for solving PDEs. They used a convolutional encoder-decoder long short-term memory (LSTM) network. This network was used for low dimensional feature extraction and evolutional learning. Next, PDE residuals are used for the PDE solution estimation. The residual is a value which is obtained when we substitute the current estimate into the discretized PDE. If the PDE solution depends on time, then the residuals have to converge with every time step. They (Ren et al. 2022 ) used the PDE residual R(x,y,t, \(\theta\) ) given by Eq. 10 :

Here \({y \in \mathbb {R}^n}\) is the solution of the PDE in the temporal domain \(t \in\) [0,T], and in the physical domain \(\Omega\) . \(\textit{u}_t\) is the first order derivative. \(\nabla _x\) is the gradient for x and F is the non-linear function having parameter \(\lambda\) . It must be noted that F plays an important role in Eq. 10 because the residual is obtained by substituting the iterative input parameters in the PDE function.

The loss function L is the sum of squares of the residuals of the PDEs. For a 2D PDE system, L is given by Eq. 11 :

Here n and m are the height and the width in the spatial domain respectively. T is the total of time-steps and \(||\cdot ||_2\) is the \(l_2\) Norm.

The loss function was based on PDE residuals. The initial- and boundary-conditions were fixed inside the network. The model was enhanced by regressive as well as residual links which simulated time flow. They solved three types of PDEs using their model viz. 2D Burgers’ equation, the \(\lambda -\omega\) equation and the FitzHugh Nagumo equation. Promising results were obtained. Figure 4 gives the overview of the PhyCRNet architecture.

Schematic of the PhyCRNet model for solving PDEs. Each RNN cell consists of an encoder unit, a Long short term memory (LSTM) unit and a decoder unit. C and h are the cell and hidden states of the Convolutional long short term memory (ConvLSTM) units respectively. BC Encoding refers to the Boundary condition (BC) encoding wherein the BCs are enforced on the output variables. \({u_0}\) is the Initial condition (IC) and \({u_i}\) refers to the state variable for time-step \({t \in [1,T]}\) . Ren et al. ( 2022 )

RNNs are usually used for prediction of time-series problems. Their application to PDE solving in forms like PhyCRNet, marked a shift of technique and gave promising results. However, the performance of RNNs for solving PDEs is affected by issues like high complexity, narrow scope, long-training times, etc.

2.3 Long short term memory (LSTM) neural networks for PDE solving

In their work (José et al. 2021 ), Ferrandis et al. proposed prediction of naval vessel motion by PDEs using Long short term memory (LSTM) neural networks. The input to their LSTM model was the stochastic wave elevation for a particular sea state and the output of their LSTM model comprised of the vessel motions viz. pitch, heave and roll. Promising prediction results were obtained for the vessel motion for arbitrary wave elevations. They trained their LSTM neural networks using offline simulation and extended the prediction to online mode. Their objective function for minimization during training was the Mean squared error (MSE) given by Eq. 12 :

Here X is the observed value vector and \(\hat{X_i}\) is the predicted value vector.

Their work was modelled by the universal approximation theorem for functional problems. They claimed that their work was the first to implement such a model for real engineering processes. A schematic of the general LSTM neural network model is shown in Fig. 5 .

Schematic of the general Long-short term menory (LSTM) neural network illustrating the unfolding of the feedback loop which makes it suitable for sequential data processing. In the unfolded model \(x_i\) is input to the \(i^{th}\) cell, \(c_i\) is cell-state for \(i^{th}\) cell and \(h_i\) is the hidden-state of the \(i^{th}\) cell. The subscripts \(t-1\) , t and \(t+1\) represent three successive time-steps.Song et al. ( 2020 )

As is evident from the above, LSTM NNs are complex in nature. In spite of this, LSTM NNs are suitable for regression problems like time-series prediction tasks, due to their recurrent nature. However, LSTM NNs are used less in PDE solving. And as of now, few works of PDE solving using LSTM NNs are found. This issue is due to the complexity of modeling PDEs with LSTM NNs. In spite of this, some unique applications of LSTM NN based PDE solving have been proposed and promising results are being obtained.

2.4 Generative adversarial networks (GANs) for PDE solving

Generative adversarial networks (GAN) (Goodfellow et al. 2014 ; Gui et al. 2021 ; Yang 2019 ; Gao and Ng 2022 ) are machine learning algorithms that use deep learning for generation of new data. A GAN is made up of two NNs i.e., a generator and a discriminator. The generator and discriminator are trained together. The generator generates artificial data which imitates the real data, while the discriminator separates the data generated by the generator from the real data. GANs have become popular with tasks like image-morphing (Gui et al. 2021 ). In their work (Gao and Ng 2022 ), Gao and Ng proposed the physics informed GAN called Wassertein Generative Adversarial Network (WGAN), which was used for solving PDEs. These GANs use a unique function known as the Wassertein function for convergence. Wassertein function is suitable for PDE solving, and leads to convenient convergence for the latter. The usage of this function has paved the way for using GANs for PDE solving. They stated that GANs could be formulated in the general form given by Eq. 13 :

In Eq. 13 , G and F are the classes of the generator and the discriminator respectively. \(\pi\) is the distribution of the source and v is the distribution used for approximation. \(g_\theta\) and \(f_\alpha\) are the generator and discriminator functions respectively. x , z , \(\alpha\) , and \(\theta\) are the inputs to the PDE function to be solved. They form the input parameter-space to be explored for solving the PDE. \(\mathbb {E}\) is the energy function to be minimized for solving the PDE.

The authors of Gao and Ng ( 2022 ) showed that the generalization error to be minimized for obtaining the PDE solution, converged to the approximation error of the GAN model for large data, and they obtained promising results. Schematic for the WGAN used in their work is shown in Fig. 6 .

Schematic of the GAN. The GAN is trained till the discriminator which compares the real data x and the generated data G ( z ) cannot distinguish between the two (Wang et al. 2017 )

There are many types of GANs found in literature which have been applied diversely to engineering problems. The most popular application of GANs is image-morphing, though the former is not limited to the latter. Using GANs for solving PDEs comes as a unique application. This is because of the complexity in adapting GANs to solve specific framework problems like those of PDE solving. However as seen with WGANs, progress in this area is being made, and promising results have been obtained.

2.5 Transformers for PDE solving

Inspired by the initial work on Transformers (Vaswani et al. 2017 ), Shuhao Cao applied self-attention based Transformers to data driven learning for PDEs (Cao 2021 ). He used Hilbert space approximation for operators. He showed that soft-max normalization was not needed for the scaled dot product in attention mechanisms. He introduced a novel normalization layer which mimicked the Petrov-Galerkin projection for scalable propagation through the attention-based layers in Transformers. The Galerkin attention operator uses the best approximation of f in the \(l_2\) norm \(||\cdot ||_H\) as given by Eq. 14 :

Here H is the Hilbert space, and \(f \in H\) . \((\mathbb {Q},\mathbb {V})\) refer to the Query and Value subspaces used in the attention maps respectively. \(g_\theta (\cdot )\) is a learnable map of the Galerkin attention operator. \(f_h \in \mathbb {Q}_h\) is the best approximation of f in \(|| \cdot ||_H\) , \(b(\cdot , \cdot ):V \times Q \rightarrow \mathbb {R}\) is the continuous bilinear form and c is the boundary condition limit. \(y \in \mathbb {R}^{n \times d}\) is the current latent representation.

This novel technique helped the Transformer model to obtain a good accuracy in learning operators for un-normalized data tasks. The three PDEs he used for experimentation purposes were the Burgers’ equation, the Darcy flow interface process and a coefficient identification process. He called his improved Transformer as the Galerkin Transformer, which demonstrated a better cost of training and a better performance over the conventional counterparts. A schematic of Galerkin attention mechanism is shown in Fig. 7 .

Schematic of the Galerkin attention module used in the novel Transformer which is used to solve PDEs. Here ( Q ,  K ,  V ) are the (Query, Key and Value) matrices of the input to the attention module. \(K^T\) denotes the transpose of the Key matrix K . z is the attention-value matrix which is the output of the attention module. Cao ( 2021 )

Transformers were initially developed for Natural language processing (NLP) and were later on adapted to computer vision in the form of Visual transformers (ViTs). Their recent application to PDE solving is an encouraging step. This is because the huge number of training parameters in Transformers can effectively absorb the large number of dependencies in PDEs. However, Transformers have their own issues e.g. need for very large amount of training data, long training times, and those performances which still are not able to rival that of CNNs. Now, as more training data for semi-supervised, un-supervised and formula-driven supervised learning are becoming available, these issues are being addressed.

2.6 Deep reinforcement learning neural networks (DRLNNs) for solving PDEs

Deep reinforcement learning neural networks (DRLNNs) (Hafiz 2023 ; Hafiz et al. 2023 ) are deep networks using Reinforcement learning (RL) (Hafiz et al. 2021 ). DRLNNs have also been used for solving PDEs (Han et al. 2018 ). Han et al. ( 2018 ) proposed a deep learning based technique which was capable of solving high dimensional PDEs. They reformulated the PDEs using stochastic DEs and the solution gradient was approximated by deep neural networks using RL. Their backward stochastic differential equation (BSDE) played the role of model based RL and the solution gradient played the role of the policy function (Han et al. 2018 ). Considering the PDE given by Eq. 15 , its model showed promising results as shown in Table 1 . The PDE used for experimentation was a high dimensional (d = 100) Gobet and Tukedjiev equation from the work (Gobet and Turkedjiev 2017 ) given by Eq. 15 as:

where ( t ,  x ) are the temporal and spatial variables of the oscillating solution \(y^{*}(t,x)\) given by Eq. 16 as:

Here D is the dimensionality of the system and T is the time. In the above equations k =1.6, \(\lambda\) =0.1 and T =1.

It is observed from Table 1 that the SD is quite low and both the Mean error (%) and SD decrease with the increase in the number of layers in the deep network. This is testimony to the fact that large-data models like DRLNNs are quite capable of solving complex high dimensional PDEs. RL was initially limited to basic algorithms. Now it has developed into a substantial field of research having numerous techniques for many applications. With the development of DRLNNs, the fields of deep learning and RL were merged. However, there is not a perfect merger due to their respective unique natures. Also with RL there is the potential issue of RL systems going ‘rogue’ due to greed and harming their frameworks and environments. Nevertheless, DRLNNs offer solutions to many important modern day problems and are generally controlled (Raissi 2024 ; Siegel et al. 2023 ).

A graphical abstract of the above models for PDE solving is given in Fig. 8 in the form of a timeline. It can be observed from the timeline that the majority of large-data models for solving PDEs are neural networks. To summarize the main large-data models discussed in this work, the PDEs they solve, and the pros and cons of these models, we highlight the same in Table 2 .

Timeline for using large-data models

3 Current trends

There has been significant research on analyzing the generalization errors using techniques like PINNs (Mishra and Molinaro 2022 ; Penwarden et al. 2023 ) and DeepONet (Kovachki et al. 2021a ; Lanthaler et al. 2022 ). These techniques have very good performance. Lu et al. ( 2022a ) compared the efficiency of these two techniques. There is often a desire for incremental research based on existing methods like PINNs. The model architecture for neural network training for solving PDEs consists of input, neural net approximation, and the network loss function (Huang et al. 2022 ). A three pronged approach has been used for improving performance as discussed below.

Loss function: Improving the loss function is very useful for obtaining superior performance. Jagtap et al. ( 2020 ) improved the PINNs by using the cPINNs as well as the XPINNs (Jagtap and Karniadakis 2021 ). They used multiple domains and additional constraints. Kutz et al. and Patel et al. did the same by using parsimony (Nathan Kutz and Brunton 2022 ) and spatio-temporal schemes (Patel et al. 2022 ). Other studies have used the initial conditions and the boundary differently. For instance, Li et al. ( 2022 ) who fused partial integration and level set techniques.

Model used: Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Long short term memory (LSTM) neural networks, and even the attention based Transformers have been recently used and have given better performance (Ren et al. 2022 ; José et al. 2021 ; Cao 2021 ; Gao and Ng 2022 ; Meng et al. 2022 ) in solving PDEs. U-FNO with more U-Fourier layers (Wen et al. 2022 ) and the attention based model viz. FNO (Peng et al. 2022 ) have been developed. Also, research has been done for optimization of activation functions (Wang et al. 2021 ; Yifan and Zaki 2021 ; Venturi and Casey 2023 ).

Input: Kovacs et al. ( 2022 ) used parameters for determination of the eigen-value equation coefficients as additional inputs . Hadorn improved the DeepONet model by allowing the base function to shift and scale the input values (Patrik 2022 ). Lye et al. improved the ML technique accuracy by using a multi-level approach (Lye et al. 2021 ).

The state-of-the-art technologies have led to much better capability. Lu et al. ( 2021a ) crafted DeepXDE using PINNs and DeepONet. In the industry, NVIDIA the popular Graphics Processing Unit (GPU) manufacturing company has used PINNs, DeepONet, and enhanced finite numerical optimization techniques in a toolbox to build the Digital Twin. Also, the advances in quantum-based technology are favoring numerical optimization (Swan et al. 2021 ). In addition to these, more research (Wang et al. 2022 ) is being done on PDE solving by introduction of Gaussian processes (Chen et al. 2021 ), and use of hybrid Finite Element Method - Neural Network models (Mitusch et al. 2021 ). Li et al. have improved their previous works of Finite Numerical Optimization and have come up with a CNN model for operator learning (Kovachki et al. 2021b ). A notable aspect of the newly developed model is its robustness to discretization-invariance, leading to its use for a wider range of applications. As mentioned earlier, like the case of DeepONet (Lu et al. 2021b ), there is an effort to use the state-of-the-art deep learning models in this area. In spite of the fact that there are numerous new techniques, they share a common thing. That is, the boundary between theoretical process mechanism and experimental data is being dissolved. Lastly fusion of these two aspects has profoundly improved (Nelsen and Stuart 2021 ; Kadeethum et al. 2021 ; Gupta and Jaiman 2022 ; Gin et al. 2021 ; Bao et al. 2020 ; Jin et al. 2022 ; Lu et al. 2022b ). Using alternate techniques for developing large-data models like Transformers (Cao 2021 ) is also an interesting pointer to the potential to be unlocked in PDE solving. The diversity of large-data models available today also offers rich choices for solving PDEs and has many potential applications (Antony et al. 2023 ; Li et al. 2023 ; Shen et al. 2023 ).

4 Issues and future scope

The mixing of scientific computing with deep learning is very likely due to the advances in technology and research (Huang et al. 2022 ). However, this trend is reaching a flash point due to abundant computational resources warranting new research directions. Also, the gaps in the theoretical models and the experimental data, pose a problem and their elimination is difficult by conventional means. Further, in spite of the fact that CNNs have robust pattern recognition capabilities, their interpretability and inner working are not extensively researched. New techniques like partial integration and numerical optimization have tried to relate the earlier knowledge of PDEs and the new information of big-data from models like CNNs. Large-data models are often referred to as being ‘data-hungry’ due to the need for extensive training. As such there needs to be research on ways to augment training data without the need for huge amounts of ‘natural’ data. This is an open research area. Again, the interpretability and complexity raise issues, and addressing the same remains an open problem.

Techniques can be coarsely classified into iterative numerical techniques and machine learning based techniques (Psaros et al. 2023 ). Numerical analysis directly depicts the DE mechanism, while as other ML techniques use probabilistic expressions for data characterization. However, for handling specific PDE solving, approximation can also be used. An example is the parameter approximation used in CNNs for PDEs. Also, special CNNs like DeepONet may be used for functional description of the large-data physical models. These techniques are promising for solving PDEs. Hence using different large-data models can also benefit by their respective strengths. In addition to using neural networks and bifurcated structures, different models may be used (Jagtap et al. 2022 ; Sirignano and Spiliopoulos 2018 ; Gupta et al. 2021 ). Also, the reverse path can be used in dynamical system pattern application for model enhancement. This can lead to substantial benefits for model learning interpretability. As mentioned above, one interesting area is the generation of training data for ‘data-hungry’ models without the use of manual collection. An example of this is mathematical formula-based generation of training data, e.g., by Formula-driven supervised learning or FDSL (Hafiz et al. 2023 ).

5 Conclusion

In this review paper, an overview of solving Partial differential equation (PDE) using large-data models was given. An introduction to the area was presented along with its publication trends. This was followed by a discussion of various techniques used for solving PDEs using large-data models. The large-data models discussed included Convolutional neural networks (CNNs), Recurrent neural networks (RNNs), Long-short term memory (LSTM) neural networks, Generative adversarial networks (GANs), attention-based Transformers and the Deep reinforcement learning neural networks (DRLNNs). The pros and cons of these techniques were discussed. A trend timeline for the purpose was also given. Then, the major issues and future scope in the area were discussed. Finally, we hope this literature survey becomes a quick guide for the researchers and motivates them to consider using large-data models to solve significant problems in PDE based mathematical modeling.

Data availibility

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

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Hafiz, A.M., Faiq, I. & Hassaballah, M. Solving partial differential equations using large-data models: a literature review. Artif Intell Rev 57 , 152 (2024). https://doi.org/10.1007/s10462-024-10784-5

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HbA1c changes in a deprived population who followed or not a diabetes self-management programme, organised in a multi-professional primary care practice: a historical cohort study on 207 patients between 2017 and 2019

• Sarah Ajrouche 1 ,
• Lisa Louis 1 ,
• Maxime Esvan 2 ,
• Anthony Chapron 1 , 2 ,
• Ronan Garlantezec 3 &
• Emmanuel Allory 1 , 2 , 4  

BMC Endocrine Disorders volume  24 , Article number:  72 ( 2024 ) Cite this article

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Metrics details

Diabetes self-management (DSM) helps people with diabetes to become actors in their disease. Deprived populations are particularly affected by diabetes and are less likely to have access to these programmes. DSM implementation in primary care, particularly in a multi-professional primary care practice (MPCP), is a valuable strategy to promote care access for these populations. In Rennes (Western France), a DSM programme was designed by a MPCP in a socio-economically deprived area. The study objective was to compare diabetes control in people who followed or not this DSM programme.

The historical cohort of patients who participated in the DSM programme at the MPCP between 2017 and 2019 ( n  = 69) was compared with patients who did not participate in the programme, matched on sex, age, diabetes type and place of the general practitioner’s practice ( n  = 138). The primary outcome was glycated haemoglobin (HbA1c) change between 12 months before and 12 months after the DSM programme. Secondary outcomes included modifications in diabetes treatment, body mass index, blood pressure, dyslipidaemia, presence of microalbuminuria, and diabetes retinopathy screening participation.

HbA1c was significantly improved in the exposed group after the programme ( p  < 0.01). The analysis did not find any significant between-group difference in socio-demographic data, medical history, comorbidities, and treatment adaptation.

Conclusions

These results, consistent with the international literature, promote the development of DSM programmes in primary care settings in deprived areas. The results of this real-life study need to be confirmed on the long-term and in different contexts (rural area, healthcare organisation).

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Introduction

Diabetes is a chronic disease that has doubled in prevalence in the last three decades [ 1 ] and is now one of the ten first causes of death worldwide [ 2 ]. Currently, 463 million people have diabetes worldwide (4.5 million in France) and this number could rise to 700 million by 2045 [ 3 ]. Diabetes incidence has increased dramatically, particularly that of type 2 diabetes mellitus that accounts for 90% of all cases [ 4 ]. Diabetes is associated with high morbidity index and altered quality of life [ 5 , 6 , 7 ]. Its prevalence has particularly increased in low-income and disadvantaged socio-economic groups [ 8 , 9 , 10 ], and even more in developed countries [ 11 ]. Its prevalence was twice as high in people receiving universal health coverage (UHC) [ 8 ] in whom it was also associated with worse glycaemic control [ 12 ] and more complications [ 13 , 14 , 15 ]. Higher diabetes prevalence was also found in some immigrant populations. For example, in metropolitan France, the risk of diabetes is 2.5 times higher in women who came from a North African country than in non-immigrant women [ 9 ]. Therefore, the population’s contextual and cultural characteristics need to be considered when developing preventive actions, such as Diabetes Self-Management (DSM) programmes [ 16 , 17 ].

DSM education brings together the knowledge and skills that make people more aware about their health and their health choices by offering specific training, support and coaching [ 18 ]. DSM education enables people with diabetes to acquire and maintain skills to manage diabetes, resulting in quality of life improvement, increasing active role with the healthcare providers (HCP), and better adherence to treatment/follow-up and prevention of complications [ 19 , 20 ]. The objective of DSM education is to make patients more autonomous and to produce a complementary effect to the usual pharmacological interventions [ 19 ]. It is an ongoing process, adapted to the disease course and the patient's lifestyle [ 21 ]. Although their effectiveness is acknowledged, particularly for type 2 diabetes mellitus [ 22 , 23 , 24 ], participation in DSM programmes in group settings is still limited among people with diabetes [ 25 ], especially in deprived populations. This difficult access is partly explained by their living conditions and socio-cultural background that complicate access to programmes and the will to change lifestyle habits [ 26 ]. Another explanation is that the current DSM programmes were not developed by taking into account the social and cultural background of the targeted populations [ 27 ].

The accessibility issues to DSM programmes and the obstacles to DSM practice are a major research topic [ 28 , 29 ]. Furthermore, the fact that DSM education is mostly organised in hospitals [ 30 , 31 ] may constitute an additional obstacle [ 32 ]. In 2014, in France, only 3.9% of self-management programmes were run in primary care settings, compared with 82% in a hospital structure [ 18 ]. Primary care now appears to be the preferred place for promoting access to care and reducing social inequalities in health [ 27 ]. Multi-professional Primary Care Practices (MPCP) bring together medical/paramedical professionals and social services around a common health project to improve inter-professional collaboration and access to care for the population [ 33 ]. Therefore, they seem suitable places for developing prevention programmes due to their accessibility based on their geographical position, relational proximity with the habitants, better cultural knowledge by the HCP and capacity to break down social isolation [ 34 ]. MPCPs are an opportunity to integrate DSM education in primary care and they could become reference structures in this field [ 35 , 36 ].

In Rennes, the Villejean district is one of the five socio-economically deprived areas of the city. The median income is estimated at 670 euros (vs 1628 euros in the whole city), 38.3% of the population is unemployed, and 51% of < 20-year-old people receive UHC [ 37 ]. In 2015, 71 HCPs of this district decided to create the "Rennes Nord-Ouest" MPCP and developed a collective DSM programme for their patients with diabetes (supplementary files 1 and 2). In accordance with the recommendations, DSM programmes must be evaluated [ 18 ]. The value of this programme was initially demonstrated from the users’ point of view [ 34 ]. This qualitative study in 2020 also showed that in the first year of the DSM programme, participants were from nine different countries and 80% were considered as socio-economically deprived. This assessment must be continued by including quantitative biomedical parameters, as described in the international literature [ 38 ]. In Europe, several randomised controlled trials have demonstrated the benefit of group DSM for improving glycaemic control in non-deprived populations, such as the X-PERT study [ 39 ] and the DESMOND study [ 40 ]. In the United States, two randomised control trials carried out by community health workers in clinics found a significative effect of DSM programmes among socially deprived immigrant people with diabetes [ 41 , 42 ]. However, we did not find any study on similar interventions for deprived people carried out in MPCPs.

The main objective of this study in a socio-economically deprived area was to compare diabetes control in a group that participated in a DSM programme run by an MPCP and in a group that did not receive this intervention.

Study design

This was an historical exposed/non-exposed cohort study to assess the effect of a DSM intervention in primary care, carried out by a MPCP located in a socio-economically deprived area of Rennes, France.

Description of the intervention

The programme targeted ≥ 18-year-old people with diabetes to improve or develop self-care skills and change their eating habits. The DSM programme was designed and implemented by the "Rennes Nord-Ouest" MPCP, in the Villejean district, Rennes, France, in 2017. Patients were included in the programme upon suggestion by one of the MPCP HCPs involved in their care (e.g. general practitioner (GP), nurse, pharmacist, chiropodist), even if their own GP was not working at the MPCP. HCP of the MPCP recruited participants during their usual consultations. Refusal to participate was not recorded. Only interested patients had a BEPI (Bilan educatif partagé initial, patient-centred educational assessment) (supplementary file 3) with a HCP of the team before the DSM programme start to fix personal objectives that were used to prepare a personalized attendance programme to the different workshops.The programme consisted of seven to nine workshops that lasted 1–2 h and were held on weekdays between 9am and 5pm over a period of 1–2 months. The MPCP received annual funding from the local health authority (Agence régionale de santé) to cover the intervention running costs, and the training and remuneration of the involved HCPs.

Exposed and non-exposed groups

The exposed group (receiving the intervention) included ≥ 18-year-old patients with type 1 or type 2 diabetes who were followed by at least one HCP in the MPCP and who participated in the DSM programme between 2017 and 2020. All the 75 patients who participated in the programme (at least BEPI completion) were eligible. If some had participated in more than one annual session, only their first participation was considered.

The non-exposed group included all the patients selected from the SOPHIA database of the GPs whose patients were in the exposed group. SOPHIA is a free diabetes support service set up by the French public health insurance in 2008 to offer remote coaching (emails, personal online space, and telephone follow-up with a nurse) adapted to the needs of people with diabetes in order to help them live better with their disease. This service was offered to all patients at the MPCP (i.e. people in the exposed and non-exposed groups). The SOPHIA database includes ≥ 18-year-old patients with type 1 and 2 diabetes who are registered with a GP, have long duration disease (LDD) status for diabetes, are affiliated to the public health insurance, and had at least three prescriptions for anti-diabetic drugs in the year of the intervention.

Each patient in the exposed group was randomly matched to two control patients based on sex (male or female), diabetes type (type 1 or type 2), year of birth (before 1960 or after; median calculated in the exposed group) and whether their GP was a MPCP member. The intervention date was the BEPI date.

The exclusion criteria for the exposed and non-exposed groups were: GP’s or patient’s refusal to participate in the study, patients unable to read and write in French, lack of follow-up during the study period (patient arrived at the practice after the intervention date, or left before), haemoglobinopathy that does not allow HbA1c monitoring, gestational diabetes, and drug-induced diabetes.

Study endpoints

The primary outcome was glycated haemoglobin change (HbA1c in %) between 12 months before and 12 months after the intervention start date (i.e. the BEPI date).

Secondary outcomes were modifications in diabetes treatment, body mass index (BMI; in kg/m2), systolic and diastolic blood pressure (in mmHg), lipid profile (low density lipoprotein C, LDLc, in mmol/L), microalbuminuria, and screening for diabetic retinopathy between before and after the intervention.

Data collection

Data were collected by two residents in general practice in 11 practices (21 GPs who followed the participants) after the intervention, between March and December 2021. Data were extracted from computerised medical records (consultations with clinical examination, laboratory work-up results, and specialist letters) from the practice professional software. Data were collected for the years 2017 to 2020, and as close as possible to the target dates (12 months before and 12 months after the intervention) to obtain at least two distinct values, particularly in terms of kidney function, lipid levels and microalbuminuria.

To characterise the two groups, each patient’s socio-demographic data (year of birth, sex, profession, education level, and socio-professional categories) and medical history (diabetes type and duration, other associated LDD) were collected. Concerning chronic treatment, prescriptions close to the target dates were identified to determine the diabetes treatments (metformin, other oral drugs, GLP-1 analogues, or insulin). Prescriptions for statins, angiotensin converting enzyme inhibitors, or related drugs were also retained.

Lastly, mentions of ophthalmological consultations (specialist’s letters or key words) were searched in the different consultations within the study interval.

Statistical analysis

Patient characteristics were expressed as n (%) for categorical variables and mean ± standard deviation (SD) for continuous variables. For univariate comparison between (exposed and non-exposed) groups, the Student’s t or Mann–Whitney-Wilcoxon’s test was used for continuous variables and the χ2 or Fisher’s exact test for categorical variables.

Outcome changes over time were analysed using generalised linear mixed models. A sensitivity analysis was performed for the primary outcome using a model adjusted for sex, age, BMI, and education level. Multiple imputation was used to account for missing values. Fifty imputed datasets were created and combined using standard between/within-variance techniques. Statistical analyses were computed at the two-sided α level of 5% with SAS version 9.4 (SAS Institute, Cary, North Carolina, USA).

Ethical aspects and legislation

This study was approved by the Rennes University Hospital ethics committee on 14 June 2021 (Number 21.77–2, supplementary file 5). It complied with the reference methodology MR-004 defined by the French committee on personal data protection (Commission Nationale Informatique et Libertés; CNIL) and with the European General Data Protection Regulation (GDPR).

Among the 75 patients who completed a BEPI between 2017 and 2019, 24 GP’s were identified. Three GP’s refused to participate; each of them had one patient who had the BEPI. As three other patients with a BEPI refused to participate to the study, the exposed group was composed of 69 patients (Fig.  1 ). In the SOPHIA database, 488/560 patients followed by the GPs of the patients in the exposed group did not participate in the intervention. Therefore, a participation rate of 13% to the DSM programme could be estimated. Among them, 149 were selected by random 2:1 matching. After excluding 11 patients, 138 patients were included in the non-exposed group. With the 69 patients of the exposed group, 207 patients were included in the study.

Description of the study population (Table  1 )

The analysis did not find any significant difference between groups concerning socio-demographic characteristics, age at diabetes diagnosis [49 (± 12) years for the exposed group and 49 (± 13) years for the non-exposed group], and percentage of patients with diabetes discovered < 1 year before the intervention date [ n  = 13 (19.1%) for the exposed group and n  = 22 (17.3%) for the non-exposed group]. Education level and percentage of retired patients [ n  = 29 (42%) for the exposed group and n  = 43 (37.7%) for the non-exposed group] were comparable between groups. Presence of another known LDD [ n  = 29 (42%) in the exposed group and n  = 57 (41.3%) in the non-exposed group], mean number of LDDs per patient and their nature, and comorbidities (hypertension, dyslipidaemia, known diabetic nephropathy, known diabetic retinopathy or obesity) were not significantly different between groups.

Pre-intervention data (Table  2 )

Pre-intervention weight, BMI and blood pressure were not significantly different between groups. Among treatments, only prescription of GLP-1 analogues was higher in the exposed group than non-exposed group [ n  = 12 (17.6%) vs n  = 6 (4.3%); p  = 0.01]. Among laboratory data, the mean HbA1c level was significantly higher in the exposed than non-exposed group [8.3% ± 2.2 vs 7.1% ± 1.2; p  < 0.01], and more patients had nephropathy with microalbuminuria in the exposed than non-exposed group [ n  = 19 (33.9%) vs n  = 17 (17.9%); p  = 0.02]. Adherence to the annual ophthalmological follow-up was higher in the exposed than non-exposed group [ n  = 39 (72.2%) vs n  = 48 (44.4%); p  < 0.01].

Post-intervention changes (Table  3 , Fig.  2 )

HbA1c (%) change over time (24 months) in the exposed and non-exposed groups

After the intervention, the mean HbA1c decreased by 0.73% [-1.13; -0.33] in the exposed group and increased by 0.35% [0.07; 0.63] in the non-exposed group ( p  < 0.01) (primary endpoint). All the secondary endpoints were similar between groups (supplementary file 6). In the secondary analyses, HbA1c change difference in the two groups after exclusion of patients with type 1 diabetes was still significant ( p  < 0.01) and remained also after the sensitivity analysis adjusted for sex, age, BMI and education level ( p  < 0.01).

The main result of our study is the significant difference in HbA1c change ( p  < 0.01) between the exposed group and the non-exposed group at 12 months post-intervention (i.e. DSM programme). This result is consistent with the literature. The systematic review by Odgers-Jewell et al. found that DSM education in groups efficiently reduced HbA1c by 0.3% at 12 months and up to 36 months [ 38 ]. Like in our study, there was no significant difference in BMI, blood pressure and LDLc change between exposed and non-exposed groups during the same period. The TIME randomised controlled trial on the long-term effectiveness of a programme for low-income populations in Houston community clinics found improvements in HbA1c at 12, 18 and even 24 months post-intervention [ 43 ]. Compared with the exposed group, HbA1c level in the non-exposed group (conventional medical follow-up) worsened. Similarly, the randomised controlled trial by Trento et al. [ 44 ] showed a progressive increase over 5 years in the HbA1c of controls compared with individuals receiving group DSM education in a hospital. In our study, the pre-intervention HbA1c and microalbuminuria were significantly higher in the intervention group, suggesting that patients who participated in the programme had more unbalanced and complicated diabetes. Hadjiconstantinou et al. found that patients with higher HbA1c (> 7%) benefit more from DSM programmes, as observed for our participants [ 29 ]. In this perspective article, the authors stressed that better outcomes were observed in groups that included participants with higher baseline HbA1c, younger age (< 65 years), and a higher proportion of ethnic minorities, like in our population. The lack of significant between-group difference in HbA1c and microalbuminuria after the intervention (supplementary file 6), combined with the analysis of variance for HbA1c, may indicate that the DSM intervention has a catch-up effect between groups, bringing both populations to same level. Indeed, while HbA1c decreased by 0.73% [-1.13; -0.33] in the exposed group, it increased by 0.35% [0.07; 0.63] in the non-exposed group ( p  < 0.01). Insulin prescription alone cannot explain this result because changes in insulin prescription were similar between groups ( p  = 0.54) and the HbA1c change difference remained also after the subgroup analysis adjusted for insulin prescription ( p  < 0.01). One hypothesis to be considered is that HCPs might have preferentially proposed the DSM programme to patients with badly controlled diabetes, although this was not an objective of the programme. In an interdisciplinary literature review, Carey et al. suggested the concept of " proportionate universalism " according to which health actions should be universal, but with a scale and intensity proportionate to the patients’ disadvantage level [ 45 ]. " Proportionate universalism " would be a way to move towards more equity in health by rebalancing situations without stigmatising population groups. Continuity of care in general practice allows practitioners to reduce social inequalities in health. Gray et al., in a systematic review of observational studies between 1996 and 2017, highlighted that increased continuity of care by doctors is associated with lower mortality rate in their patients [ 46 ]. Similarly, Sandvik et al. described the GP’s contribution to the life expectancy of their patients through the implementation of informal (access to all the patient's information), longitudinal (transcending the various disease episodes), and interpersonal (the relationship of trust established between patient and GP) continuity [ 47 ].

Another important finding in our study was the significant higher adherence to the ophthalmological follow-up in the exposed group than in the non-exposed group (72.2% versus 44% before the intervention and 72% versus 38.1% after the intervention). This may be explained by a closer follow-up of patients in the exposed group by their GP/other HCPs. However, this does not seem to have had an effect on baseline HbA1c that was higher in the exposed group. Additionally, our exposed group may have had a lower level of health literacy (i.e. the set of individual and environmental conditions for a patient to understand and process health information) [ 48 ]. This could explain why the GP better followed these patients and, for instance, might have been more likely to ask the secretary of the practice to organise an appointment with the specialist rather than delegating this task directly to the patient. According to the French national health council (Haut Conseil de la Santé Publique; HCSP), " people with low literacy level are 1.5 to 3 times more likely to be in unfavourable health conditions than people with higher literacy level " [ 27 ]. This could explain the initial difference in HbA1c level between groups. A qualitative study on the health literacy level of participants in a DSM programme in a socio-economically deprived area of Montpellier (south of France) highlighted the diversity of health literacy profiles that coexisted in that area [ 49 ]. Moreover, low health literacy is more likely to be observed among people with low income, belonging to ethnic minorities, or migrant populations [ 27 ]. Our exposed group included mainly patients from a practice in an area with elevated socio-economic difficulties and consequently people with more precarious profiles.

Strengths and limitations

To our knowledge, this is the first French study that evaluated the effect on HbA1c of a DSM intervention carried out by an MPCP in a socio-economically deprived area. Another of its strengths is that patients were from different general practices in this deprived area and their medical records were fully accessible. Moreover, our exclusion criteria included absence of follow-up during the study period or the presence of a pathology that did not allow HbA1c monitoring. The aim was to optimise data collection, especially for the primary outcome (HbA1c changes). Our study also has several limitations including missing data, potential residual cofounding, and potential selection bias. First, data were missing for some variables, especially education level and participation rate. Education level is not routinely collected in medical records. We assumed that this variable was missing at random and consequently we used the multiple imputation method to deal with this issue. The obtained results were in accordance with the main analysis. Second, other information (e.g. private health insurance status, marital and family situation, country of birth, understanding of written French, financial situation) was not present in the medical records. These missing data would have allowed matching the two groups also for these socio-economic variables. In our opinion, to develop research in primary care in France, the healthcare organisation needs to think how the patients’ socio-economic data could be collected using the GP’s professional software tools. Moreover, the study retrospective nature did not allow collecting other potential cofounding variables, for instance participation in other DSM programmes or individual data about deprivation for both groups. In addition, we used a logistic regression to take into account potential confounding factors collected in our study. Alternatively, we could have used a propensity score to take into account the non-random allocation of the intervention in our study. However, the performance of these two methods is similar in observational studies [ 50 , 51 , 52 ]. Lastly, we did not know why some patients with diabetes followed at this MPCP did not participate in the DSM programme (refusal rate and reasons for this choice). Therefore, we could not exclude, in addition to a possible reversion to the mean, a selection bias because our exposed group may constitute a subgroup of the population with diabetes more committed to better control their HbA1c.

Our findings suggest that HbA1c improved after participation in a DSM programme led by an MPCP in a socio-economically deprived area. This needs to be confirmed by a prospective study, but it should already encourage the development of DSM targeted to deprived populations in primary care.

Availability of data and materials

The datasets used and analysed in the current study are available from the corresponding author on reasonable request.

Abbreviations

Angiotensin Converting Enzyme Inhibitor

Angiotensin II Receptor Antagonist

Agence Régionale de Santé (Local Health Authority)

Bilan Educatif Partagé Initial (Initial patient-centred educational assessment)

Body Mass Index

Chronic Kidney Disease Epidemiology

Commission Nationale de l’Informatique et des Libertés (French Committee on Data Protection)

Complémentaire Santé Solidaire

Diastolic Blood Pressure

Diabetes Self-Management

General Data Protection Regulation

Glomerular Filtration Rate

Glucagon-Like Peptide-1

General Practitioner

Haute Autorité de Santé (French Health Authority)

Glycated Haemoglobin fraction A1c

HealthCare Provider

Haut Conseil de la Santé Publique (National Health Council)

Institut National de la Statistique et des Etudes Economiques (National institute of statistic and economic studies)

Long Duration Disease

Low Density Lipoprotein C

Multi-professional Primary Care Practice

Type two Diabetes Mellitus

Randomised Controlled Trial

Règlement Général sur la Protection des Données (General data protection framework)

Systolic Blood Pressure

Universal Health Coverage

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Acknowledgements

We thank the Rennes Nord-Ouest primary care practice (managed by the association “Avenir Santé Villejean Beauregard”). We thank all the study participants and their GPs who gave their consent to the use of their health data. We thank the French network of University Hospitals HUGO (‘Hôpitaux Universitaires du Grand Ouest’) that supported this article.

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Department of General Practice, Univ Rennes, 2, Avenue du Pr Léon Bernard, RENNES Cedex, 35043, France

Sarah Ajrouche, Lisa Louis, Anthony Chapron & Emmanuel Allory

CHU Rennes, Inserm CIC 1414 (Centre d’Investigation Clinique), Rennes, 35000, France

Maxime Esvan, Anthony Chapron & Emmanuel Allory

CHU de Rennes, Univ Rennes, Inserm, EHESP (Ecole des Hautes Etudes en Santé Publique), Irset - UMR_S 1085, Rennes, 35000, France

Ronan Garlantezec

LEPS (Laboratoire Educations et Promotion de la Santé), University of Sorbonne Paris Nord, UR 3412, Villetaneuse, F-93430, France

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SA, LL, ME, EA: Substantial contributions to the conception and design of the work SA, LL: data acquisition SA, LL, ME, EA: data analysis SA, LL, ME, RG, EA: data interpretation SA, LL, ME, RG, EA: drafted the manuscript SA, LL, ME, AC, RG, EA: substantively revised it.

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Correspondence to Emmanuel Allory .

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Each participant was given an information letter and informed consent was obtained (supplementary file 4). This study was approved by the Rennes University Hospital ethics committee on June 14, 2021 (Number 21.77–2) (supplementary file 5). All procedures performed in the study involving human participants were in accordance with the national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Ajrouche, S., Louis, L., Esvan, M. et al. HbA1c changes in a deprived population who followed or not a diabetes self-management programme, organised in a multi-professional primary care practice: a historical cohort study on 207 patients between 2017 and 2019. BMC Endocr Disord 24 , 72 (2024). https://doi.org/10.1186/s12902-024-01601-9

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Received : 27 March 2024

Accepted : 07 May 2024

Published : 20 May 2024

DOI : https://doi.org/10.1186/s12902-024-01601-9

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