peer reviewed articles on higher education

The Review of Higher Education

Penny A. Pasque, The Ohio State University; Thomas F. Nelson Laird, Indiana University, Bloomington

Journal Details

The Review of Higher Education  is interested in empirical research studies, empirically-based historical and theoretical articles, and scholarly reviews and essays that move the study of colleges and universities forward. The most central aspect of  RHE  is the saliency of the subject matter to other scholars in the field as well as its usefulness to academic leaders and public policymakers. Manuscripts submitted for  RHE  need to extend the literature in the field of higher education and may connect across fields and disciplines when relevant. Selection of articles for publication is based solely on the merits of the manuscripts with regards to conceptual or theoretical frameworks, methodological accurateness and suitability, and/or the clarity of ideas and gathered facts presented. Additionally, our publications center around issues within US Higher Education and any manuscript that we send for review must have clear implications for US Higher Education. 

Guidelines for Contributors

Manuscripts should be typed, serif or san serif text as recommended by APA 7th edition (e.g., 11-point Calibri, 11-point Arial, and 10-point Lucida Sans Unicode, 12-point Times New Roman, 11-point Georgia, 10-point Computer Modern) double-spaced throughout, including block quotes and references. Each page should be numbered on the top right side of the page consecutively and include a running head. Please supply the title of your submission, an abstract of 100 or fewer words, and keywords as the first page of your manuscript submission (this page does not count towards your page limit). The names, institutional affiliations, addresses, phone numbers, email addresses and a short biography of authors should appear on a separate cover page to aid proper masking during the review process. Initial and revised submissions should not run more than 32 pages (excluding abstract, keywords, and references; including tables, figures and appendices). Authors should follow instructions in the 7th edition Publication Manual of the American Psychological Association; any manuscripts not following all APA guidelines will not be reviewed. Please do not change fonts, spacing, or margins or use style formatting features at any point in the manuscript except for tables. All tables should be submitted in a mutable format (i.e. not a fixed image). Please upload your manuscript as a word document. All supporting materials (i.e., tables, figures, appendices) should be editable in the manuscript or a separate word document (i.e., do not embedded tables or figures). For a fixed image, please upload a separate high-resolution JPEG.

Authors should use their best judgment when masking citations. Masking some or all citations that include an author’s name can help prevent reviewers from knowing the identities of the authors. However, in certain circumstances masking citations is unnecessary or could itself reveal the identities of manuscript authors. Because authors are in the best position to know when masking citations will be effective, the editorial team will generally defer to them for these decisions.

Manuscripts are to be submitted in Word online at  mc.manuscriptcentral.com/rhe . (If you have not previously registered on this website, click on the “Register here” link to create a new account.) Once you log on, click on the “Author Center” link and then follow the printed instructions to submit your manuscript.

The term “conflict of interest” means any financial or other interest which conflicts with the work of the individual because it (1) could significantly impair the individual’s objectivity or (2) could create an unfair advantage for any person or organization. We recommend all authors review and adhere to the ASHE Conflict of Interest Policy before submitting any and all work. Please refer to the policy at  ashe.ws/ashe_coi

Please note that  The Review of Higher Education  does not require potential contributors to pay an article submission fee in order to be considered for publication.  Any other website that purports to be affiliated with the Journal and that requires you to pay an article submission fee is fraudulent. Do not provide payment information. Instead, we ask that you contact the  RHE  editorial office at  [email protected]  or William Breichner the Journals Publisher at the Johns Hopkins University Press  [email protected] .

Author Checklist for New Submissions

Page Limit.  Manuscripts should not go over 32 pages (excluding abstract, keywords, and references; including tables, figures and appendices.)

Masked Review.  All author information (i.e., name, affiliation, email, phone number, address) should appear on a separate cover page of the manuscript. The manuscript should have no indication of authorship. Any indication of authorship will result in your manuscript being unsubmitted.

Formatting.  Manuscripts should be typed, serif or san serif text as recommended by APA 7th edition (e.g., 11-point Calibri, 11-point Arial, and 10-point Lucida Sans Unicode, 12-point Times New Roman, 11-point Georgia, 10-point Computer Modern), double-spaced throughout, including block quotes and references, and each page should be numbered on the top right side of the page consecutively. Authors should follow instructions in the 7th edition Publication Manual of the American Psychological Association; this includes running heads, heading levels, spacing, margins, etc.. Any manuscripts not following APA 7th edition will be unsubmitted. [Please note, the  RHE  editorial team recommends 12-pt Times New Roman font to ensure proper format conversion within the ScholarOne system.]

Abstract.  All manuscripts must include an abstract of 100 words or fewer, and keywords as the first page of your manuscript submission (this page does not count towards your page limit).

Author Note.  An Author’s note may include Land Acknowledgments, Disclosure Statement (i.e., funding sources), or other acknowledgments. This should appear on your title page (not in the masked manuscript).  

Tables.  All tables should be editable. Tables may be uploaded in the manuscript itself or in a separate word document. All tables must be interpretable by readers without the reference to the manuscript. Do not duplicate information from the manuscript into tables. Tables must present additional information from what has already been stated in the manuscript.

Figures.  Figures should be editable in the manuscript or a separate word document (i.e., no embedded tables). For fixed images, please upload high-resolution JPEGs separately.

References.  The reference page should follow 7th edition APA guidelines and be double spaced throughout (reference pages do not count toward your page limit). 

Appendices.  Appendices should generally run no more than 3 manuscript pages. 

Additional Checklist for Revised Submissions

Revised manuscripts should follow the checklist above, with the following additional notes: 

Page Limit.  Revised manuscripts should stay within the page limit for new submissions (32 pages). However, we do realize that this is not always possible, and we may allow for a couple of extra pages for your revisions. Extensions to your page length will be subject to editor approval upon resubmission, but may not exceed 35 pages (excluding abstract, keywords, and references).

• Author Response to Reviewer Comments.  At the beginning of your revised manuscript file, please include a separate masked statement that indicates fully [1] all changes that have been made in response to the reviewer and editor suggestions and the pages on which those changes may be found in the revised manuscript and [2] those reviewer and editor suggestions that are not addressed in the revised manuscript and a rationale for why you think such revisions are not necessary. This can be in the form of a table or text paragraphs and must appear at the front of your revised manuscript document. Your response to reviewer and editor comments will not count toward your manuscript page limit. Please note that, because you will be adding your response to the reviewer and editor feedback to the beginning of your submission, this may change the page numbers of your document unless you change the pagination and start your manuscript itself on page 1. The choice is yours but either way, please ensure that you reference the appropriate page numbers within your manuscript in these responses. Additionally, when you submit your revised manuscript, there will be a submission box labeled “Author Response to Decision Letter”. You are not required to duplicate information already provided in the manuscript, but instead may use this to send a note to the reviewer team (e.g., an anonymous cover letter or note of appreciation for feedback). Please maintain anonymity throughout the review process by NOT including your name or by masking any potentially identifying information when providing your response to the reviewer's feedback (both in documents and the ScholarOne system).

Editorial Correspondence

Please address all correspondence about submitting articles (no subscriptions, please) to one or both of the following editors:

Dr. Penny A. Pasque, PhD Editor, Review of Higher Education 341 C Ramseyer Hall 29 W. Woodruff Avenue The Ohio State University Columbus, OH 43210 email:  [email protected]

Dr. Thomas F. Nelson Laird, PhD Editor, Review of Higher Education 201 North Rose Avenue Indiana University School of Education Bloomington, IN 47405-100 email:  [email protected]

Submission Policy

RHE publishes original works that are not available elsewhere. We ask that all manuscripts submitted to our journal for review are not published, in press or submitted to other journals while under our review. Additionally, reprints and translations of previously published articles will not be accepted.

Type of Preliminary Review

RHE utilizes a collaborative review process that requires several members of the editorial team to ensure that submitted manuscripts are suitable before being sent out for masked peer-review. Members of this team include a Editor, Associate Editor and Managing Editors. Managing Editors complete an initial review of manuscripts to ensure authors meet RHE ’s Author Guidelines and work with submitting authors to address preliminary issues and concerns (i.e., APA formatting). Editors and Associate Editors work together to decide whether it should be sent out for review and select appropriate reviewers for the manuscript.

Type of Review

When a manuscript is determined as suitable for review by the collaborative decision of the editorial team, Editors and/or Associate Editors will assign reviewers. Both the authors’ and reviewers’ are masked throughout the review and decision process.

Criteria for Review

Criteria for review include, but are not limited to, the significance of the topic to higher education, completeness of the literature review, appropriateness of the research methods or historical analysis, and the quality of the discussion concerning the implications of the findings for theory, research, and practice. In addition, we look for the congruence of thought and approach throughout the manuscript components.

Type of Revisions Process

Some authors will receive a “Major Revision” or “Minor Revision” decision. Authors who receive such decisions are encouraged to carefully attend to reviewer’s comments and recommendations and resubmit their revised manuscripts for another round of reviews. When submitting their revised manuscripts, authors are asked to include a response letter and indicate how they have responded to reviewer comments and recommendations. In some instances, authors may be asked to revise and resubmit a manuscript more than once.

Review Process Once Revised

Revised manuscripts are sent to the reviewers who originally made comments and recommendations regarding the manuscript, whenever possible. We rely on our editorial board and ad-hoc reviewers who volunteer their time and we give those reviewers a month to provide thorough feedback. Please see attached pdf for a visual representation of the RHE workflow .

Timetable (approx.)

• Managing Editor Technical Checks – 1-3 days
• Editor reviews and assigns manuscript to Associate Editors – 3-5 days
• Associate Editor reviews and invites reviewers – 3-5 days
• Reviewer comments due – 30 days provided for reviews
• Associate Editor makes a recommendation –  5-7 days
• Editor makes decision – 5-7 days
• If R&R, authors revise and resubmit manuscript – 90 days provided for revisions
• Repeat process above until manuscript is accepted or rejected -

Type of review for book reviews

Book reviews are the responsibility of the associate editor of book reviews. Decisions about acceptance of a book review are made by that associate editor.

The Hopkins Press Journals Ethics and Malpractice Statement can be found at the ethics-and-malpractice  page.

The Review of Higher Education expects all authors to review and adhere to ASHE’s Conflict of Interest Policy before submitting any and all work. The term “conflict of interest” means any financial or other interest which conflicts with the work of the individual because it (1) could significantly impair the individual’s objectivity or (2) could create an unfair advantage for any person or organization. Please refer to the policy at ashe.ws/ashe_coi .

Guidelines for Book Reviews

RHE publishes book reviews of original research, summaries of research, or scholarly thinking in book form. We do not publish reviews of books or media that would be described as expert opinion or advice for practitioners.

The journal publishes reviews of current books, meaning books published no more than 12 months prior to submission to the associate editor in charge of book reviews.

If you want to know whether the RHE would consider a book review before writing it, you may email the associate editor responsible for book reviews with the citation for the book.

Reviewers should have scholarly expertise in the higher education research area they are reviewing.

Graduate students are welcome to co-author book reviews, but with faculty or seasoned research professionals as first authors.

Please email the review to the associate editor in charge of book reviews (Timothy Reese Cain, [email protected] ), who will work through necessary revisions with you if your submission is accepted for publishing.

In general, follow the APA Publication Manual, 7th edition.

Provide a brief but clear description and summary of the contents so that the reader has a good idea of the scope and organization of the book. This is especially important when reviewing anthologies that include multiple sections with multiple authors.

Provide an evaluation of the book, both positive and negative points. What has been done well? Not so well? For example the following are some questions that you can address (not exclusively), as appropriate:

What are the important contributions that this book makes?

What contributions could have been made, but were not made?

What arguments or claims were problematic, weak, etc.?

How is the book related to, how does it supplement, or how does it complicate current work on the topic?

To which audience(s) will this book be most helpful?

How well has the author achieved their stated goals?

Use quotations efficiently to provide a flavor of the writing style and/or statements that are particularly helpful in illustrating the author(s) points. 

If you cite any other published work, please provide a complete reference.

Please include a brief biographical statement immediately after your name, usually title and institution. Follow the same format for co authored reviews. The first author is the contact author.

Please follow this example for the headnote of the book(s) you are reviewing: Stefan M. Bradley. Upending the Ivory Tower: Civil Rights, Black Power, and the Ivy League. New York: New York University Press, 2018. 465 pp. $35. ISBN 97814798739999.

Our preferred length is 2,000–2,500 words in order for authors to provide a complete, analytical, review. Reviews of shorter books may not need to be of that length.

The term “conflict of interest” means any financial or other interest which conflicts with the work of the individual because it (1) could significantly impair the individual’s objectivity or (2) could create an unfair advantage for any person or organization. We recommend all book reviewers read and adhere to the ASHE Conflict of Interest Policy before submitting any and all work. Please refer to the policy at ashe.ws/ashe_coi

NOTE: If the Editor has sent a book to an author for review, but the author is unable to complete the review within a reasonable timeframe, we would appreciate the return of the book as soon as possible; thanks for your understanding.

Please send book review copies to the contact above. Review copies received by the Johns Hopkins University Press office will be discarded.

Penny A. Pasque,         The Ohio State University

Thomas F. Nelson Laird,         Indiana University-Bloomington

Associate Editors

Angela Boatman,         Boston College

Timothy Reese Cain (including Book Reviews),         University of Georgia

Milagros Castillo-Montoya,         University of Connecticut

Tania D. Mitchell,         University of Minnesota

Chrystal George Mwangi       George Mason University

Federick Ngo,        University of Nevada, Las Vegas

Managing Editors

Stephanie Nguyen,         Indiana University Bloomington

Monica Quezada Barrera,         The Ohio State University

Editorial Board

Sonja Ardoin,         Clemson University

Peter Riley Bahr,        University of Michigan

Vicki Baker,      Albion College

Allison BrckaLorenz,        Indiana University Bloomington

Nolan L. Cabrera,        The University of Arizona

Brendan Cantwell,        Michigan State University

Rozana Carducci,        Elon University

Deborah Faye Carter,         Claremont Graduate University

Ashley Clayton,         Louisiana State University

Regina Deil-Amen,         The University of Arizona 

Jennifer A. Delaney,     University of Illinois Urbana Champaign

Erin E. Doran,    Iowa State University

Antonio Duran,   Arizona State University 

Michelle M. Espino,        University of Maryland 

Claudia García-Louis,        University of Texas, San Antonio

Deryl Hatch-Tocaimaza,        University of Nebraska-Lincoln

Nicholas Hillman,        University of Wisconsin-Madison

Cindy Ann Kilgo,        Indiana University-Bloomington

Judy Marquez Kiyama,  University of Arizona

Román Liera,        Montclair State University

Angela Locks,        California State University, Long Beach

Demetri L. Morgan,  Loyola University Chicago

Rebecca Natow,         Hofstra University 

Z Nicolazzo,        The University of Arizona

Elizabeth Niehaus,        University of Nebraska-Lincoln

Robert T. Palmer,        Howard University

Rosemary Perez,        University of Michigan

OiYan Poon,         Spencer Foundation 

Kelly Rosinger,        The Pennsylvania State University

Vanessa Sansone,         The University of Texas at San Antonio

Tricia Seifert,        Montana State University

Barrett Taylor,         University of North Texas 

Annemarie Vaccaro,  University of Rhode Island

Xueli Wang,        University of Wisconsin-Madison

Stephanie Waterman,         University of Toronto 

Rachelle Winkle-Wagner,         University of Wisconsin-Madison

Association for the Study of Higher Education Board of Directors

The Review of Higher Education is the journal of Association for the Study Higher Education (ASHE) and follows the ASHE Bylaws and Statement on Diversity. 

ASHE Board of Directors

Abstracting & Indexing Databases

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• Academic Search Alumni Edition, 9/1/2003-
• Academic Search Complete, 9/1/2003-
• Academic Search Elite, 9/1/2003-
• Academic Search Premier, 9/1/2003-
• Current Abstracts, 9/1/2003-
• Education Research Complete, 3/1/1997-
• Education Research Index, Sep.2003-
• Education Source, 3/1/1997-
• Educational Administration Abstracts, 3/1/1991-
• ERIC (Education Resources Information Center), 1977-
• MLA International Bibliography (Modern Language Association)
• Poetry & Short Story Reference Center, 3/1/1997-
• PsycINFO, 2001-, dropped
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• ArticleFirst, vol.15, no.3, 1992-vol.35, no.2, 2011
• Electronic Collections Online, vol.20, no.1, 1996-vol.35, no.2, 2011
• Periodical Abstracts, v.26, n.4, 2003-v.33, n.3, 2010
• PsycFIRST, vol.24, no.3, 2001-vol.33, no.1, 2009
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• Education Collection, 7/1/2003-
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• Health Research Premium Collection, 7/1/2003-
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• ProQuest 5000, 07/01/2003-
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• ProQuest Central, 07/01/2003-
• Psychology Database, 7/1/2003-
• Research Library, 07/01/2003-
• Social Science Premium Collection, 07/01/2003-
• Educational Research Abstracts Online
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• Studies on Women and Gender Abstracts (Online)

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1.8 (2022) 3.2 (Five-Year Impact Factor) 0.00195 (Eigenfactor™ Score) Rank in Category (by Journal Impact Factor): 185 of 269 journals, in “Education & Educational Research”

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Hopkins Press Journals

Higher Education

The International Journal of Higher Education Research

Recognized as the leading international journal on higher education studies, this publication examines educational developments throughout the world in universities, polytechnics, colleges, and vocational and education institutions. It reports on developments in both public and private higher education sectors.

Higher Education features contributions from leading scholars from different countries who tackle the problems of teachers as well as students, and of planners as well as administrators. It presents authoritative overview articles, comparative studies and analyses of particular problems or issues.

While each higher education system has its own distinctive features, common problems and issues are shared internationally by researchers, teachers and institutional leaders. Higher Education offers opportunities for the exchange of research results, experience and insights, and provides a forum for ongoing discussion between experts.

This is a transformative journal , you may have access to funding.

• Jenni Case,
• Hugo Horta,
• Simon Marginson

Latest issue

Volume 87, Issue 4

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HIGHER EDUCATION is an international journal in the multidisciplinary field of higher education research. Its policy is to give priority to papers that are relevant to the international higher education research and policy community.

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Open Access

Peer-reviewed

Research Article

Effects of the COVID-19 pandemic in higher education: A data driven analysis for the knowledge acquisition process

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations Instituto de Física de Líquidos y Sistemas Biológicos (UNLP-CONICET), La Plata, Argentina, Departamento de Ciencias Básicas, Facultad de Ingeniería, Universidad Nacional de La Plata (UNLP), La Plata, Argentina, Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB, Palma de Mallorca, Spain

Roles Data curation, Formal analysis, Methodology, Software, Writing – original draft, Writing – review & editing

Affiliation Departamento de Física Médica, Centro Atómico Bariloche, CONICET, CNEA, Bariloche, Argentina

Roles Data curation, Formal analysis, Methodology, Software, Validation, Writing – original draft, Writing – review & editing

Affiliation Departamento de Estadística, Centro Regional Universitario Bariloche (CRUB) Universidad Nacional del Comahue (UNCOMA), Neuquén, Argentina

Roles Conceptualization, Formal analysis, Investigation, Methodology, Supervision, Writing – original draft, Writing – review & editing

Affiliations División Física Estadística e Interdisciplinaria, Centro Atómico Bariloche and CONICET, Bariloche, Argentina, Profesorado en Física, Universidad Nacional de Río Negro (UNRN), Bariloche, Argentina

• Fátima Velásquez-Rojas, 
• Jesus E. Fajardo, 
• Daniela Zacharías, 
• María Fabiana Laguna

• Published: September 7, 2022
• https://doi.org/10.1371/journal.pone.0274039
• Reader Comments

The COVID-19 pandemic abruptly changed the classroom context and presented enormous challenges for all actors in the educational process, who had to overcome multiple difficulties and incorporate new strategies and tools to construct new knowledge. In this work we analyze how student performance was affected, for a particular case of higher education in La Plata, Argentina. We developed an analytical model for the knowledge acquisition process, based on a series of surveys and information on academic performance in both contexts: face-to-face (before the onset of the pandemic) and virtual (during confinement) with 173 students during 2019 and 2020. The information collected allowed us to construct an adequate representation of the process that takes into account the main contributions common to all individuals. We analyzed the significance of the model by means of Artificial Neural Networks and a Multiple Linear Regression Method. We found that the virtual context produced a decrease in motivation to learn. Moreover, the emerging network of contacts built from the interaction between peers reveals different structures in both contexts. In all cases, interaction with teachers turned out to be of the utmost importance in the process of acquiring knowledge. Our results indicate that this process was also strongly influenced by the availability of resources of each student. This reflects the reality of a developing country, which experienced prolonged isolation, giving way to a particular learning context in which we were able to identify key factors that could guide the design of strategies in similar scenarios.

Citation: Velásquez-Rojas F, Fajardo JE, Zacharías D, Laguna MF (2022) Effects of the COVID-19 pandemic in higher education: A data driven analysis for the knowledge acquisition process. PLoS ONE 17(9): e0274039. https://doi.org/10.1371/journal.pone.0274039

Editor: Jianguo Wang, China University of Mining and Technology, CHINA

Received: September 7, 2021; Accepted: August 19, 2022; Published: September 7, 2022

Copyright: © 2022 Velásquez-Rojas et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting information files.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The process of acquiring knowledge is one of the most complex for the human being since it involves individual and social processes that have been studied by various epistemological currents [ 1 ]. The educational context where this process is developed is of great relevance since it represents the meeting space between teachers and students, in which a fundamental part of the construction of new knowledge occurs.

The COVID-19 pandemic abruptly changed this context with classroom closures of unprecedented extent and duration, disrupting conventional education in schools and universities around the world. Such measures were an extension of the isolation established in many countries to mitigate the effects of COVID-19, given that social distancing proved to be one of the most effective strategies [ 2 – 10 ].

The educational community as a whole made an enormous effort to quickly adapt to the distance and online learning that this lockdown brought [ 11 ], but it is no less true that students were forced to rely much more on their own resources to sustain the continuity of their learning during this period [ 11 – 13 ]. In the particular case of Argentina the confinement measures began on March 20, 2020, affected all educational levels and coincided with the beginning of the first semester of the academic year.

The new educational context not only brought about great challenges but was also reflected in the results obtained by the students [ 14 – 18 ]. The effects of the change in the learning conditions, although recent and still in process, have been analyzed from different perspectives [ 11 – 17 ]. A less explored methodology, which we propose to address here, is to study this problem from the point of view of complex systems, in line with what was done by some of the authors of this work just before the start of the pandemic [ 19 ]. The reason behind choosing this research design lies in the fact that the approach from this perspective allows the interactions between the individuals involved to be adequately considered when analyzing the effect of a global variable, such as the pandemic. But in addition, the usefulness of mathematical modeling to unravel the relevance of different factors that are present in the knowledge acquisition process was demonstrated in our previous work.

In [ 19 ] we developed an analytical model (the KA model) based on data from a series of surveys that are contrasted with information on academic performance of students, to analyze how the knowledge acquisition depends globally on different extrinsic and intrinsic factors. Regarding the intrinsic factors, one that contributes greatly to the acquisition of knowledge of students is motivation, and this is precisely one of the most affected by the pandemic [ 20 ]. According to the EU report [ 14 ], the closure of physical schools and the adoption of distance education can negatively affect student learning through four main channels: less time spent learning, symptoms of stress, a change in the way that students interact, and lack of motivation to learn. But, it is possible to use a model to assess the hypothesis that lack of motivation is one of the strongest negative impacts of the pandemic on students, regardless of their personal characteristics? In particular, and since the KA model was developed for a specific (face-to-face) context, the first question to be answered in this work should be whether this model is sensitive to modifications of the educational context.

On the other hand, it was already mentioned that the change in physical context affected extrinsic factors that contribute to the acquisition of knowledge, such as the interaction with peers and teachers. This interaction has been found to be essential for the development of positive self-esteem, self-confidence, and a sense of identity. In fact, there is significant evidence showing that social skills are positively associated with cognitive skills and school achievement [ 21 , 22 ]. In this regard, a series of questions arise: From the perspective of the students, did the bond with teachers improve or worsen during the pandemic? Did the interaction between peers change with the change of context? What aspects of it can be measured in the new context?

Analyzing the consequences of the pandemic on the educational performance is a matter of global importance. It is well known that the distance education is essential to ensure the continuity of learning in situations in which face-to-face classes are suspended. In places where virtual and remote strategies were already becoming a reality, the change was a positive [ 18 ]. However, in other countries something as basic as Internet access is still a privilege, guaranteeing distance education cannot be taken for granted. The preparation (or lack thereof) of some countries in this area has revealed the weaknesses of educational methodologies and resources [ 13 ]. Bringing this situation to light is one more step towards fairness.

The previous statements prompt us to seek answers about how much the academic performance of students was affected by the change in the educational context caused by the pandemic. In addition, and in relation to the KA model, we would like to evaluate whether the aspects that we consider relevant have a comparable importance in the construction of knowledge, as well as the consistency of these results when comparing both scenarios.

In this new approach we adapt the analytical model presented in [ 19 ] to compare the knowledge acquisition process in two different contexts: face-to-face (before the onset of the pandemic) and virtual (during the confinement), for a particular case in higher education in Argentina. We present a study that involves 173 students and its entire evolution during 2019 and 2020 in both contexts. Furthermore, and in order to assess the relevance of the parameters we chose for our model, we apply two robust and versatile tools used in multiple applications: Artificial Neural Networks and a Multiple Linear Regression Method.

The article is organized as follows: in the Methods section we describe the participants and its educational context, the data collection and variables (which include the surveys used to construct our data-based model) and the different approaches used to fit the parameters of the model. Then, we present the main results of this work and finally, we summarize and discuss our findings.

Educational context

The research was carried out with several sections of students who attended the Physics II course, corresponding to the second year of Engineering careers at the Faculty of Engineering of the National University of La Plata (UNLP) [ 23 ], Argentina, during the years 2019 and 2020. The Faculty offers 13 engineering degrees, so the interest of the students in the course can vary greatly.

The complete course lasts one semester, with a workload of 8 hours per week divided into 2 theoretical-practical classes. The course consists of two parts, at the end of which a partial written test is taken with a score between 0 and 10. There are two approval regimes: direct promotion, which implies being exempt from the final test (if the average between the two partial exams is 6 or more) or promotion by final exam (if the average is between 4 and 6). Partial tests have an instance of recuperation during the semester and another at the end of it, where the student can improve any of the lower scores obtained in previous tests. This organization was also maintained during the confinement (in virtual context).

Participants

The first part of the research was done during the two semesters of the year 2019, with four different sections in face-to-face context for a total of 81 students (50 male, 31 female). The second part was developed during the year 2020 and also involved four different sections in two semesters, for a total of 92 students (61 male, 31 female). In all cases we had access to the final grade they obtained in the course. In both contexts, we worked with 4 different sections of students for a total of 8 sections, 173 students in 2 years. The initial group of students was much larger, however there were 173 who participated in the whole process. These data are reported in the ( S1 File ) and has been collected with the following actions:

• It does not involve minors.
• It has been collected anonymously. Students have been identified by a numerical code, avoiding gathering of any personal information.
• Students have been informed by the lecturers that some information about their activity could be anonymously collected for statistical purposes. Authors of this study did not receive any objections.
• The tasks related to this study were completely voluntary and they did not in any form alter students’ activities, classes, or the assessment process.

Considering these circumstances, we do not need to apply for ethics approval from our university since no personal data, minors or potentially hazardous activities were involved in the study.

Besides, all teachers involved in the study (some of them also co-authors of this manuscript) who were responsible for the subject taught also gave consent to carry out the study.

We obtained verbal consent from all the participants in the study.

Data collection and variables

We are interested in analyzing and comparing the processes observed in both contexts in terms of the KA model presented in [ 19 ]. A first step consisted in carrying out a classification such as that proposed by Bordogna and Albano [ 24 ] and which proved to be useful in our previous work. This involved separating the students into three different groups according to their final achievements K f , which we relate to the final grade obtained in the course. This was done as follows: (a) High-achieving (HA) students: 8 ≤ K f ≤ 10, (b) Average-achieving (AA) students: 6 < K f < 8 and (c) Low-achieving (LA) students: K f ≤ 6. It is worth noting that students with a final grade lower than 4 are not included in this study.

In Table 1 we show the number of students who participated in the work divided according to their final achievements K f , that we relate to the final grade obtained in the course. Interestingly, and as we found in [ 19 ], the groups have qualitatively different characteristics regarding the relevance of the factors considered in the construction of the new knowledge, as it will be clear shortly.

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https://doi.org/10.1371/journal.pone.0274039.t001

In Fig 1 we display the final grades obtained for all students that we include in the present work. In filled symbols we plot the data in the face-to-face context and the empty symbols represent the data in the virtual context. These data provide us with the information to contrast our theoretical model. A first look at this graph reveals that the marks obtained in the two contexts were different for the HA and LA groups, while the AA group did not present differences. HA students, whose grades were higher than 8, had on average a better performance in virtual context than in face-to-face context. The opposite is seen with the Low-achievement students, LA. To analyze the possible causes of these differences is one of the main purposes of the present paper.

https://doi.org/10.1371/journal.pone.0274039.g001

KA model for both contexts.

It is worth noting that in our study the contribution of peers to the acquisition of knowledge was gathered in two ways: the group conformation and the peer interaction itself. The group conformation includes information on the spatial distribution of the students and the formation of groups, obtained through direct observations of the classes before confinement and through questions in online surveys during confinement. An analysis of the differences in the structure of the peer network formed in each context is carried out in Fig 4 in the Results section.

During the virtual context, important and complementary information was also collected, such as resources the students had (work-space, technological equipment) and the context itself and how it was perceived. Although they are not included as terms in Eq 1 , we carry out a description of the observed situation in the S2 File .

Finally, it should be noted that in our study we focus on a specific type of learning, related to scientific concepts of classical physics. While we are aware that this is not the only value learned in the classroom, we simplify the concept of knowledge to use the final grade as a concrete and quantifiable measure of the student’s performance.

Here we present the surveys carried out on students during each semester of classes ( Table 2 ). The numbers and letters in the last column correspond to the values that we assign to each of them, in order to transfer the answers to the KA model of Eq 1 . The questions marked with (*) were reformulated to adapt them to the virtual context. The surveys carried out in the virtual context were delivered and completed in a digital way using Google tools, while those corresponding to the pre-confinement stage were delivered personally and were completed manually.

Although the surveys were broader, here we only include the questions involved in the model.

https://doi.org/10.1371/journal.pone.0274039.t002

The quantities evaluated more than once (as is the case of M or T ) were averaged in order to have a single value for each factor. Besides, the combination of strategies for the question that measures the interaction with the teacher T in the third survey was given the following numerical values: ABC = AB = AC = BC = 1, A = B = 0.7, C = AD = BD = ABD = ACD = BCD = 0.5, CD = 0.3, D = 0.1 (students could mark several options). These values were given to enhance the use of the strategies provided by the specific section to which the students belonged (options A, B).

From surveys to KA model.

https://doi.org/10.1371/journal.pone.0274039.t003

Proposed tools for analysis

As it was already mentioned, each of these groups has different characteristics regarding the relevance of the factors considered in the construction of Eq 1 . To explicitly measure the weight of each of them we apply two different and complementary approaches: Artificial Neural Networks (ANN) and a Multiple Linear Regression Method (MLR).

In reference [ 25 ], the capability of the ANN for estimating parameters of complex nonlinear and linear problems has been shown. A single-layer perceptron (SLP) constitutes a particular case of the ANN whose output equation resembles Eq 1 . This allows to cross-validate the MLR, which is the most common form of linear regression analysis to treat this kind of problem.

Single Layer Perceptron (SLP) network overview.

To reproduce Eq 1 from an ANN architecture we employed a SLP [ 25 ]. This type of ANN constitutes a particular case of a Multilayer Perceptron (MLP) [ 26 ]. The SLP is a feedforward network of a single artificial neuron-like unit, whose x j inputs (disposed akin to biologic dendrites) are multiplied by a corresponding weight w j and this product is passed to a neuron-like unit where the aforementioned product is added up, as shown in Fig 2 .

The usual ANN notation is in black text and in red text, the equivalent terms corresponding to this particular work are shown. (See Eq 1 ). The element-wise product between the inputs and the weights are added up in the “net input function” stage and suppressing the activation function, an output corresponding to the linear combination of the inputs and the weights is obtained.

https://doi.org/10.1371/journal.pone.0274039.g002

The SLP model was implemented in the programming language Python by means of the Keras package [ 27 ].

Multiple Linear Regression Method.

Besides, β M , β T , β P , β HA , β LA and β F are the regression coefficients corresponding to the variables M , T , P , HA , LA and F , respectively, and they were estimated through the OLS (Ordinary Least Squares) method.

This model was fitted using the function lm() in the programming language R version 4.1.0 [ 28 ].

Comparison between contexts

In our previous work [ 19 ], we compared the results of our KA model with the final grade that the students obtained. Looking for an answer to our main question, about how the educational context affected student performance, we first compare the general results in both, face-to-face and virtual contexts.

We proposed in Eq 1 that the final knowledge reached by a student on a given topic is mainly due to three contributing factors, the personal motivation ( M ), the influence of the teachers ( T ) and the influence of peers ( P ). In Fig 3 we show the average values of the final grade of each group, < K f >, together with average of the data obtained from the surveys carried out, in order to analyze and compare the differences observed with the change of context.

(a) Final grade < K f >, (b) motivation M , (c) interaction with teachers T and (d) interaction between peers P .

https://doi.org/10.1371/journal.pone.0274039.g003

These results allow us to respond positively to our first question, about whether our approach is sensitive to changes in the educational context. As we can see, although the KA model was originally developed for a specific context (face to face), the values of the variables are different for both contexts.

In Fig 3(a) we show the average final grade < K f > for each group of students (HA, AA and LA) and in both contexts. We observe again the differences we first noticed in Fig 1 , related to how the performance of each group is modified with the change of context. For HA students, < K f > increased during the virtual context while for AA and LA it decreased. In what follows, and to deepen the understanding of what is observed, we will analyze what was obtained for the three contributing factors (also averaged for each group), and that we plot in panels (b), (c) and (d) of Fig 3 .

The values of motivation presented in Fig 3(b) reflects a widely studied aspect of the psychological impact of the pandemic on students [ 14 , 20 ]. Our results clearly report the impact of the virtual context on the motivation of students, no matter the group they belong to. This fact should in itself be an alarm to build policies to support the mental health and educational success of the students at all times. If motivation dropped notably in the new virtual context, and the final knowledge is considered as the sum of several factors that contribute to the acquisition of this knowledge, then the way of interacting with peers and teachers also had to change.

The general decrease in the virtual context observed in motivation is not repeated in the other factors analyzed in this study. Fig 3(c) gives us information about the teacher’s contribution from the students’ perspective. Note that for the HA group it has the same weight in both contexts (face-to-face and virtual), while for the AA and LA groups the interaction with teachers increased in the virtual context. Generally, the teacher acts as an intermediary between the activities carried out by the students in order to assimilate the new knowledge and in this new context their presence and support (albeit virtual) was fundamental for many students.

Finally, in Fig 3(d) , we can see the differences in the interaction between peers for each group of students, another issue that was affected during the pandemic.

We can see that HA’s enriched the study in groups in the virtual context in contrast to the other groups of students. We also found that the structure of the emerging contact network from peer interaction presents very different characteristics in both contexts. More details about this aspect of the problem are presented in the next subsection. The situation observed in Fig 3(d) for the interaction between peers is the one that most reflects the behavior of the general performance ( Fig 3(a) ), however the trend is attenuated due to what is observed in Fig 3(b) and 3(c) . These results may partially respond to the change observed in the way students interact.

The aforementioned results can be summarized in Table 4 where we show the relative changes between both contexts. This quantity expresses what it was observed in Fig 3 with the raw data obtained in the surveys: A strong decrease in the motivation term for all groups of students, and different trends in the way of interacting with peers and with teachers depending on the group to which the students belong.

https://doi.org/10.1371/journal.pone.0274039.t004

Networks of peer interactions.

The analysis carried out around Fig 3 indicates that the change in physical context modified the way in which students interact with each other. Furthermore, in this area data was collected in different ways depending on the context. In the face-to-face context, the observations in the classroom were made in situ, with photographic records and paper surveys. During the virtual context, the surveys were digital using Google tools as mentioned above. In the latter case, no observations could be made, so the students were asked how their interaction with the group was and with whom they specifically interacted. This fact could result in a lack of information for this context. However, that was not the case, since although the information collected in both cases is not completely comparable, they suggest a change in behavior in the relationship between peers. Table 5 expresses the number of students who were observed grouped or isolated during the face-to-face classes. Likewise, for the virtual case, the number of students who affirmed to study or not in a group is reported. We find that the percentage of isolated students decreased from 37% to 26% with the change of context. Interestingly, the increase in interaction between students in the virtual context was observed to a greater or lesser extent for the three groups.

https://doi.org/10.1371/journal.pone.0274039.t005

To deepen the understanding of how students modified their way of interacting, we draw in Fig 4(a) the network that represents the students before confinement (face-to-face context) for N = 81. As we said, the data was obtained from direct observations in the classroom, where the nodes represent the students (divided in the HA, AA and LA groups) and the links their interactions. Note that here we use double bonds, indicating a reciprocal interaction.

(a) Network scheme from classroom observations in face-to-face context where the links are reciprocal interactions. (b) Network scheme from data obtained through surveys in virtual context. The links can be or not be reciprocal interactions. The nodes marked with an asterisk represent students who claimed to interact with students from another section who did not participate in this study.

https://doi.org/10.1371/journal.pone.0274039.g004

Besides, in Fig 4(b) we show the network that describes the students in virtual context for N = 92. The data come from the surveys carried out, and again the nodes represent the students divided in the groups HA, AA and LA. We use links to represent their interactions, although now they are double or single, as the responses to the surveys given by the students may or may not be reciprocal. Moreover, the nodes marked with an asterisk represent students who claimed to interact with students from another section who did not participate in this study.

A comparison between both networks indicates some similarities, such as the presence of highly connected clusters, as well as isolated students. However, the network corresponding to the virtual context has nodes that connect two different clusters, acting as “bridges”. This was not observed in the face-to-face context and could mean a new form of relationship between students. This result deepens the understanding of the effect that the pandemic has on peer relationships, and answers some of the questions asked in the introduction on this topic.

Measure of the relevance of the terms that influence the knowledge acquisition process

A way to validate the model presented in Eq 1 is to analyze the relevance of the terms that compose it. In our previous work [ 19 ] we did it by adding coefficients to each factor of the KA model. These coefficients could be interpreted as the relative weight that each term in Eq 1 has, and were chosen so that the average value calculated with the model for each group is as close as possible to the average value of the actual final grades obtained. In order to analyze the relevance and consistency of the factors that we chose to describe the knowledge acquisition process, we now we choose two different and complementary approaches to find the weight of each term of Eq 1 : Artificial Neural Networks (ANN) and a Multiple Linear Regression Method (MLR).

ANN approach.

https://doi.org/10.1371/journal.pone.0274039.g005

https://doi.org/10.1371/journal.pone.0274039.t006

Finally, in Fig 6 we present a comparison between the final grade for each student and the final knowledge obtained from Eq 1 (KA model) with the coefficients obtained with the ANN approach. The global behavior of the KA model follows the general trend of the data. The observed dispersion is due to the presence of particular cases, whose complete evolution is not captured by the model. In our previous work [ 19 ] we made an analysis of some particular cases like these.

https://doi.org/10.1371/journal.pone.0274039.g006

MLR approach.

We make use of the Multiple Linear Regression Method in order to find the weights of each contributing factor of the KA model, and compare them with the ones obtained in the previous section. The results are shown in Table 7 , where we express the values for β , SE (standard error) and p-value for the terms of the Eq 3 .

https://doi.org/10.1371/journal.pone.0274039.t007

The p-values obtained show that all beta regression coefficients are statistically significant. Assumptions of linearity, independence, homoscedasticity and normality were checked, as well as the presence of influential values.

At last, we show in Fig 7 a comparison between the final grade for each student and the final knowledge of Eq 1 (KA model) with the coefficients obtained with the MLR approach. Again, the K f obtained with the model behaves similarly to the data. It should be noted the similarity of the result obtained in Figs 5 and 6 with that shown in Fig 2 of [ 19 ]. In the present work, the adjustment of the weights that gave rise to both figures was carried out in a more appropriate way than in that paper, where the coefficients of each term were chosen exploratory.

https://doi.org/10.1371/journal.pone.0274039.g007

In a previous work we proposed to describe the knowledge acquisition process as a dynamic quantity composed of several terms, where it was implicit that such a process was carried out in the classroom. But, what happens when the physical place where this complex socio-cultural construction takes place changes? What are the consequences of that specific educational context being taken from one day to the next? We seek to answer these questions by discussing how the terms of the knowledge acquisition model were modified, and which ones most directly influenced student performance during the transition to virtuality.

For that, we analyze the knowledge acquisition process in face-to-face and virtual contexts for a specific study case. Our investigation spanned two years and involved 173 students, observing the evolution of their learning process for each particular context.

Inspired by the work of Ref. [ 19 ], we wanted to assess whether the observed changes in academic performance can be understood from a model that incorporates the main factors that contribute to the knowledge acquisition process. The KA model is an analytical model based on data, which incorporates information from a series of surveys and whose results are contrasted with information on academic performance. The surveys were carried out 3 times during each semester and reflected the feelings of the students during their learning process that influenced their performance.

The raw data in Fig 1 show that the final grade of the students in both contexts presented differences. Specifically, the grades of the students with High-achievement (HA) were better in virtual context than in face-to-face context. The opposite is seen with Low-achieving students (LA), while the intermediate performance group (AA) did not show differences. In the results shown in [ 18 ], the performance of the students who used remote learning tools showed an improvement in the virtual context. We believe that this difference is due to the fact that having better resources positioned them in a privileged place with respect to the case studied in this work.

The results obtained in Fig 3 reinforce accepted ideas related to the importance of motivation in the learning process: the switch to virtual context caused a negative impact on the motivation of the entire student population, but was strongly reflected in the performance of LA students. This fact should alert the educational community and especially those responsible for building support mechanisms for the mental health of students. Furthermore, we observe that the new context generates a change in the way students interact with their peers and teachers. In particular, the HA students did not modify the interaction with the teachers (maintaining high values in both contexts) while they strengthened the study in groups in the virtual context, unlike the rest of the groups. For AA and LA students, interaction with teachers increased in the virtual context, and this result highlights the importance of the teacher’s role as a consultant and as fundamental support for students.

We also find that the structure of the network of contacts that is formed between peers in both contexts presents some common characteristics, as well as some interesting differences, as we saw in Fig 4 . Among the first is that both networks have highly connected clusters, as well as a significant number of completely isolated students. The virtual context network, however, shows a feature not observed in the other network: the presence of individuals who interact with one or more students from different clusters. These individuals act as bridges between students who otherwise would not be connected. These structures could be reflecting a new form of relationship between students that occurs more easily in the virtual context. Nevertheless, we are aware that this analysis requires a more detailed investigation that is beyond the scope of this work with the data we currently have. On the other hand, it is also true that the virtual context made it possible to record that the interactions between the students go beyond what happens in the classroom space.

Related with the previous analysis is the fact that, although the equation in the KA model is linear, the term of peers can be interpreted as an effective version of a real non-linear interaction. This term in itself adds complexity to the model since group interaction does not obey “linear” rules. However, the simplification made in the KA model remains valid in light of the results obtained in [ 19 ] and are in line with the idea that the learning process is not limited to the interactive behavior of individual teachers and students, but should be understood in terms of collaborative behavior [ 29 ].

In order to find out the relevance of the factors that we included in the KA model, we used two different approaches: a standard Multiple Linear Regression Method and a Single Layer Perceptron, which is a particular type of Artificial Neural Network.

The results obtained with the neural network ( Fig 5 ) indicate that in both contexts the weights are similar. This result also shows that the raw results adequately describe each context, since the data obtained in each situation reflect the particular reality that each group of students is going through.

Moreover, both approaches indicate a greater relevance of the term of interaction with teachers. We were able to collect information from the teachers to support this fact and the perception of the change in the interaction with the students was also commented on by them (see S2 File ). The knowledge acquisition process comes hand in hand with the importance of the interaction with teachers, and the literalness of their presence in the accompaniment during learning. This result also confirms in some way the universality of the educational act.

The comparisons of Figs 6 and 7 between the raw data and the results obtained with the KA model indicate that the general behavior of individuals can be suitably described with Eq 1 , which is simply the sum of the relative contributions of each of the proposed factors: personal motivation, interaction with peers and influence of teachers. The robustness of the coefficients obtained with the two approaches also indicates that the information collected in the surveys and observations was sufficient to construct an adequate representation of the process. We are aware that this simplification leaves out a huge number of variables that are integrated to give rise to the unique process that each person experiences. But we believe that the results obtained allow us to validate our choice of factors as the main contributions common to all individuals.

Now, we discuss some considerations on the scope and limitations of this work.

One is that we must not lose sight of the fact that the change in the specific physical context brought with it a change in the evaluation criteria. Actually, this aspect was addressed in the teacher interviews that we summarize in the ( S2 File ). As K f is a hard data (the final grade obtained in the course), it would be more appropriate to build new models that consider these data in a more comprehensive way, taking into account the challenges that arose due to the change in this educational context.

Another important issue that is absent from the KA model is the personal context of the students and their available resources. The reason why it was not included is because we had no survey done on these topics in the face-to-face period, so it was not possible to compare both contexts. However, in the Supplementary Material ( S2 File ) we include additional information regarding this subject obtained from the surveys carried out in the virtual context. When asking the students for their feelings regarding confinement, the responses were varied but reluctance was reflected in more than half of the responses. This coincides with our observation about the lack of motivation (see Fig 3(b) ). The emotional stress, widely discussed in this context, goes beyond the academic environment and it was an important characteristic that we tried to capture with our research. Moreover, we found some relevant differences between the students of the different groups, which could influence their performance. Among them, a third of the students belonging to the LA group said they had a poor Internet connection in contrast to the HA group in which this situation occurred for a sixth of the students. More importantly, 13% of students belonging to the LA group did not have a laptop computer and 30% did not have an adequate study space.

These results show how the pandemic has increased educational inequalities at the economic, technological, social and even emotional level of the actors in the educational process. The virtual context promoted a change in teaching and learning methodologies, but it also brought another great challenge that is still far from being resolved, namely access to resources for all students. Hence the importance of recognizing inequalities to make visible the urgent need to build university policies that improve this situation.

A final though has to do with the generalizability of our results. Although this study was done for a specific case, the main factors analyzed here (motivation, interaction with peers and teachers) are not isolated from the global scenario. The generalization of the KA model to other educational scenarios is not only possible but quite straightforward. It should be noted, however, that the part of our study referring to the virtual context was carried out during the first year of the pandemic, so the results obtained could be strongly influenced by the transition between both contexts. Nevertheless, we believe they are valuable in themselves and can serve to deepen the understanding of the complex process of learning.

Supporting information

S1 file. survey data: numerical values associated with the ka model..

https://doi.org/10.1371/journal.pone.0274039.s001

S2 File. Additional information obtained from student and teacher surveys.

https://doi.org/10.1371/journal.pone.0274039.s002

Acknowledgments

The authors acknowledge Dr. José Javier Ramasco for his helpful suggestions on data analysis and availability.

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The relationship between education and health: reducing disparities through a contextual approach

Anna zajacova.

Western University

Elizabeth M. Lawrence

University of North Carolina

Adults with higher educational attainment live healthier and longer lives compared to their less educated peers. The disparities are large and widening. We posit that understanding the educational and macro-level contexts in which this association occurs is key to reducing health disparities and improving population health. In this paper, we briefly review and critically assess the current state of research on the relationship between education and health in the United States. We then outline three directions for further research: We extend the conceptualization of education beyond attainment and demonstrate the centrality of the schooling process to health; We highlight the dual role of education a driver of opportunity but also a reproducer of inequality; We explain the central role of specific historical socio-political contexts in which the education-health association is embedded. This research agenda can inform policies and effective interventions to reduce health disparities and improve health of all Americans.

URGENT NEED FOR NEW DIRECTIONS IN EDUCATION-HEALTH RESEARCH

Americans have worse health than people in other high-income countries, and have been falling further behind in recent decades ( 137 ). This is partially due to the large health inequalities and poor health of adults with low education ( 84 ). Understanding the health benefits of education is thus integral to reducing health disparities and improving the well-being of 21 st century populations. Despite tremendous prior research, critical questions about the education-health relationship remain unanswered, in part because education and health are intertwined over the lifespans within and across generations and are inextricably embedded in the broader social context.

We posit that to effectively inform future educational and heath policy, we need to capture education ‘in action’ as it generates and constrains opportunity during the early lifespans of today’s cohorts. First, we need to expand our operationalization of education beyond attainment to consider the long-term educational process that precedes the attainment and its effect on health. Second, we need to re-conceptualize education as not only a vehicle for social success, valuable resources, and good health, but also as an institution that reproduces inequality across generations. And third, we argue that investigators need to bring historical, social and policy contexts into the heart of analyses: how does the education-health association vary across place and time, and how do political forces influence that variation?

During the past several generations, education has become the principal pathway to financial security, stable employment, and social success ( 8 ). At the same time, American youth have experienced increasingly unequal educational opportunities that depend on the schools they attend, the neighborhoods they live in, the color of their skin, and the financial resources of their family. The decline in manufacturing and rise of globalization have eroded the middle class, while the increasing returns to higher education magnified the economic gaps among working adults and families ( 107 ). In addition to these dramatic structural changes, policies that protected the welfare of vulnerable groups have been gradually eroded or dismantled ( 129 ). Together, these changes triggered a precipitous growth of economic and social inequalities in the American society ( 17 ; 106 ).

Unsurprisingly, health disparities grew hand in hand with the socio-economic inequalities. Although the average health of the US population improved over the past decades ( 67 ; 85 ), the gains largely went to the most educated groups. Inequalities in health ( 53 ; 77 ; 99 ) and mortality ( 86 ; 115 ) increased steadily, to a point where we now see an unprecedented pattern: health and longevity are deteriorating among those with less education ( 92 ; 99 ; 121 ; 143 ). With the current focus of the media, policymakers, and the public on the worrisome health patterns among less-educated Americans ( 28 ; 29 ), as well as the growing recognition of the importance of education for health ( 84 ), research on the health returns to education is at a critical juncture. A comprehensive research program is needed to understand how education and health are related, in order to identify effective points of intervention to improve population health and reduce disparities.

The article is organized in two parts. First, we review the current state of research on the relationship between education and health. In broad strokes, we summarize the theoretical and empirical foundations of the education-health relationship and critically assess the literature on the mechanisms and causal influence of education on health. In the second part, we highlight gaps in extant research and propose new directions for innovative research that will fill these gaps. The enormous breadth of the literature on education and health necessarily limits the scope of the review in terms of place and time; we focus on the United States and on findings generated during the rapid expansion of the education-health research in the past 10–15 years. The terms “education” and “schooling” are used interchangeably. Unless we state otherwise, both refer to attained education, whether measured in completed years or credentials. For references, we include prior review articles where available, seminal papers, and recent studies as the best starting points for further reading.

THE ASSOCIATION BETWEEN EDUCATION AND HEALTH

Conceptual toolbox for examining the association.

Researchers have generally drawn from three broad theoretical perspectives to hypothesize the relationship between education and health. Much of the education-health research over the past two decades has been grounded in the Fundamental Cause Theory ( 75 ). The FCT posits that social factors such as education are ‘fundamental’ causes of health and disease because they determine access to a multitude of material and non-material resources such as income, safe neighborhoods, or healthier lifestyles, all of which protect or enhance health. The multiplicity of pathways means that even as some mechanisms change or become less important, other mechanisms will continue to channel the fundamental dis/advantages into differential health ( 48 ). The Human Capital Theory (HCT), borrowed from econometrics, conceptualizes education as an investment that yields returns via increased productivity ( 12 ). Education improves individuals’ knowledge, skills, reasoning, effectiveness, and a broad range of other abilities, which can be utilized to produce health ( 93 ). The third approach, the Signaling or Credentialing perspective ( 34 ; 125 ) has been used to explain the observed large discontinuities in health at 12 or 16 years of schooling, typically associated with the receipt of a high school and college degrees, respectively. This perspective views earned credentials as a potent signal about one’s skills and abilities, and emphasizes the economic and social returns to such signals. Thus all three perspectives postulate a causal relationship between education and health and identify numerous mechanisms through which education influences health. The HCT specifies the mechanisms as embodied skills and abilities, FCT emphasizes the dynamism and flexibility of mechanisms, and credentialism identifies social responses to educational attainment. All three theoretical approaches, however, operationalize the complex process of schooling solely in terms of attainment and thus do not focus on differences in educational quality, type, or other institutional factors that might independently influence health. They also focus on individual-level factors: individual attainment, attainment effects, and mechanisms, and leave out the social context in which the education and health processes are embedded.

Observed associations between education and health

Empirically, hundreds of studies have documented “the gradient” whereby more schooling is linked with better health and longer life. A seminal 1973 book by Kitagawa and Hauser powerfully described large differences in mortality by education in the United States ( 71 ), a finding that has since been corroborated in numerous studies ( 31 ; 42 ; 46 ; 109 ; 124 ). In the following decades, nearly all health outcomes were also found strongly patterned by education. Less educated adults report worse general health ( 94 ; 141 ), more chronic conditions ( 68 ; 108 ), and more functional limitations and disability ( 118 ; 119 ; 130 ; 143 ). Objective measures of health, such as biological risk levels, are similarly correlated with educational attainment ( 35 ; 90 ; 140 ), showing that the gradient is not a function of differential reporting or knowledge.

The gradient is evident in men and women ( 139 ) and among all race/ethnic groups ( 36 ). However, meaningful group differences exist ( 60 ; 62 ; 91 ). In particular, education appears to have stronger health effects for women than men ( 111 ) and stronger effects for non-Hispanic whites than minority adults ( 134 ; 135 ) even if the differences are modest for some health outcomes ( 36 ). The observed variations may reflect systematic social differences in the educational process such as quality of schooling, content, or institutional type, as well as different returns to educational attainment in the labor market across population groups ( 26 ). At the same time, the groups share a common macro-level social context, which may underlie the gradient observed for all.

To illustrate the gradient, we analyzed 2002–2016 waves of the National Health Interview Survey (NHIS) data from adults aged 25–64. Figure 1 shows the levels of three health outcomes across educational attainment levels in six major demographic groups predicted at age 45. Three observations are noteworthy. First, the gradient is evident for all outcomes and in all race/ethnic/gender groups. Self-rated health exemplifies the staggering magnitude of the inequalities: White men and women without a high school diploma have about 57% chance of reporting fair or poor health, compared to just 9% for college graduates. Second, there are major group differences as well, both in the predicted levels of health problems, as well as in the education effects. The latter are not necessarily visible in the figures but the education effects are stronger for women and weaker for non-white adults as prior studies showed (table with regression model results underlying the prior statement is available from the authors). Third, an intriguing exception pertains to adults with “some college,” whose health is similar to high school graduates’ in health outcomes other than general health, despite their investment in and exposure to postsecondary education. We discuss this anomaly below.

Predicted Probability of Health Problems

Source: 2002–2016 NHIS Survey, Adults Age 25–64

Pathways through which education impacts health

What explains the improved health and longevity of more educated adults? The most prominent mediating mechanisms can be grouped into four categories: economic, health-behavioral, social-psychological, and access to health care. Education leads to better, more stable jobs that pay higher income and allow families to accumulate wealth that can be used to improve health ( 93 ). The economic factors are an important link between schooling and health, estimated to account for about 30% of the correlation ( 36 ). Health behaviors are undoubtedly an important proximal determinant of health but they only explain a part of the effect of schooling on health: adults with less education are more likely to smoke, have an unhealthy diet, and lack exercise ( 37 ; 73 ; 105 ; 117 ). Social-psychological pathways include successful long-term marriages and other sources of social support to help cope with stressors and daily hassles ( 128 ; 131 ). Interestingly, access to health care, while important to individual and population health overall, has a modest role in explaining health inequalities by education ( 61 ; 112 ; 133 ), highlighting the need to look upstream beyond the health care system toward social factors that underlie social disparities in health. Beyond these four groups of mechanisms that have received the most attention by investigators, many others have been examined, such as stress, cognitive and noncognitive skills, or environmental exposures ( 11 ; 43 ). Several excellent reviews further discuss mechanisms ( 2 ; 36 ; 66 ; 70 ; 93 ).

Causal interpretation of the education-health association

A burgeoning number of studies used innovative approaches such as natural experiments and twin design to test whether and how education causally affects health. These analyses are essential because recommendations for educational policies, programs, and interventions seeking to improve population health hinge on the causal impact of schooling on health outcomes. Overall, this literature shows that attainment, measured mostly in completed years of schooling, has a causal impact on health across numerous (though not all) contexts and outcomes.

Natural experiments take advantage of external changes that affect attainment but are unrelated to health, such as compulsory education reforms that raise the minimum years of schooling within a given population. A seminal 2005 study focused on increases in compulsory education between 1915 and 1939 across US states and found that a year of schooling reduced mortality by 3.6% ( 78 ). A re-analysis of the data indicated that taking into account state-level mortality trends rendered the mortality effects null but it also identified a significant and large causal effect on general health ( 88 ). A recent study of a large sample of older Americans reported a similar pattern: a substantial causal effect of education for self-rated health but not for mortality ( 47 ). School reform studies outside the US have reported compelling ( 122 ) or modest but significant ( 32 ) effects of schooling on health, although some studies have found nonsignificant ( 4 ), or even negative effects ( 7 ) for a range of health outcomes.

Twin design studies compare the health of twins with different levels of education. This design minimizes the influence of family resources and genetic differences in skills and health, especially for monozygotic twins, and thus serves to isolate the effect of schooling. In the US, studies using this design generated robust evidence of a causal effect of education on self-rated health ( 79 ), although some research has identified only modest ( 49 ) or not significant ( 3 ; 55 ) effects for other physical and mental health outcomes. Studies drawing on the large twin samples outside of the US have similarly found strong causal effects for mortality ( 80 ) and health ( 14 ; 16 ; 51 ) but again some analyses yielded no causal effects on health ( 13 ; 83 ) or health behaviors ( 14 ). Beyond our brief overview, readers may wish consult additional comprehensive reviews of the causal studies ( 40 ; 45 ; 89 ).

The causal studies add valuable evidence that educational attainment impacts adult health and mortality, even considering some limitations to their internal validity ( 15 ; 88 ). To improve population health and reduce health disparities, however, they should be viewed as a starting point to further research. First, the findings do not show how to improve the quality of schooling or its quantity for in the aggregate population, or how to overcome systematic intergenerational and social differences in educational opportunities. Second, their findings do take into account contexts and conditions in which educational attainment might be particularly important for health. In fact, the variability in the findings may be attributable to the stark differences in contexts across the studies, which include countries characterized by different political systems, different population groups, and birth cohorts ranging from the late 19 th to late 20 th centuries that were exposed to education at very different stages of the educational expansion process ( 9 ).

TOWARD A SOCIALLY-EMBEDDED UNDERSTANDING OF THE EDUCATION-HEALTH RELATIONSHIP

To date, the extensive research we briefly reviewed above has identified substantial health benefits of educational attainment in most contexts in today’s high-income countries. Still, many important questions remain unanswered. We outline three critical directions to gain a deeper understanding of the education-health relationship with particular relevance for policy development. All three directions shift the education-health paradigm to consider how education and health are embedded in life course and social contexts.

First, nearly universally, the education-health literature conceptualizes and operationalizes education in terms of attainment, as years of schooling or completed credentials. However, attainment is only the endpoint, although undoubtedly important, of an extended and extensive process of formal schooling, where institutional quality, type, content, peers, teachers, and many other individual, institutional, and interpersonal factors shape lifecourse trajectories of schooling and health. Understanding the role of the schooling process in health outcome is relevant for policy because it can show whether interventions should be aimed at increasing attainment, or whether it is more important to increase quality, change content, or otherwise improve the educational process at earlier stages for maximum health returns. Second, most studies have implicitly or explicitly treated educational attainment as an exogenous starting point, a driver of opportunities in adulthood. However, education also functions to reproduce inequality across generations. The explicit recognition of the dual function of education is critical to developing education policies that would avoid unintended consequence of increasing inequalities. And third, the review above indicates substantial variation in the education-health association across different historical and social contexts. Education and health are inextricably embedded in these contexts and analyses should therefore include them as fundamental influences on the education-health association. Research on contextual variation has the potential to identify contextual characteristics and even specific policies that exacerbate or reduce educational disparities in health.

We illustrate the key conceptual components of future research into the education-health relationship in Figure 2 . Important intergenerational and individual socio-demographic factors shape educational opportunities and educational trajectories, which are directly related to and captured in measures of educational attainment. This longitudinal and life course process culminates in educational disparities in adult health and mortality. Importantly, the macro-level context underlies every step of this process, shaping each of the concepts and their relationships.

Enriching the conceptualization of educational attainment

In most studies of the education-health associations, educational attainment is modeled using years of schooling, typically specified as a continuous covariate, effectively constraining each additional year to have the same impact. A growing body of research has substituted earned credentials for years. Few studies, however, have considered how the impact of additional schooling is likely to differ across the educational attainment spectrum. For example, one additional year of education compared to zero years may be life-changing by imparting basic literacy and numeracy skills. The completion of 14 rather than 13 years (without the completion of associated degree) could be associated with better health through the accumulation of additional knowledge and skills as well, or perhaps could be without health returns, if it is associated with poor grades, stigma linked to dropping out of college, or accumulated debt ( 63 ; 76 ). Examining the functional form of the education-health association can shed light on how and why education is beneficial for health ( 70 ). For instance, studies found that mortality gradually declines with years of schooling at low levels of educational attainment, with large discontinuities at high school and college degree attainment ( 56 ; 98 ). Such findings can point to the importance of completing a degree, not just increasing the quantity (years) of education. Examining mortality, however, implicitly focused on cohorts who went to school 50–60 years ago, within very different educational and social contexts. For findings relevant to current education policies, we need to focus on examining more recent birth cohorts.

A particularly provocative and noteworthy aspect of the functional form is the attainment group often identified as “some college:” adults who attended college but did not graduate with a four-year degree. Postsecondary educational experiences are increasingly central to the lives of American adults ( 27 ) and college completion has become the minimum requirement for entry into middle class ( 65 ; 87 ). Among high school graduates, over 70% enroll in college ( 22 ) but the majority never earn a four-year degree ( 113 ). In fact,, the largest education-attainment group among non-elderly US adults comprises the 54 million adults (29% of total) with some college or associate’s degree ( 113 ). However, as in Figure 1 , this group often defies the standard gradient in health. Several recent studies have found that the health returns to their postsecondary investments are marginal at best ( 110 ; 123 ; 142 ; 144 ). This finding should spur new research to understand the outcomes of this large population group, and to glean insights into the health returns to the postsecondary schooling process. For instance, in the absence of earning a degree, is greater exposure to college education in terms of semesters or earned credits associated with better health or not? How do the returns to postsecondary schooling differ across the heterogeneous institutions ranging from selective 4-year to for-profit community colleges? How does accumulated college debt influence both dropout and later health? Can we identify circumstances under which some college education is beneficial for health? Understanding the health outcomes for this attainment group can shed light on the aspects of education that are most important for improving health.

A related point pertains to the reliability and validity of self-reported educational attainment. If a respondent reports 16 completed years of education, for example, are they carefully counting the number of years of enrollment, or is 16 shorthand for “completed college”? And, is 16 years the best indicator of college completion in the current context when the median time to earn a four-year degree exceeds 5 years ( 30 )? And, is longer time in college given a degree beneficial for health or does it signify delayed or disrupted educational pathways linked to weaker health benefits ( 132 )? How should we measure part-time enrollment? As studies begin to adjudicate between the health effects of years versus credentials ( 74 ) in the changing landscape of increasingly ‘nontraditional’ pathways through college ( 132 ), this measurement work will be necessary for unbiased and meaningful analyses. An in-depth understanding may necessitate primary data collection and qualitative studies. A feasible direction available with existing data such as the National Longitudinal Survey of Youth 1997 (NLSY97) is to assess earned college credits and grades rather than years of education beyond high school.

As indicated in Figure 2 , beyond a more in-depth usage of the attainment information, we argue that more effective conceptualization of the education-health relationship as a developmental life course process will lead to important findings. For instance, two studies published in 2016 used the NLSY97 data to model how gradual increases in education predict within-individual changes in health ( 39 ; 81 ). Both research teams found that gradual accumulation of schooling quantity over time was not associated with gradual improvements in health. The investigators interpreted the null findings as an absence of causal effects of education on health, especially once they included important confounders (defined as cognitive and noncognitive skills and social background). Alternatively, perhaps the within-individual models did not register health because education is a long-term, developing trajectory that cannot be reduced to point-in-time changes in exposure. Criticisms about the technical aspects of theses studies notwithstanding ( 59 ), we believe that these studies and others like them, which wrestle with the question of how to capture education as a long-term process grounded in the broader social context, and how this process is linked to adult health, are desirable and necessary.

Education as (re)producer of inequality

The predominant theoretical framework for studying education and health focuses on how education increases skills, improves problem-solving, enhances employment prospects, and thus opens access to other resources. In sociology, however, education is viewed not (only) as increasing human capital but as a “sieve more than a ladder” ( 126 ), an institution that reproduces inequality across generations ( 54 ; 65 ; 103 ; 114 ). The mechanisms of the reproduction of inequality are multifarious, encompassing systematic differences in school resources, quality of instruction, academic opportunities, peer influences, or teacher expectations ( 54 ; 114 ; 132 ). The dual role of education, both engendering and constraining social opportunities, has been recognized from the discipline’s inception ( 52 ) and has remained the dominant perspective in sociology of education ( 18 ; 126 ). Health disparities research, which has largely dismissed the this perspective as “specious” ( 93 ), could benefit from pivoting toward this complex sociological paradigm.

As demonstrated in Figure 2 , parental SES and other background characteristics are key social determinants that set the stage for one’s educational experiences ( 20 ; 120 ). These characteristics, however, shape not just attainment, but the entire educational and social trajectories that drive and result in particular attainment ( 21 ; 69 ). Their effects range from the differential quality and experiences in daycare or preschool settings ( 6 ), K-12 education ( 24 ; 136 ), as well as postsecondary schooling ( 5 ; 127 ). As a result of systematically different experiences of schooling over the early life course stratified by parental SES, children of low educated parents are unlikely to complete higher education: over half of individuals with college degrees by age 24 came from families in the top quartile of family income compared to just 10% in the bottom quartile ( 23 ).

Unfortunately, prior research has generally operationalized the differences in educational opportunities as confounders of the education-health association or as “selection bias” to be statistically controlled, or best as a moderating influence ( 10 ; 19 ). Rather than remove the important life course effects from the equation, studies that seek to understand how educational and health differences unfold over the life course, and even across generations could yield greater insight ( 50 ; 70 ). A life course, multigenerational approach can provide important recommendations for interventions seeking to avoid the unintended consequence of increasing disparities. Insofar as socially advantaged individuals are generally better positioned to take advantage of interventions, research findings can be used to ensure that policies and programs result in decreasing, rather than unintentionally widening, educational and health disparities.

Education and health in social context

Finally, perhaps the most important and policy-relevant emerging direction to improving our understanding of the education-health relationship is to view both as inextricably embedded within the broad social context. As we highlight in Figure 2 , this context underlies every feature of the development of educational disparities in health. In contrast to the voluminous literature focusing on individual-level schooling and health, there has been a “startling lack of attention to the social/political/economic context” in which the relationships are grounded ( 33 ). By context, we mean the structure of a society that varies across time and place, encompassing all major institutions, policy environments, as well as gender, race/ethnicity, age, and socioeconomic stratification. Under what circumstances, conditions, and policies are the associations between education and health stronger or weaker?

Within the United States, the most relevant units of geo-political boundaries generating distinct policy contexts are states, although smaller geographic units are also pertinent ( 44 ; 100 ). Since the 1980s, the federal government has devolved an increasing range of key socioeconomic, political, and health-care decisions to states. This decentralization has resulted in increasing diversity across states in conditions for a healthy life ( 96 ; 101 ). A recent study demonstrates how different environments across US states yield vastly different health returns to education ( 100 ). State-level characteristics had little impact on adults with high education, whose disability levels were similarly low regardless of their state of residence. In contrast, disability levels of low-educated adults were not only high but also varied substantially across states: disability was particularly high in states that have invested less in the social welfare of its residents, such as Mississippi, Kentucky, and West Virginia. Highly-educated adults, particularly white adults and men who can convert education into other resources most readily, use personal resources to protect their health like a ‘personal firewall’ ( 97 ). Their less-educated peers, meanwhile, are vulnerable without social safety nets. Demonstrating the potential for informing policy in this area, the findings directly identify state policies that influence the extent to which educational attainment matters for health and longevity. These include economic policies including state income tax structures and education expenditures per capita, as well as policies influencing social cohesion in a state, such as income inequality and unemployment rates. Beyond the US, investigators can leverage differences in political systems across countries to assess the impact of different welfare regimes on the education-health associations, as some European researchers began generating ( 41 ; 82 ).

Similar to variation across geo-political boundaries, research on variation across time can highlight policies and conditions that mitigate or inflate health disparities. How has the education-health association changed over time? In recent decades, the association has become increasingly strong, with widening disparities in health outcomes across education ( 53 ; 77 ; 86 ; 116 ; 143 ). These increases started in the 1980s ( 17 ) at the same time that social inequality began rising with the political embrace of pro-market neoliberal policies ( 33 ). Since then, the United States has been increasingly marked by plummeting economic wellbeing (except for the wealthiest Americans), growing economic segregation, emerging mass incarceration, downward social mobility, and despair in many working-class communities ( 17 ; 95 ; 129 ). Conversely, in the two decades prior (1960s and 70s), social disparities in health were decreasing ( 1 ; 72 ). During those decades, many pro-social policies such as Civil rights legislation, War on Poverty programs, and racial desegregation were improving social inequalities. Macro-level political forces, clearly, can influence not only social but also health inequalities ( 104 ). Two facts follow: growing disparities are not inevitable and changes in the education-health relationship may be strongly linked to social policies. While some of the growth in educational inequalities may be attributable to changes in educational composition of the population with increasingly negatively select groups of adults at the lowest levels of schooling, these compositional changes likely play only a minor role in the overall trends ( 38 ; 58 ). Linking education and health to the broader social context brings to the forefront the ways in which we, as individuals and a collective society, produce and maintain health disparities.

Implications for Policy and Practice

Reducing macro-level inequalities in health will require macro-level interventions. Technological progress and educational expansion over the past several decades have not decreased disparities; on the contrary, educational disparities in health and mortality have grown in the US. Moreover, the consistent, durable relationship between education and health and the multitude of mechanisms linking them suggests that programs targeting individual behaviors will have limited impact to counteract disparities. Thus, we argue that future findings from the new research directions proposed here can be used to intervene at the level of social contexts to alter educational trajectories from an early age, with the ultimate goal of reducing health disparities. We note two promising avenues for policy development.

One potential solution may focus on universal federal and state-level investment in the education and well-being of children early in the life course to disrupt the reproduction of social inequalities and change subsequent educational trajectories. Several experimental early-education programs such as the Perry Preschool Project and Carolina Abecedarian Project have demonstrated substantial, lasting, and wide-ranging benefits, including improved adult health ( 25 ; 57 ; 102 ). These programs provided intensive, exceptionally high-quality, and diverse services to children, and it is these characteristics that appear central to their success ( 138 ). Further research on the qualitative and social dimensions of education and their effects on health can inform future model educational programs and interventions across all ages.

Another important issue for both researchers and policymakers pertains to postsecondary enrollment and attrition, and their effects on health. Educational expansion in the college-for-all era has yielded high post-secondary enrollment, but also unacceptable dropout rates with multiple detrimental consequences, including high rates of student debt ( 64 ) and stigma ( 76 ), which may negatively affect health. Emerging studies found that college dropouts fail to benefit from their postsecondary investments. Next we need to understand under what circumstances college goers do reap health benefits, or how their postsecondary experience can be modified to improve their health.

For both of these avenues, effective implementation will need further research on the specific institutional characteristics and social contexts that shape the schooling effects. However, in designing interventions and policies, we need to be aware of the dual role of education as a drive and reproducer of inequality. Individuals from advantaged backgrounds may be better positioned to take advantage of new educational opportunities, and thus any interventions and programs need to ensure that marginalized populations have equal or greater access in order to avoid the unintended consequence of further intensifying disparities. Finally, researchers and policymakers should engage in a dialogue such that researchers effectively communicate their insights and recommendations to policymakers, and policymakers convey the needs and challenges of their practices to researchers.

Education and health are central to individual and population well-being. They are also inextricably embedded in the social context and structure. Future research needs to expand beyond the individual-focused analyses and hypothesize upstream ( 96 ), taking a contextual approach to understanding education and health. Such an approach will require interdisciplinary collaborations, innovations in conceptual models, and rich data sources. The three directions for further research on health returns to education we outlined above can help generate findings that will inform effective educational and health policies and interventions to reduce disparities. During this critical time when health differences are widening and less educated Americans are experiencing social and health declines, research and policy has the opportunity to make a difference and improve the health and well-being of our population.

Contributor Information

Anna Zajacova, Western University.

Elizabeth M. Lawrence, University of North Carolina.

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Edited by Maya Alkateb-Chami, Jane Choi, Jeannette Garcia Coppersmith, Ron Grady, Phoebe A. Grant-Robinson, Pennie M. Gregory, Jennifer Ha, Woohee Kim, Catherine E. Pitcher, Elizabeth Salinas, Caroline Tucker, Kemeyawi Q. Wahpepah

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The Harvard Educational Review (HER) is a scholarly journal of opinion and research in education. The Editorial Board aims to publish pieces from interdisciplinary and wide-ranging fields that advance our understanding of educational theory, equity, and practice. HER encourages submissions from established and emerging scholars, as well as from practitioners working in the field of education. Since its founding in 1930, HER has been central to elevating pieces and debates that tackle various dimensions of educational justice, with circulation to researchers, policymakers, teachers, and administrators.

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Editorial Board

2023-2024 Harvard Educational Review Editorial Board Members

Maya Alkateb-Chami Development and Partnerships Editor, 2023-2024 Editor, 2022-2024 [email protected]

Maya Alkateb-Chami is a PhD student at the Harvard Graduate School of Education. Her research focuses on the role of schooling in fostering just futures—specifically in relation to language of instruction policies in multilingual contexts and with a focus on epistemic injustice. Prior to starting doctoral studies, she was the Managing Director of Columbia University’s Human Rights Institute, where she supported and co-led a team of lawyers working to advance human rights through research, education, and advocacy. Prior to that, she was the Executive Director of Jusoor, a nonprofit organization that helps conflict-affected Syrian youth and children pursue their education in four countries. Alkateb-Chami is a Fulbright Scholar and UNESCO cultural heritage expert. She holds an MEd in Language and Literacy from Harvard University; an MSc in Education from Indiana University, Bloomington; and a BA in Political Science from Damascus University, and her research on arts-based youth empowerment won the annual Master’s Thesis Award of the U.S. Society for Education Through Art.

Jane Choi Editor, 2023-2025

Jane Choi is a second-year PhD student in Sociology with broad interests in culture, education, and inequality. Her research examines intra-racial and interracial boundaries in US educational contexts. She has researched legacy and first-generation students at Ivy League colleges, families served by Head Start and Early Head Start programs, and parents of pre-K and kindergarten-age children in the New York City School District. Previously, Jane worked as a Research Assistant in the Family Well-Being and Children’s Development policy area at MDRC and received a BA in Sociology from Columbia University.

Jeannette Garcia Coppersmith Content Editor, 2023-2024 Editor, 2022-2024 [email protected]

Jeannette Garcia Coppersmith is a fourth-year Education PhD student in the Human Development, Learning and Teaching concentration at the Harvard Graduate School of Education. A former public middle and high school mathematics teacher and department chair, she is interested in understanding the mechanisms that contribute to disparities in secondary mathematics education, particularly how teacher beliefs and biases intersect with the social-psychological processes and pedagogical choices involved in math teaching. Jeannette holds an EdM in Learning and Teaching from the Harvard Graduate School of Education where she studied as an Urban Scholar and a BA in Environmental Sciences from the University of California, Berkeley.

Ron Grady Editor, 2023-2025

Ron Grady is a second-year doctoral student in the Human Development, Learning, and Teaching concentration at the Harvard Graduate School of Education. His central curiosities involve the social worlds and peer cultures of young children, wondering how lived experience is both constructed within and revealed throughout play, the creation of art and narrative, and through interaction with/production of visual artifacts such as photography and film. Ron also works extensively with educators interested in developing and deepening practices rooted in reflection on, inquiry into, and translation of the social, emotional, and aesthetic aspects of their classroom ecosystems. Prior to his doctoral studies, Ron worked as a preschool teacher in New Orleans. He holds a MS in Early Childhood Education from the Erikson Institute and a BA in Psychology with Honors in Education from Stanford University.

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• Research article
• Open access
• Published: 22 April 2024

What does it mean to be good at peer reviewing? A multidimensional scaling and cluster analysis study of behavioral indicators of peer feedback literacy

• Yi Zhang   ORCID: orcid.org/0000-0001-7153-0955 1 ,
• Christian D. Schunn 2 &
• Yong Wu 3  

International Journal of Educational Technology in Higher Education volume  21 , Article number:  26 ( 2024 ) Cite this article

Metrics details

Peer feedback literacy is becoming increasingly important in higher education as peer feedback has substantially grown as a pedagogical approach. However, quality of produced feedback, a key behavioral aspect of peer feedback literacy, lacks a systematic and evidence-based conceptualization to guide research, instruction, and system design. We introduce a novel framework involving six conceptual dimensions of peer feedback quality that can be measured and supported in online peer feedback contexts: reviewing process, rating accuracy, feedback amount, perceived comment quality, actual comment quality, and feedback content. We then test the underlying dimensionality of student competencies through correlational analysis, Multidimensional Scaling, and cluster analysis, using data from 844 students engaged in online peer feedback in a university-level course. The separability of the conceptual dimensions is largely supported in the cluster analysis. However, the cluster analysis also suggests restructuring perceived and actual comment quality in terms of initial impact and ultimate impact. The Multi-Dimensional Scaling suggests the dimensions of peer feedback can be conceptualized in terms of relative emphasis on expertise vs. effort and on overall review quality vs. individual comment quality. The findings provide a new road map for meta-analyses, empirical studies, and system design work focused on peer feedback literacy.

Introduction

Peer review, as a student-centered pedagogical approach, has become widely used in higher education (Gao et al., 2023 ; Kerman et al., 2024 ). In recent years, higher education research has begun to investigate peer feedback literacy (Dawson et al., 2023 ; Little et al., 2024 ; Nieminen & Carless, 2023 ). Peer feedback literacy refers to the capacity to comprehend, interpret, provide, and effectively utilize feedback in a peer review context (Dong et al., 2023 ; Man et al., 2022 ; Sutton, 2012 ). It supports learning processes by fostering critical thinking, enhancing interpersonal skills, and promoting active engagement in course groupwork (Hattie & Timperley, 2007 ). To date, conceptualizations of peer feedback literacy have primarily been informed by interview and survey data (e.g., Dong et al., 2023 ; Woitt et al., 2023 ; Zhan, 2022 ). These methods have provided valuable insights into learners’ knowledge of and attitudes towards peer feedback. However, they have not generally examined the behavioral aspect of peer feedback literacy, especially the quality of the feedback that students with high feedback literacy produce (Gielen et al., 2010 ). Knowledge and attitudes to not always translate into effective action (Becheikh et al., 2010 ; Huberman, 1990 ), and the quality of feedback that students actually produce play an important role in their learning from the process (Lu et al., 2023 ; Topping, 2023 ; Zheng et al., 2020 ; Zong et al., 2021a , b ).

In order to make progress on behavioral indicators of peer feedback literacy, it is important to recognize a lack of agreement in the literature in defining the key aspects of “quality” of peer feedback. In fact, collectively, a large number of different conceptualizations and measures have been explored (Jin et al., 2022 ; Noroozi et al., 2022 ; Patchan et al., 2018 ; Tan & Chen, 2022 ), and their interrelationships have not been examined. Further, much of the literature to date has investigated peer feedback quality at the level of individual comments and ratings. Individual comments and ratings can be driven by characteristics of the object being studied, moment-to-moment fluctuations in attention and motivation, as well as feedback literacy of the reviewer. To understand the dimensionality of feedback literacy, investigations of reviewing quality must be conducted at the level of reviewers, not individual comments. For example, specific comment choices may have weak or even negative relationships based upon alternative structures (i.e., a reviewer might choose between two commenting strategies in a given comment), but at the individual level (as a reviewer) the same elements might be positively correlated reflecting more general attitudes or skills.

Integrating across many prior conceptualizations and empirical investigations, we propose a new conceptual framework that broadly encompasses many dimensions of reviewing quality. We then present an empirical investigation using multidimensional scaling and cluster analysis of the dimensionality of peer reviewing quality at the reviewer level (i.e., the behavioral component of peer feedback literacy), utilizing a large peer review dataset in a university-level course.

Literature review

While most studies of peer reviewing quality have tended to focus on one or two specific measures, a few authors considered peer reviewing quality more broadly. In building a tool for university computer science courses that automatically evaluates peer feedback quality, Ramachandran et al. ( 2017 ) proposed conceptualizing peer feedback quality in terms of six specific measures such as whether the feedback is aligned to the rubric dimensions, whether the feedback has a balanced tone, and whether the feedback was copied from another review. Since their focus was on tool building, they did not consider the dimensionality of the specific measures.

More recently, Zhang and Schunn ( 2023 ) proposed a five-dimensional conceptual framework for assessing the quality of peer reviews: accuracy, amount, impact, features, and content. The larger framework was not tested, and only a few specific measures were studied in university biology courses. Using a broader literature review, here we expand and refine this framework to include six dimensions: reviewing process, rating accuracy, amount, perceived comment quality, actual comment quality, and feedback content (see Table  1 ).

The first dimension, reviewing process , pertains to varying methods students use while reviewing, significantly affecting feedback quality. This includes aspects like time devoted to reviewing or use of drafting of comments. Studies conducted in a lab and on MOOCs found a positive correlation between efficient time management and improved review accuracy (Piech et al., 2013 ; Smith & Ratcliff, 2004 ). However, such easily-collected process measures may not accurately represent effective processes. For instance, time logged in an online system may not reflect actual working time. Indeed, another study found that spending slightly below-average time reviewing correlated with higher reliability (Piech et al., 2013 ). To address this concern, Xiong and Schunn ( 2021 ) focused on whether reviews were completed in extremely short durations (< 10 min) instead of measuring the total time spent on a review. Similarly, numerous revisions while completing a review could signify confusion rather than good process. Methods like eye-tracking (Bolzer et al., 2015 ) or think-aloud techniques (Wolfe, 2005 ) could provide additional measures related to peer reviewing processes.

The second dimension, rating accuracy , focuses on peer assessment and the alignment between a reviewers’ ratings and a document’s true quality. True document quality is ideally determined by expert ratings, but sometimes, more indirect measures like instructor or mean multi-peer ratings are used. Across varied terms like error, validity, or accuracy, the alignment of peer ratings with document quality is typically quantified either by measuring agreement (i.e., distance from expert ratings—Li et al., 2016 ; Xiong & Schunn, 2021 ) or by measuring evaluator consistency (i.e., having similar rating patterns across document and dimension—Schunn et al., 2016 ; Tong et al., 2023 ; Zhang et al., 2020 ). Past studies typically focused on specific indicators without examining their interrelations or their relationship with other dimensions of peer reviewing quality.

The third dimension, amount , can pertain to one peer feedback component (i.e., the number or length of comments in a review) or broadly to peer review (i.e., the number of reviews completed). Conceptually, this dimension may be especially driven by motivation levels and attitudes towards peer feedback, but the amount produced can also reflect understanding and expertise (Zong et al., 2022 ). Within amount, a distinction has been made between frequency—defined by the number of provided comments or completed reviews as a kind of behavioral engagement (Zong et al., 2021b ; Zou et al., 2018 )—and comment length, indicating cognitive engagement and learning value (Zong et al., 2021a ). While comment length logically correlates with quality dimensions focused on the contents of a comment (i.e., adding explanations or potential solutions increases length), its associations with many other dimensions, like accuracy in ratings, reviewing process, or feedback content, remain unexplored.

The fourth dimension, perceived comment quality , focuses on various aspects of comments from the feedback recipient’s perspective; peer feedback is a form of communication, and recipients are well positioned to judge communication quality. This dimension may focus on the initial processing of the comment (e.g., was it understandable?; Nelson & Schunn, 2009 ) or its ultimate impact (e.g., was it accepted? was it helpful for revision? did the recipient learn something?; Huisman et al., 2018 ), typically measured using Likert scales. Modern online peer feedback systems used in university contexts often incorporate a step where feedback recipients rate the received feedback’s helpfulness (Misiejuk & Wasson, 2021 ). However, little research has explored the relation between perceived comment quality and other reviewing quality dimensions, especially at the grain size of a reviewer (e.g., do reviewers whose comments are seen as helpful tend to put more effort into reviewing, produce more accurate ratings, or focus on critical aspects of the document?).

The fifth dimension, actual comment quality , revolves around the comment’s objective impact (e.g., is it implementable or what is processed by the reviewer?) or concrete, structural elements influencing its impact (e.g., does it provide a solution, is the tone balanced, does it explain the problem?). This impact, or feedback uptake (Wichmann et al., 2018 ), typically pertains to the comment’s utilization in revisions (Wu & Schunn, 2021b ). However, as comments might be ignored for reasons unrelated to their comment content (Wichmann et al., 2018 ), some studies focus upon potential impact (Cui et al., 2021 ; Leijen, 2017 ; Liu & Sadler, 2003 ; Wu & Schunn, 2023 ). Another approach examines comment features likely to influence their impact, like the inclusion of explanations, suggestions, or praise (Lu et al., 2023 ; Tan & Chen, 2022 ; Tan et al., 2023 ; Wu & Schunn, 2021a ). Most studies on actual comment quality have explored how students utilize received feedback (van den Bos & Tan, 2019 ; Wichmann et al., 2018 ; Wu & Schunn, 2023 ), with much less attention given to how actual comment quality is related to other dimensions of feedback quality, particularly at the level of feedback providers (e.g., do reviewers who provide more explanations give more accurate ratings?).

The last dimension, feedback content , shifts from the structure of the comment (e.g., was it said in a useful way?) to the semantic topic of the content (i.e., was the comment about the right content?). Content dimensions explored thus far include whether the review comments were aligned with the rubric provided by the instructor (Ramachandran et al., 2017 ), whether they covered the whole object being reviewed (Ramachandran et al., 2017 ), whether they attend to the most problematic issues in the document from an expert perspective (e.g., Gao et al., 2019 ), whether they focused on pervasive/global issues (Patchan et al., 2018 ) or higher-order writing issues (van den Bos & Tan, 2019 ) rather than sentence level issues, whether the comments were self-plagiarized or copied from other reviewers (Ramachandran et al., 2017 ), or whether multiple peers also referred to these same issues (Leijen, 2017 ), which indicates that many readers find it problematic. It is entirely possible that reviewers give many well-structured comments but generally avoid addressing the most central or challenging issues in a document perhaps because those require more work or intellectual risk (Gao et al., 2019 ). It could be argued that high peer feedback literacy involves staying focused on critical issues. However, it is unknown whether reviewers who tend to give well-structured comments when provided a focused rubric tend to give more accurate ratings or address critical issues in the documents they are reviewing.

The present study

In the current study, we seek to expand upon existing research on peer reviewing quality by examining its multidimensional structure, at the reviewer level, in essence developing behavioral dimensions of peer review literacy. This exploration is critical for theoretical and practical reasons: the dimensionality of peer reviewing quality is foundational to conceptualizations of peer feedback literacy, sampling plans for studies of peer feedback literacy, and interventions designed to improve peer feedback literacy.

To make it possible to study many dimensions and specific measures of peer feedback quality at once, we leverage an existing dataset involving a university-level course in which different studies have collectively developed measures and data for a wide range of reviewing quality constructs. We further add a few measures that can be efficiently added using mathematical formulas. As a result, we are able to study five of the six dimensions (all but feedback content) and specifically eighteen specific measures. Our primary research question is: What is the interrelationship among different dimensions and measures of peer reviewing quality at the reviewer level? Specifically, we postulate that the five dimensions—reviewing process, rating accuracy, amount of feedback, perceived comment quality, and actual comment quality—are interconnected in strong ways within a dimension and in relatively weaker ways across dimensions.

Participants

Participants were 844 students enrolled in an Advanced Placement course in writing at nine secondary schools distributed across the United States. Participants were predominately female (59%; 4% did not report gender) and Caucasian (55%), followed by Asian (12%), African American (7%), and Hispanic/Latino (7%; 19% did not report their ethnicity). The mean age was 17 years ( SD  = 1.8).

The Advanced Placement (AP) course is a higher education course aimed for advanced high school students who are ready for instruction at the higher education level, similar to cases in which advanced high school students attend a course at a local university. This course is typically taken by students who are only 1 year younger than first-year university students, the point at which this specific course is normally taken, and by students who are especially likely to go on to university and wanting to be able to get credit for university-level courses to reduce their university degree time and costs. Since student enrollment in higher education and studies of their behavior focus on their general level of proficiency rather than age, students in this course should be thought of as more similar to entry-level university students than they are to general high school students. Further, the course is designed and regulated by a national organization, the College Board, to be entirely equivalent to a university course in content and grading.

The AP English Language and Composition course focuses on argument and rhetorical elements of writing, equivalent to the first-writing course that is required at most universities in the US (College Board, 2021 ). For a study on peer feedback within this course context, students from a school were taught by the same teacher, interacting online for peer feedback activities. Nine eligible teachers with experience in teaching this AP course were recruited. The selected teachers met the following eligibility criteria: 1) they had previously taught the course; 2) they were teaching at least two sections of the course during the study period; 3) they agreed to participate in training on effective use of the online peer feedback approach and study requirements; 4) they were willing to assign a specific writing assignment to students and require peer feedback on that assignment using the online system; and 5) they collectively represented a diverse range of regions in the US and student demographics.

All data were collected via an online peer-reviewing system, Peerceptiv ( https://peerceptiv.com ; Schunn, 2016 ), a system predominantly used at the university level (Yu & Schunn, 2023 ). The system provided access to data organized by research ids to protect student privacy, and the Human Research Protection Office at the University of Pittsburgh approved research on this data.

The task involved analyzing rhetorical strategies in a provided persuasive essay, with the specific prompt from a prior year’s end-of-year test. Students needed to: 1) submit their own document using a pseudonym; 2) review at least four randomly-assigned peer documents and rate document quality using seven 7-point rubrics, along with providing comments supported by seven corresponding comment prompts; 3) back-evaluate the helpfulness of received comments using a 5-point scale; and 4) submit a revised document. Half the students used an experimental version of the system that necessitated the use of a revision planning tool to indicate which received comments would be implemented in the revision and their priority, on a 3-point scale.

Measures of reviewing quality

This study examined 18 measures of peer reviewing quality in five categories (see Table  2 ), utilizing both simple mathematics calculations (like mean rating and word count) and labor-intensive hand-coding for comment content analysis. The hand-coding was aggregated from four prior studies (Wu & Schunn, 2020a , b , 2021a , b ). This analysis introduces new elements: novel measures (priority, agreement measures, number of features), integration of measures not previously examined together, and an analysis of the data aggregated to the reviewer-level data. The detailed hand coding processes are described in the prior publications. Here we give brief summaries of the measures and their coding reliabilities.

The amount and mean perceived comment quality measures were directly calculated by computer from the raw data. All the remaining measures involving data coded by a trained pool of four undergraduate research assistants and six writing experts (all with years of experience teaching writing and familiarity with specific writing assignment and associated reviewing rubrics used in the study). A given measure involved either undergraduate assistants or expertise depending upon the level of expertise required. Artifacts were coded by two individuals to assess reliability; discrepancies were resolved through discussion to improve data quality. Coding on each dimension for both research assistants and experts involved a training phase in which coders iteratively coded a subset of artifacts and discussed discrepancies/revised coding manuals until acceptable levels of reliability were obtained.

Before all hand-coding procedures, comments were segmented by idea units by a research assistant if a given textbox included comments about two or more different issues, resulting in 24,816 comments. Then, given the focus of the writing assignment on learning complex elements of writing, comments about low-level writing issues (i.e., typos, spelling, grammar) were excluded from further coding and data analysis, resulting in 20,912 high-level comments.

Reviewing process

The duration of the review process was determined by the recorded time interval between the point at which a document assigned for review was downloaded and the point at which the completed review was submitted. Reviews completed within a duration of less than 10 min were likely expedited, given the need to attend to seven dimensions, even for the expert evaluators (Xiong & Schunn, 2021 ). Here we used the converse, Not speeded, to refer to positive feedback quality.

Rating accuracy

As a reminder, both students and experts rated the quality of the documents submitted for peer review based on seven 1-to-7 scales. Accuracy was separately defined in terms of both rating agreement and rating consistency (Tong et al., 2023 ; Xiong & Schunn, 2021 ) and in regard to the standard of expert judgments and mean peer judgments. Expert judgments are considered the gold standard of validity, but mean peer judgments are often the only available standard in studies with very large datasets. In practice, expert ratings and mean peer ratings are often highly correlated (Li et al., 2016 ).

Expert agreement was calculated as the negative sum absolute value of the difference between the true document quality (assessed by the trained experts; kappa = 0.73) and each reviewer’s judgment of the document quality across the seven dimensions and documents. The peer agreement was calculated in the same way but used the mean ratings across the peers rather than the expert judgments. The negation was applied to the absolute error to create an accuracy measurement in which higher values indicated higher accuracy. A constant of 42 (maximum difference 6 * 7 dimensions) was added to minus the absolute error to make most values sit between 0 and 42, with 42 reflecting high accuracy.

The expert consistency was calculated as the linear correlation between true document quality (assessed by the trained experts) and each reviewer’s judgment of document quality across the seven dimensions. The peer consistency was calculated in the same way, but again using mean ratings across the peers instead of expert ratings. Values logically could vary between -1 and 1 (though rarely were valued negatively), with higher consistency values indicating higher accuracy.

Students were assigned a fixed number of documents to review but sometimes did not complete all the required reviews and sometimes completed extra reviews. Within a review, students had to give at least one comment for each of the seven dimensions, but they could give more than one comment for each dimension, and there was no required minimum or maximum length for a given comment. As a result, students could provide one or several comments, each consisting of a single word or several paragraphs. Prior research on peer feedback has found that comments involving more than 50 words typically include useful information for receivers (Wu & Schunn, 2020a ) and tend to produce more learning for comment providers (Zong et al., 2022 ). Also, there may be a tradeoff in that students could submit fewer longer comments or more total comments. Thus, we also calculated the percentage of long comments: the total number of long comments (i.e., having more than 50 words) divided by the total number of comments. To capture the three main ways in which amount varied, we included the number of reviews completed for the peer assessment task ( #Reviews ), the mean number of comments ( #Comments ), the percentage of long comments ( %Long comments ).

Perceived comment quality

All students were required to judge the helpfulness of the comments they received on a 1-to-5 scale, and students using the experimental revision planning interface had to select the priority with which they would implement each comment on a 1-to-3 scale. Both sources of data address perceived comment quality, with one involving a mixture of the value of comments for revision and for learning, and the other focusing exclusively on whether comments were useful for revision. Thus, two measures were created, one based on mean comment helpfulness and the other based on mean comment implementation priority.

Actual comment quality

The measures of actual comment quality were based upon hand-coding by the experts and trained research assistants. The first approach to actual comment quality focused on the usefulness of the comments. The experts coded feedback in terms of implementation in three ways: implementable (Kappa = 0.92), implemented (Kappa = 0.76) and improvement (Kappa = 0.69). Implementable ( N  = 14,793) refers to whether the comments could be addressed in a revision (i.e., was not pure praise or just a summary of the author’s work). By contrast, implemented refers to whether the comment was incorporated in the submitted document revision: a change in the document was made that could be related to the provided comment ( N  = 11,252). Non implementable comments were coded, by definition, as not implemented.

The improvement value of comments was coded by the experts for how much the comment could improve document quality ( N  = 1,758; kappa = 0.69). The two points were given when addressing a comment would measurably improve the document’s quality on the given rubrics (e.g., moving from a 5 to a 7 on a scale). One point was awarded when addressing a comment could improve document quality in terms of the underlying rubric dimensions, but not by enough to be a measurable change on the 7-point rubric scale. No points were given when addressing a comment would not improve document quality, would make the document worse, or would involve both improvements and declines (Wu & Schunn, 2020b ). Improvement was only coded for implementable comments.

Another approach to actual comment quality focused on specific feedback features that typically are helpful for revision or learning (Jin et al., 2022 ; Tan & Chen, 2022 ; Wu & Schunn, 2020a ). Research assistants coded the comments for whether they provided a specific solution (Kappa = 0.76), gave a more general suggestion for how to address the problem but not an exact solution (Kappa = 0.79), explicitly identified the problem (Kappa = 0.81) and explained the problem (Kappa = 0.80). Separate measures were created for each feature, calculated as the percentage of comments having each feature. There was also an aggregate features measure, calculated as the mean number of features contained in each comment ( #Features ).

Data analysis

Table 4 in Appendix shows the descriptive information for all the measures of peer reviewing quality at the reviewer level. Because of the different data sources, N s varied substantially across measures. In addition, some of the measures tended to have relatively high means with negative skews, like # of reviews, rating agreement and rating accuracy measures, and helpfulness. Other measures had low means and positive skews, like the specific comment features, %implemented, and mean improvement.

The peer reviewing measures were first analyzed for reliability across reviews. Conceptually, this analysis examines whether reviewers tended to give reviews of similar quality on a given measure across the reviews they completed on an assignment. It is possible that the reviewing quality was heavily influenced by characteristics of the object being reviewed (e.g., it is easier to include solutions for weaker documents), and thus not a measure of peer feedback literacy. Other incidental factors such as order of the reviews or presence of a distraction could also have mattered, but those factors likely would influence the reliability of all the measures rather than just isolated measures.

Reliability was measured via an Intraclass Correlation Coefficient ( ICC ). There are many forms of ICC. In terms of the McGraw and Wong ( 1996 ) framework, we used ICC(k) , which represents the agreement reliability (meaning level of deviation from the same exact rating) across k ratings (typically 4 in our data) using a one-way random analysis, because each reviewer was given different documents to review from a larger population of possible documents (Koo & Li, 2016 ). We used the Landis and Koch ( 1977 ) guidelines for interpreting the ICC values for the reliabilty of the measures: almost perfect for values above 0.80; substantial for values from 0.61 to 0.80; moderate for values of 0.41 to 0.60; fair for values of 0.21 to 0.40; slight for values of 0.01 to 0.20, and poor for values less than 0.

Finally, to show the interrelationship among the variables, we conducted a three-step process of: 1) pairwise correlation among all measures with pairwise rather than listwise deletion given the high variability in measure N s (see Figure 3 in Appendix for sample sizes); 2) multidimensional scaling (MDS) applied to the correlation data to visualize the relative proximity of the measures; and 3) a hierarchical cluster analysis applied to the results of the correlation matrix to extract conceptual clusters of measures. We conducted the analyses in R: pairwise correlations using the “GGally” package, multidimensional scaling using the “magrittr” package, and hierarchical clustering using the “stats” package. For the correlational analysis, we applied both linear and rank correlations since there were strong skews to some of the measures. The two approaches produced similar results. 

Multidimensional scaling (MDS) is a statistical technique employed to visualize and analyze similarities or dissimilarities among variables in a dataset (Carroll & Arabie, 1998 ). While factor analysis is typically used to test or identify separable dimensions among many specific measures, MDS provides a useful visualization of the interrelationship of items, particularly when some items inherently straddle multiple dimensions. It also provides a useful visualization of the interrelationship of the dimensions rather than just of the items (Ding, 2006 ). The outcome of MDS is a “map” that represents these variables as points within a lower-dimensional space, typically two or three dimensions, while preserving the original distances between them as much as possible (Hout et al., 2013 ). In the current study, we chose two dimensions based on a scree plot of the eigenvalues associated with each MDS dimension (see Figure 4 in Appendix )—two dimensions offered a relatively good fit and is much easier to visualize. We expected measures within each conceptual dimension to sit close together on the MDS map.

Hierarchical cluster analysis, a general family of algorithms, is the dominant approach to grouping similar variables or data points based on their attributes or features (Murtagh & Contreras, 2017 ). It can accurately identify patterns within even small datasets (e.g., a 18*18 correlation matrix) since it leverages pairwise distances between all contributing measures. Further, it requires no assumptions about cluster shape, while other common algorithms like K-means assume that clusters are spherical and have similar sizes. However, we note that a K-means clustering algorithm produced similar clusters, so the findings are not heavily dependent upon the algorithm that was used. We expected to obtain the five clusters of dimensions as proposed in Table  2 .

We first focus on the reliability of each peer reviewing quality (defined by agreement in values across completed reviews). As shown by the blue cells along the main diagonal in Fig.  1 , the measures #Comments , %Long comments, and %Suggestions showed perfect reliability [0.81, 0.95], and the rest of measures of peer reviewing quality, except Improvement , showed moderate to substantial reliability [0.48, 0.79]. Only the Improvement measure showed only a slight level of measure reliability across reviews. It is possible that Improvement is primarily driven by the document, perhaps because some documents have limited potential for improvement or that the scope for improvement relies heavily on the match between what the reviewer can perceive and the specific needs of the document. Taken together, all but one measures fell within the required range to be considered reliable, and the results involving the Improvement measure may be inconsistent due to measurement noise.

Measure reliability (diagonal cells and white font; / = NA) and linear inter-correlations (bold values for p  < .05, and italic values for not significant values), organized by proposed peer feedback literacy dimension

The linear measure intercorrelation shown in Fig.  1 revealed that, except for Peer agreement , almost all measures were significantly and positively correlated with one another. Based on the patterns, one of the measures— %Long comment was removed from the amount dimension in the analyses that follow. Focusing on the rating accuracy measures, except for the correlations of Peer agreement with Expert consistency and Peer consistency with Expert agreement , all the correlations were positive and statistically significant. Further, the correlations with measures in other dimensions were often non-significant and always small: Peer agreement , Max out group  = 0.18; Peer consistency , Max out group  = 0.18; Expert Agreement , Max out group  = 0.31; and Expert consistency , Max out group  = 0.26. The largest cross-dimension correlations occurred for the two expert accuracy measures with actual comment quality measures such as %Implementable and Improvement . The results supported treating these measures as one dimension, even though the intercorrelations within the dimensions are relatively weak.

Turning to the amount dimension, we again note that %Long Comments only had weak correlations with #Reviews and #Comments ( r  = 0.15 and r  = 0.1) compared to the relationship between #Reviews and #Comments ( r  = 0.63). After removing %Long Comments from the amount dimension, the in-group correlation ( r  = 0.63) was much higher than the out-group correlations ( #Reviews , Max out group  = 0.14; #Comments , Max out group  = 0.20). Thus, the support for treating amount involving #Review and #Comment as a dimension was strong.

The support for a perceived quality dimension, as originally defined, was weak. The two measures correlated with one another at only r  = 0.22. Correlations with measures in the amount and accuracy dimensions were also weak, but correlations with actual quality measures were often moderate. The results suggest some reorganization of the perceived and actual comment quality dimensions may be required.

Finally, the eight measures in the actual comment quality dimension were generally highly correlated with one another. Compared with out-group correlations, %Implementable (Min in group  = 0.32 > Max out group  = 0.31), %Implemented (Min in group  = 0.41 > Max out group  = 0.34), #Features (Min in group  = 0.51 > Max out group  = 0.39) and %Identifications (Min in group  = 0.34 > Max out group  = 0.25) were well nested in this group. However, some measures blurred somewhat with measures in the perceived comment quality dimension: Improvement (Min in group  = 0.22 < Max out group  = 0.28), %Solution (Min in group  = 0.22 < Max out group  = 0.28), %Suggestions (Min in group  = 0.34 = Max out group  = 0.34), %Explanation s (Min in group  = 0.34 < Max out group  = 0.36). Overall, the correlation results revealed some overlap with perceived comment quality, particularly for %Solutions .

Further, to better understand the similarities among these measures, MDS and hierarchical cluster analysis were conducted based on measure intercorrelation data. The MDS results are shown in Fig.  2 . Conceptually, the y-axis shows reviewing quality measures reflecting effort near the bottom (e.g., #Reviews and #Comments ) and reviewing quality measures reflecting expertise near the top (e.g., the rating accuracy group and Improvement ). By contrast, the x-axis involves review-level measures to the left and comment-level measures to the right. This pattern within the intercorrelations of measures illustrates what can be learned from MDS but would be difficult to obtain from factor analysis.

A map of peer feedback literacy based upon MDS and cluster analysis

The clustering algorithm produced five clusters, which are labeled and color-coded in Fig.  2 . The five clusters were roughly similar to the originally hypothesized construct groups in Table  1 , especially treating rating accuracy, amount, and reviewing process as distinct from each other and from perceived/actual comment quality. However, perceived and actual comment quality did not separate as expected. In particular, %Long comments and %Solutions were clustered together with helpfulness and priority. We call this new dimension Initial Impact , reflecting comment recipients’ initial reactions to feedback (without having to consider the feedback in light of the document). The remaining measures that were all proposed to be part of the actual comment quality dimension clustered together. We propose calling this dimension Ultimate Impact , reflecting their closer alignment with actual improvements and the aspects of comments are most likely to lead to successful revisions.

General discussion

Understanding the fundamental structure of peer review literacy from a behavioral/skills perspective, rather than a knowledge and attitudes perspective, was a fundamental goal of our study. With the support of online tools, peer feedback is becoming increasingly implemented in a wide range of educational levels, contexts, disciplines, course types, and student tasks. As a form of student-centered instruction, it has great potential to improve learning outcomes, but then also critically depends upon effective full participation by students in their reviewing roles. Thus, it is increasingly important to fully conceptualize and develop methods for studying and supporting peer feedback literacy.

Our proposed framework sought to build a coherent understanding of peer reviewing quality in terms of six dimensions—reviewing process, rating accuracy, feedback amount, perceived comment quality, actual comment quality, and feedback content—offering a unified perspective on the scattered and fragmented notions of peer reviewing quality (Ramachandran et al., 2017 ; Yu et al., 2023 ). Consolidating the disparate measures from the literature into dimensions serves many purposes. For example, when university educators understand the intricacies of the reviewing process, they can provide clearer guidance and training to students, improving the quality of feedback provided. Similarly, understanding the dimensional structure can organize investigations of what dimensions are shaped by various kinds of supports/training, and which dimensions influence later learning outcomes, either for the reviewer or the reviewee.

Unlike previous studies that primarily explored relationships among reviewing quality dimensions at the comment level (Leijen, 2017 ; Misiejuk et al., 2021 ; Wu & Schunn, 2021b ), our work focuses on the reviewer level, as an approach to studying the behavioral elements of peer feedback literacy, complementing the predominantly knowledge and attitudes focus of interview and survey studies on peer feedback literacy. This shift in level of analysis is important because reviewing quality measures at the comment level might exhibit weak or even negative relationships due to varied structures or intentions. However, at the reviewer level, these measures may exhibit positive correlations, reflecting overarching strategies, motivations, or skills.

Our findings, as illustrated by the linear intercorrelation analysis, illuminate the interconnectedness of various factors shaping peer feedback literacy. The overarching theme emerging from the analysis is inherent multidimensionality, a facet of peer review literacy that has been previously highlighted in the literature (Winstone & Carless, 2020 ). The findings from the current study also suggest that peer feedback literacy can be organized into relative emphasis on expertise vs. effort and relative focus on review level vs. comment level aspects. It will be especially interesting to examine the ways in which training and motivational interventions will shape those different behavioral indicators.

It is important to note that survey-based measures of peer feedback literacy find that all of the dimensions identified within those studies were strongly correlated with one another (e.g., Dong et al., 2023 ) to the extent that the pragmatic and theoretical value of measuring them separately could be questioned. For example, feedback-related knowledge and willingness to participate in peer feedback were correlated at r  = 0.76, and all the specific indicators on those scales loaded at high levels on their factors. Within our framework, those factors could be framed as representing the expertise vs. effort ends of the literacy continuum, which our findings suggest should be much more distinguishable than r  = 0.76. Indeed, we also found dimensional structure to peer feedback literacy, but the correlations among dimensions are quite low, and even the correlations among different measures within a dimension were modest. If survey measures are going to be used in future studies on peer feedback literacy, it will be important to understand how well they align with students’ actual behaviors. Further, it may be necessary to extend what kinds of behaviors are represented on those surveys.

Our findings also suggest a strong separation of ratings accuracy from the impact that comments will have on their recipients. While there is some relationship among the two, particularly when focusing on expert evaluations of ratings accuracy and expert judgments of the improvement that comments will produce, the r  = 0.26 correlation is quite modest. Both constructs represent a kind of expertise in the reviewer. But ratings accuracy represents attending to and successfully diagnosing all the relative strengths and weaknesses in a submission (i.e., having a review level competence), whereas improvements offered in comments can involve more focus on particular problems, not requiring a reviewer to be broadly proficient (i.e., having a comment level competence). In addition, especially useful comments require not only diagnosing a major problem but also offering strategies addressing that problem.

Our findings also help to situate specific measures of feedback quality that have drawn increasing attention given their pragmatic value in data collection and data analysis: comment helpfulness ratings and %long comments. On the one hand, they are central measures of the larger landscape of peer feedback quality. On the other hand, the only represent one dimension of peer feedback literacy: the initial impact of the comments being produced. Adding in rating accuracy measures like peer agreement or peer consistency and amount measures likes # of reviews and # of comments, would provide a broader measurement of peer feedback literacy while still involving easy to collect and analyze measures. To capture the ultimate impact dimension, studies would need to invest in the laborious task of hand coding comments (which is still much less laborious than hand coding implementation or expensive than expert coding of improvement) or perhaps turn to innovations in NLP and generative AI to automatically code large numbers of comments.

Limitations and future directions

We note two key limitations to our current study. First, the exclusion of the feedback content dimension potentially left out a critical element of the peer reviewing process, which future research should aim to incorporate, possibly being implemented with larger datasets like the current study through automated techniques like Natural Language Processing (Ramachandran et al., 2017 ). Such technological advances could reveal hidden patterns and correlations with the feedback content, potentially leading to a more comprehensive understanding of peer reviewing quality.

Furthermore, the geographical and contextual constraints of our study—specifically to an introductory university writing course in the US using one online peer feedback system—may limit the generalizability of our findings. Past meta-analyses and meta-regressions suggest minimal impact of discipline, class size, or system setup on the validity of peer review ratings or the derived learning benefits (Li et al., 2016 ; Sanchez et al., 2017 ; Yu & Schunn, 2023 ). However, it is important to replicate these novel findings of this study across various contexts.

Our investigation sought to investigate the dimensionality of peer feedback literacy, a common concern in ongoing research in this domain. In previous studies, the dimensionality of peer feedback literacy has been largely shaped by data from interviews and surveys (e.g., Dong et al., 2023 ; Zhan, 2022 ). These approaches offered valuable insights into domains of learners’ knowledge and attitudes towards peer feedback (e.g., willingness to participate in peer feedback is separable from appreciation of its value or knowledge of how to participate). But such studies provided little insight into the ways in which the produced feedback varied in quality, which can be taken as the behavioral dimensions of peer feedback literacy (Gielen et al., 2010 ). It is important to note that knowledge and attitudes do not always lead to effective action (Becheikh et al., 2010 ; Huberman, 1990 ). Further, the actual quality of feedback generated by students is crucial for their learning through the process (Lu et al., 2023 ; Topping, 2023 ; Zheng et al., 2020 ; Zong et al., 2021a , b ). In the current study, we have clarified the dimensionality of the behavioral dimension, highlighting motivational vs. expertise elements at review and comment levels. These findings can become the new foundations of empirical investigations and theoretical development into the causes and consequences of peer feedback literacy.

The current findings offer actionable recommendations for practitioners (e.g., instructors, teaching assistants, instructional designers, online tool designers) for enhancing peer review processes. First, our findings identify four major areas in which practitioners need to scaffold peer reviewing quality: rating accuracy, the volume of feedback, the initial impact of comments, and the ultimate impact of comments. Different approaches are likely required to address these major areas given their relative emphasis on effort vs. expertise. For example, motivational scaffolds and considerations (e.g., workload) may be needed for improving volume of feedback, back-evaluations steps for improvement of initial impact, training on rubric dimensions for improvement of rating accuracy, and training on effective feedback structure for improvement of ultimate impact. Secondly, when resources are very constrained such that assessing the more labor-intensive dimensions of feedback quality is not possible, the multidimensional scale results suggest that length of comments and helpfulness ratings can be taken as an efficiently assessed proxy for overall feedback quality involving a mixture of effort and expertise at the review and comment levels.

Availability of data and materials

The data used to support the findings of this study are available from the corresponding author upon request.

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This study was supported by the Philosophy and Social Sciences Planning Youth Project of Guangdong Province under grant [GD24YJY01], and The National Social Science Fund of China [23BYY154].

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Zhang, Y., Schunn, C.D. & Wu, Y. What does it mean to be good at peer reviewing? A multidimensional scaling and cluster analysis study of behavioral indicators of peer feedback literacy. Int J Educ Technol High Educ 21 , 26 (2024). https://doi.org/10.1186/s41239-024-00458-1

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Deciphering the influence: academic stress and its role in shaping learning approaches among nursing students: a cross-sectional study

• Rawhia Salah Dogham 1 ,
• Heba Fakieh Mansy Ali 1 ,
• Asmaa Saber Ghaly 3 ,
• Nermine M. Elcokany 2 ,
• Mohamed Mahmoud Seweid 4 &
• Ayman Mohamed El-Ashry   ORCID: orcid.org/0000-0001-7718-4942 5  

BMC Nursing volume  23 , Article number:  249 ( 2024 ) Cite this article

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Nursing education presents unique challenges, including high levels of academic stress and varied learning approaches among students. Understanding the relationship between academic stress and learning approaches is crucial for enhancing nursing education effectiveness and student well-being.

This study aimed to investigate the prevalence of academic stress and its correlation with learning approaches among nursing students.

Design and Method

A cross-sectional descriptive correlation research design was employed. A convenient sample of 1010 nursing students participated, completing socio-demographic data, the Perceived Stress Scale (PSS), and the Revised Study Process Questionnaire (R-SPQ-2 F).

Most nursing students experienced moderate academic stress (56.3%) and exhibited moderate levels of deep learning approaches (55.0%). Stress from a lack of professional knowledge and skills negatively correlates with deep learning approaches (r = -0.392) and positively correlates with surface learning approaches (r = 0.365). Female students showed higher deep learning approach scores, while male students exhibited higher surface learning approach scores. Age, gender, educational level, and academic stress significantly influenced learning approaches.

Academic stress significantly impacts learning approaches among nursing students. Strategies addressing stressors and promoting healthy learning approaches are essential for enhancing nursing education and student well-being.

Nursing implication

Understanding academic stress’s impact on nursing students’ learning approaches enables tailored interventions. Recognizing stressors informs strategies for promoting adaptive coping, fostering deep learning, and creating supportive environments. Integrating stress management, mentorship, and counseling enhances student well-being and nursing education quality.

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Introduction

Nursing education is a demanding field that requires students to acquire extensive knowledge and skills to provide competent and compassionate care. Nursing education curriculum involves high-stress environments that can significantly impact students’ learning approaches and academic performance [ 1 , 2 ]. Numerous studies have investigated learning approaches in nursing education, highlighting the importance of identifying individual students’ preferred approaches. The most studied learning approaches include deep, surface, and strategic approaches. Deep learning approaches involve students actively seeking meaning, making connections, and critically analyzing information. Surface learning approaches focus on memorization and reproducing information without a more profound understanding. Strategic learning approaches aim to achieve high grades by adopting specific strategies, such as memorization techniques or time management skills [ 3 , 4 , 5 ].

Nursing education stands out due to its focus on practical training, where the blend of academic and clinical coursework becomes a significant stressor for students, despite academic stress being shared among all university students [ 6 , 7 , 8 ]. Consequently, nursing students are recognized as prone to high-stress levels. Stress is the physiological and psychological response that occurs when a biological control system identifies a deviation between the desired (target) state and the actual state of a fitness-critical variable, whether that discrepancy arises internally or externally to the human [ 9 ]. Stress levels can vary from objective threats to subjective appraisals, making it a highly personalized response to circumstances. Failure to manage these demands leads to stress imbalance [ 10 ].

Nursing students face three primary stressors during their education: academic, clinical, and personal/social stress. Academic stress is caused by the fear of failure in exams, assessments, and training, as well as workload concerns [ 11 ]. Clinical stress, on the other hand, arises from work-related difficulties such as coping with death, fear of failure, and interpersonal dynamics within the organization. Personal and social stressors are caused by an imbalance between home and school, financial hardships, and other factors. Throughout their education, nursing students have to deal with heavy workloads, time constraints, clinical placements, and high academic expectations. Multiple studies have shown that nursing students experience higher stress levels compared to students in other fields [ 12 , 13 , 14 ].

Research has examined the relationship between academic stress and coping strategies among nursing students, but no studies focus specifically on the learning approach and academic stress. However, existing literature suggests that students interested in nursing tend to experience lower levels of academic stress [ 7 ]. Therefore, interest in nursing can lead to deep learning approaches, which promote a comprehensive understanding of the subject matter, allowing students to feel more confident and less overwhelmed by coursework and exams. Conversely, students employing surface learning approaches may experience higher stress levels due to the reliance on memorization [ 3 ].

Understanding the interplay between academic stress and learning approaches among nursing students is essential for designing effective educational interventions. Nursing educators can foster deep learning approaches by incorporating active learning strategies, critical thinking exercises, and reflection activities into the curriculum [ 15 ]. Creating supportive learning environments encouraging collaboration, self-care, and stress management techniques can help alleviate academic stress. Additionally, providing mentorship and counselling services tailored to nursing students’ unique challenges can contribute to their overall well-being and academic success [ 16 , 17 , 18 ].

Despite the scarcity of research focusing on the link between academic stress and learning methods in nursing students, it’s crucial to identify the unique stressors they encounter. The intensity of these stressors can be connected to the learning strategies employed by these students. Academic stress and learning approach are intertwined aspects of the student experience. While academic stress can influence learning approaches, the choice of learning approach can also impact the level of academic stress experienced. By understanding this relationship and implementing strategies to promote healthy learning approaches and manage academic stress, educators and institutions can foster an environment conducive to deep learning and student well-being.

Hence, this study aims to investigate the correlation between academic stress and learning approaches experienced by nursing students.

Study objectives

Assess the levels of academic stress among nursing students.

Assess the learning approaches among nursing students.

Identify the relationship between academic stress and learning approach among nursing students.

Identify the effect of academic stress and related factors on learning approach and among nursing students.

Materials and methods

Research design.

A cross-sectional descriptive correlation research design adhering to the STROBE guidelines was used for this study.

A research project was conducted at Alexandria Nursing College, situated in Egypt. The college adheres to the national standards for nursing education and functions under the jurisdiction of the Egyptian Ministry of Higher Education. Alexandria Nursing College comprises nine specialized nursing departments that offer various nursing specializations. These departments include Nursing Administration, Community Health Nursing, Gerontological Nursing, Medical-Surgical Nursing, Critical Care Nursing, Pediatric Nursing, Obstetric and Gynecological Nursing, Nursing Education, and Psychiatric Nursing and Mental Health. The credit hour system is the fundamental basis of both undergraduate and graduate programs. This framework guarantees a thorough evaluation of academic outcomes by providing an organized structure for tracking academic progress and conducting analyses.

Participants and sample size calculation

The researchers used the Epi Info 7 program to calculate the sample size. The calculations were based on specific parameters such as a population size of 9886 students for the academic year 2022–2023, an expected frequency of 50%, a maximum margin of error of 5%, and a confidence coefficient of 99.9%. Based on these parameters, the program indicated that a minimum sample size of 976 students was required. As a result, the researchers recruited a convenient sample of 1010 nursing students from different academic levels during the 2022–2023 academic year [ 19 ]. This sample size was larger than the minimum required, which could help to increase the accuracy and reliability of the study results. Participation in the study required enrollment in a nursing program and voluntary agreement to take part. The exclusion criteria included individuals with mental illnesses based on their response and those who failed to complete the questionnaires.

socio-demographic data that include students’ age, sex, educational level, hours of sleep at night, hours spent studying, and GPA from the previous semester.

Tool two: the perceived stress scale (PSS)

It was initially created by Sheu et al. (1997) to gauge the level and nature of stress perceived by nursing students attending Taiwanese universities [ 20 ]. It comprises 29 items rated on a 5-point Likert scale, where (0 = never, 1 = rarely, 2 = sometimes, 3 = reasonably often, and 4 = very often), with a total score ranging from 0 to 116. The cut-off points of levels of perceived stress scale according to score percentage were low < 33.33%, moderate 33.33–66.66%, and high more than 66.66%. Higher scores indicate higher stress levels. The items are categorized into six subscales reflecting different sources of stress. The first subscale assesses “stress stemming from lack of professional knowledge and skills” and includes 3 items. The second subscale evaluates “stress from caring for patients” with 8 items. The third subscale measures “stress from assignments and workload” with 5 items. The fourth subscale focuses on “stress from interactions with teachers and nursing staff” with 6 items. The fifth subscale gauges “stress from the clinical environment” with 3 items. The sixth subscale addresses “stress from peers and daily life” with 4 items. El-Ashry et al. (2022) reported an excellent internal consistency reliability of 0.83 [ 21 ]. Two bilingual translators translated the English version of the scale into Arabic and then back-translated it into English by two other independent translators to verify its accuracy. The suitability of the translated version was confirmed through a confirmatory factor analysis (CFA), which yielded goodness-of-fit indices such as a comparative fit index (CFI) of 0.712, a Tucker-Lewis index (TLI) of 0.812, and a root mean square error of approximation (RMSEA) of 0.100.

Tool three: revised study process questionnaire (R-SPQ-2 F)

It was developed by Biggs et al. (2001). It examines deep and surface learning approaches using only 20 questions; each subscale contains 10 questions [ 22 ]. On a 5-point Likert scale ranging from 0 (never or only rarely true of me) to 4 (always or almost always accurate of me). The total score ranged from 0 to 80, with a higher score reflecting more deep or surface learning approaches. The cut-off points of levels of revised study process questionnaire according to score percentage were low < 33%, moderate 33–66%, and high more than 66%. Biggs et al. (2001) found that Cronbach alpha value was 0.73 for deep learning approach and 0.64 for the surface learning approach, which was considered acceptable. Two translators fluent in English and Arabic initially translated a scale from English to Arabic. To ensure the accuracy of the translation, they translated it back into English. The translated version’s appropriateness was evaluated using a confirmatory factor analysis (CFA). The CFA produced several goodness-of-fit indices, including a Comparative Fit Index (CFI) of 0.790, a Tucker-Lewis Index (TLI) of 0.912, and a Root Mean Square Error of Approximation (RMSEA) of 0.100. Comparative Fit Index (CFI) of 0.790, a Tucker-Lewis Index (TLI) of 0.912, and a Root Mean Square Error of Approximation (RMSEA) of 0.100.

Ethical considerations

The Alexandria University College of Nursing’s Research Ethics Committee provided ethical permission before the study’s implementation. Furthermore, pertinent authorities acquired ethical approval at participating nursing institutions. The vice deans of the participating institutions provided written informed consent attesting to institutional support and authority. By giving written informed consent, participants confirmed they were taking part voluntarily. Strict protocols were followed to protect participants’ privacy during the whole investigation. The obtained personal data was kept private and available only to the study team. Ensuring participants’ privacy and anonymity was of utmost importance.

Tools validity

The researchers created tool one after reviewing pertinent literature. Two bilingual translators independently translated the English version into Arabic to evaluate the applicability of the academic stress and learning approach tools for Arabic-speaking populations. To assure accuracy, two additional impartial translators back-translated the translation into English. They were also assessed by a five-person jury of professionals from the education and psychiatric nursing departments. The scales were found to have sufficiently evaluated the intended structures by the jury.

Pilot study

A preliminary investigation involved 100 nursing student applicants, distinct from the final sample, to gauge the efficacy, clarity, and potential obstacles in utilizing the research instruments. The pilot findings indicated that the instruments were accurate, comprehensible, and suitable for the target demographic. Additionally, Cronbach’s Alpha was utilized to further assess the instruments’ reliability, demonstrating internal solid consistency for both the learning approaches and academic stress tools, with values of 0.91 and 0.85, respectively.

Data collection

The researchers convened with each qualified student in a relaxed, unoccupied classroom in their respective college settings. Following a briefing on the study’s objectives, the students filled out the datasheet. The interviews typically lasted 15 to 20 min.

Data analysis

The data collected were analyzed using IBM SPSS software version 26.0. Following data entry, a thorough examination and verification were undertaken to ensure accuracy. The normality of quantitative data distributions was assessed using Kolmogorov-Smirnov tests. Cronbach’s Alpha was employed to evaluate the reliability and internal consistency of the study instruments. Descriptive statistics, including means (M), standard deviations (SD), and frequencies/percentages, were computed to summarize academic stress and learning approaches for categorical data. Student’s t-tests compared scores between two groups for normally distributed variables, while One-way ANOVA compared scores across more than two categories of a categorical variable. Pearson’s correlation coefficient determined the strength and direction of associations between customarily distributed quantitative variables. Hierarchical regression analysis identified the primary independent factors influencing learning approaches. Statistical significance was determined at the 5% (p < 0.05).

Table  1 presents socio-demographic data for a group of 1010 nursing students. The age distribution shows that 38.8% of the students were between 18 and 21 years old, 32.9% were between 21 and 24 years old, and 28.3% were between 24 and 28 years old, with an average age of approximately 22.79. Regarding gender, most of the students were female (77%), while 23% were male. The students were distributed across different educational years, a majority of 34.4% in the second year, followed by 29.4% in the fourth year. The students’ hours spent studying were found to be approximately two-thirds (67%) of the students who studied between 3 and 6 h. Similarly, sleep patterns differ among the students; more than three-quarters (77.3%) of students sleep between 5- to more than 7 h, and only 2.4% sleep less than 2 h per night. Finally, the student’s Grade Point Average (GPA) from the previous semester was also provided. 21% of the students had a GPA between 2 and 2.5, 40.9% had a GPA between 2.5 and 3, and 38.1% had a GPA between 3 and 3.5.

Figure  1 provides the learning approach level among nursing students. In terms of learning approach, most students (55.0%) exhibited a moderate level of deep learning approach, followed by 25.9% with a high level and 19.1% with a low level. The surface learning approach was more prevalent, with 47.8% of students showing a moderate level, 41.7% showing a low level, and only 10.5% exhibiting a high level.

Nursing students? levels of learning approach (N=1010)

Figure  2 provides the types of academic stress levels among nursing students. Among nursing students, various stressors significantly impact their academic experiences. Foremost among these stressors are the pressure and demands associated with academic assignments and workload, with 30.8% of students attributing their high stress levels to these factors. Challenges within the clinical environment are closely behind, contributing significantly to high stress levels among 25.7% of nursing students. Interactions with peers and daily life stressors also weigh heavily on students, ranking third among sources of high stress, with 21.5% of students citing this as a significant factor. Similarly, interaction with teachers and nursing staff closely follow, contributing to high-stress levels for 20.3% of nursing students. While still significant, stress from taking care of patients ranks slightly lower, with 16.7% of students reporting it as a significant factor contributing to their academic stress. At the lowest end of the ranking, but still notable, is stress from a perceived lack of professional knowledge and skills, with 15.9% of students experiencing high stress in this area.

Nursing students? levels of academic stress subtypes (N=1010)

Figure  3 provides the total levels of academic stress among nursing students. The majority of students experienced moderate academic stress (56.3%), followed by those experiencing low academic stress (29.9%), and a minority experienced high academic stress (13.8%).

Nursing students? levels of total academic stress (N=1010)

Table  2 displays the correlation between academic stress subscales and deep and surface learning approaches among 1010 nursing students. All stress subscales exhibited a negative correlation regarding the deep learning approach, indicating that the inclination toward deep learning decreases with increasing stress levels. The most significant negative correlation was observed with stress stemming from the lack of professional knowledge and skills (r=-0.392, p < 0.001), followed by stress from the clinical environment (r=-0.109, p = 0.001), stress from assignments and workload (r=-0.103, p = 0.001), stress from peers and daily life (r=-0.095, p = 0.002), and stress from patient care responsibilities (r=-0.093, p = 0.003). The weakest negative correlation was found with stress from interactions with teachers and nursing staff (r=-0.083, p = 0.009). Conversely, concerning the surface learning approach, all stress subscales displayed a positive correlation, indicating that heightened stress levels corresponded with an increased tendency toward superficial learning. The most substantial positive correlation was observed with stress related to the lack of professional knowledge and skills (r = 0.365, p < 0.001), followed by stress from patient care responsibilities (r = 0.334, p < 0.001), overall stress (r = 0.355, p < 0.001), stress from interactions with teachers and nursing staff (r = 0.262, p < 0.001), stress from assignments and workload (r = 0.262, p < 0.001), and stress from the clinical environment (r = 0.254, p < 0.001). The weakest positive correlation was noted with stress stemming from peers and daily life (r = 0.186, p < 0.001).

Table  3 outlines the association between the socio-demographic characteristics of nursing students and their deep and surface learning approaches. Concerning age, statistically significant differences were observed in deep and surface learning approaches (F = 3.661, p = 0.003 and F = 7.983, p < 0.001, respectively). Gender also demonstrated significant differences in deep and surface learning approaches (t = 3.290, p = 0.001 and t = 8.638, p < 0.001, respectively). Female students exhibited higher scores in the deep learning approach (31.59 ± 8.28) compared to male students (29.59 ± 7.73), while male students had higher scores in the surface learning approach (29.97 ± 7.36) compared to female students (24.90 ± 7.97). Educational level exhibited statistically significant differences in deep and surface learning approaches (F = 5.599, p = 0.001 and F = 17.284, p < 0.001, respectively). Both deep and surface learning approach scores increased with higher educational levels. The duration of study hours demonstrated significant differences only in the surface learning approach (F = 3.550, p = 0.014), with scores increasing as study hours increased. However, no significant difference was observed in the deep learning approach (F = 0.861, p = 0.461). Hours of sleep per night and GPA from the previous semester did not exhibit statistically significant differences in deep or surface learning approaches.

Table  4 presents a multivariate linear regression analysis examining the factors influencing the learning approach among 1110 nursing students. The deep learning approach was positively influenced by age, gender (being female), educational year level, and stress from teachers and nursing staff, as indicated by their positive coefficients and significant p-values (p < 0.05). However, it was negatively influenced by stress from a lack of professional knowledge and skills. The other factors do not significantly influence the deep learning approach. On the other hand, the surface learning approach was positively influenced by gender (being female), educational year level, stress from lack of professional knowledge and skills, stress from assignments and workload, and stress from taking care of patients, as indicated by their positive coefficients and significant p-values (p < 0.05). However, it was negatively influenced by gender (being male). The other factors do not significantly influence the surface learning approach. The adjusted R-squared values indicated that the variables in the model explain 17.8% of the variance in the deep learning approach and 25.5% in the surface learning approach. Both models were statistically significant (p < 0.001).

Nursing students’ academic stress and learning approaches are essential to planning for effective and efficient learning. Nursing education also aims to develop knowledgeable and competent students with problem-solving and critical-thinking skills.

The study’s findings highlight the significant presence of stress among nursing students, with a majority experiencing moderate to severe levels of academic stress. This aligns with previous research indicating that academic stress is prevalent among nursing students. For instance, Zheng et al. (2022) observed moderated stress levels in nursing students during clinical placements [ 23 ], while El-Ashry et al. (2022) found that nearly all first-year nursing students in Egypt experienced severe academic stress [ 21 ]. Conversely, Ali and El-Sherbini (2018) reported that over three-quarters of nursing students faced high academic stress. The complexity of the nursing program likely contributes to these stress levels [ 24 ].

The current study revealed that nursing students identified the highest sources of academic stress as workload from assignments and the stress of caring for patients. This aligns with Banu et al.‘s (2015) findings, where academic demands, assignments, examinations, high workload, and combining clinical work with patient interaction were cited as everyday stressors [ 25 ]. Additionally, Anaman-Torgbor et al. (2021) identified lectures, assignments, and examinations as predictors of academic stress through logistic regression analysis. These stressors may stem from nursing programs emphasizing the development of highly qualified graduates who acquire knowledge, values, and skills through classroom and clinical experiences [ 26 ].

The results regarding learning approaches indicate that most nursing students predominantly employed the deep learning approach. Despite acknowledging a surface learning approach among the participants in the present study, the prevalence of deep learning was higher. This inclination toward the deep learning approach is anticipated in nursing students due to their engagement with advanced courses, requiring retention, integration, and transfer of information at elevated levels. The deep learning approach correlates with a gratifying learning experience and contributes to higher academic achievements [ 3 ]. Moreover, the nursing program’s emphasis on active learning strategies fosters critical thinking, problem-solving, and decision-making skills. These findings align with Mahmoud et al.‘s (2019) study, reporting a significant presence (83.31%) of the deep learning approach among undergraduate nursing students at King Khalid University’s Faculty of Nursing [ 27 ]. Additionally, Mohamed &Morsi (2019) found that most nursing students at Benha University’s Faculty of Nursing embraced the deep learning approach (65.4%) compared to the surface learning approach [ 28 ].

The study observed a negative correlation between the deep learning approach and the overall mean stress score, contrasting with a positive correlation between surface learning approaches and overall stress levels. Elevated academic stress levels may diminish motivation and engagement in the learning process, potentially leading students to feel overwhelmed, disinterested, or burned out, prompting a shift toward a surface learning approach. This finding resonates with previous research indicating that nursing students who actively seek positive academic support strategies during academic stress have better prospects for success than those who do not [ 29 ]. Nebhinani et al. (2020) identified interface concerns and academic workload as significant stress-related factors. Notably, only an interest in nursing demonstrated a significant association with stress levels, with participants interested in nursing primarily employing adaptive coping strategies compared to non-interested students.

The current research reveals a statistically significant inverse relationship between different dimensions of academic stress and adopting the deep learning approach. The most substantial negative correlation was observed with stress arising from a lack of professional knowledge and skills, succeeded by stress associated with the clinical environment, assignments, and workload. Nursing students encounter diverse stressors, including delivering patient care, handling assignments and workloads, navigating challenging interactions with staff and faculty, perceived inadequacies in clinical proficiency, and facing examinations [ 30 ].

In the current study, the multivariate linear regression analysis reveals that various factors positively influence the deep learning approach, including age, female gender, educational year level, and stress from teachers and nursing staff. In contrast, stress from a lack of professional knowledge and skills exert a negative influence. Conversely, the surface learning approach is positively influenced by female gender, educational year level, stress from lack of professional knowledge and skills, stress from assignments and workload, and stress from taking care of patients, but negatively affected by male gender. The models explain 17.8% and 25.5% of the variance in the deep and surface learning approaches, respectively, and both are statistically significant. These findings underscore the intricate interplay of demographic and stress-related factors in shaping nursing students’ learning approaches. High workloads and patient care responsibilities may compel students to prioritize completing tasks over deep comprehension. This pressure could lead to a surface learning approach as students focus on meeting immediate demands rather than engaging deeply with course material. This observation aligns with the findings of Alsayed et al. (2021), who identified age, gender, and study year as significant factors influencing students’ learning approaches.

Deep learners often demonstrate better self-regulation skills, such as effective time management, goal setting, and seeking support when needed. These skills can help manage academic stress and maintain a balanced learning approach. These are supported by studies that studied the effect of coping strategies on stress levels [ 6 , 31 , 32 ]. On the contrary, Pacheco-Castillo et al. study (2021) found a strong significant relationship between academic stressors and students’ level of performance. That study also proved that the more academic stress a student faces, the lower their academic achievement.

Strengths and limitations of the study

This study has lots of advantages. It provides insightful information about the educational experiences of Egyptian nursing students, a demographic that has yet to receive much research. The study’s limited generalizability to other people or nations stems from its concentration on this particular group. This might be addressed in future studies by using a more varied sample. Another drawback is the dependence on self-reported metrics, which may contain biases and mistakes. Although the cross-sectional design offers a moment-in-time view of the problem, it cannot determine causation or evaluate changes over time. To address this, longitudinal research may be carried out.

Notwithstanding these drawbacks, the study substantially contributes to the expanding knowledge of academic stress and nursing students’ learning styles. Additional research is needed to determine teaching strategies that improve deep-learning approaches among nursing students. A qualitative study is required to analyze learning approaches and factors that may influence nursing students’ selection of learning approaches.

According to the present study’s findings, nursing students encounter considerable academic stress, primarily stemming from heavy assignments and workload, as well as interactions with teachers and nursing staff. Additionally, it was observed that students who experience lower levels of academic stress typically adopt a deep learning approach, whereas those facing higher stress levels tend to resort to a surface learning approach. Demographic factors such as age, gender, and educational level influence nursing students’ choice of learning approach. Specifically, female students are more inclined towards deep learning, whereas male students prefer surface learning. Moreover, deep and surface learning approach scores show an upward trend with increasing educational levels and study hours. Academic stress emerges as a significant determinant shaping the adoption of learning approaches among nursing students.

Implications in nursing practice

Nursing programs should consider integrating stress management techniques into their curriculum. Providing students with resources and skills to cope with academic stress can improve their well-being and academic performance. Educators can incorporate teaching strategies that promote deep learning approaches, such as problem-based learning, critical thinking exercises, and active learning methods. These approaches help students engage more deeply with course material and reduce reliance on surface learning techniques. Recognizing the gender differences in learning approaches, nursing programs can offer gender-specific support services and resources. For example, providing targeted workshops or counseling services that address male and female nursing students’ unique stressors and learning needs. Implementing mentorship programs and peer support groups can create a supportive environment where students can share experiences, seek advice, and receive encouragement from their peers and faculty members. Encouraging students to reflect on their learning processes and identify effective study strategies can help them develop metacognitive skills and become more self-directed learners. Faculty members can facilitate this process by incorporating reflective exercises into the curriculum. Nursing faculty and staff should receive training on recognizing signs of academic stress among students and providing appropriate support and resources. Additionally, professional development opportunities can help educators stay updated on evidence-based teaching strategies and practical interventions for addressing student stress.

Data availability

The datasets generated and/or analysed during the current study are not publicly available due to restrictions imposed by the institutional review board to protect participant confidentiality, but are available from the corresponding author on reasonable request.

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Acknowledgements

Our sincere thanks go to all the nursing students in the study. We also want to thank Dr/ Rasha Badry for their statistical analysis help and contribution to this study.

The research was not funded by public, commercial, or non-profit organizations.

Open access funding provided by The Science, Technology & Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB).

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Ayman M. El-Ashry & Rawhia S. Dogham: conceptualization, preparation, and data collection; methodology; investigation; formal analysis; data analysis; writing-original draft; writing-manuscript; and editing. Heba F. Mansy Ali & Asmaa S. Ghaly: conceptualization, preparation, methodology, investigation, writing-original draft, writing-review, and editing. Nermine M. Elcokany & Mohamed M. Seweid: Methodology, investigation, formal analysis, data collection, writing-manuscript & editing. All authors reviewed the manuscript and accept for publication.

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Dogham, R.S., Ali, H.F.M., Ghaly, A.S. et al. Deciphering the influence: academic stress and its role in shaping learning approaches among nursing students: a cross-sectional study. BMC Nurs 23 , 249 (2024). https://doi.org/10.1186/s12912-024-01885-1

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What the data says about abortion in the U.S.

Pew Research Center has conducted many surveys about abortion over the years, providing a lens into Americans’ views on whether the procedure should be legal, among a host of other questions.

In a  Center survey  conducted nearly a year after the Supreme Court’s June 2022 decision that  ended the constitutional right to abortion , 62% of U.S. adults said the practice should be legal in all or most cases, while 36% said it should be illegal in all or most cases. Another survey conducted a few months before the decision showed that relatively few Americans take an absolutist view on the issue .

Find answers to common questions about abortion in America, based on data from the Centers for Disease Control and Prevention (CDC) and the Guttmacher Institute, which have tracked these patterns for several decades:

How many abortions are there in the U.S. each year?

How has the number of abortions in the u.s. changed over time, what is the abortion rate among women in the u.s. how has it changed over time, what are the most common types of abortion, how many abortion providers are there in the u.s., and how has that number changed, what percentage of abortions are for women who live in a different state from the abortion provider, what are the demographics of women who have had abortions, when during pregnancy do most abortions occur, how often are there medical complications from abortion.

This compilation of data on abortion in the United States draws mainly from two sources: the Centers for Disease Control and Prevention (CDC) and the Guttmacher Institute, both of which have regularly compiled national abortion data for approximately half a century, and which collect their data in different ways.

The CDC data that is highlighted in this post comes from the agency’s “abortion surveillance” reports, which have been published annually since 1974 (and which have included data from 1969). Its figures from 1973 through 1996 include data from all 50 states, the District of Columbia and New York City – 52 “reporting areas” in all. Since 1997, the CDC’s totals have lacked data from some states (most notably California) for the years that those states did not report data to the agency. The four reporting areas that did not submit data to the CDC in 2021 – California, Maryland, New Hampshire and New Jersey – accounted for approximately 25% of all legal induced abortions in the U.S. in 2020, according to Guttmacher’s data. Most states, though,  do  have data in the reports, and the figures for the vast majority of them came from each state’s central health agency, while for some states, the figures came from hospitals and other medical facilities.

Discussion of CDC abortion data involving women’s state of residence, marital status, race, ethnicity, age, abortion history and the number of previous live births excludes the low share of abortions where that information was not supplied. Read the methodology for the CDC’s latest abortion surveillance report , which includes data from 2021, for more details. Previous reports can be found at  stacks.cdc.gov  by entering “abortion surveillance” into the search box.

For the numbers of deaths caused by induced abortions in 1963 and 1965, this analysis looks at reports by the then-U.S. Department of Health, Education and Welfare, a precursor to the Department of Health and Human Services. In computing those figures, we excluded abortions listed in the report under the categories “spontaneous or unspecified” or as “other.” (“Spontaneous abortion” is another way of referring to miscarriages.)

Guttmacher data in this post comes from national surveys of abortion providers that Guttmacher has conducted 19 times since 1973. Guttmacher compiles its figures after contacting every known provider of abortions – clinics, hospitals and physicians’ offices – in the country. It uses questionnaires and health department data, and it provides estimates for abortion providers that don’t respond to its inquiries. (In 2020, the last year for which it has released data on the number of abortions in the U.S., it used estimates for 12% of abortions.) For most of the 2000s, Guttmacher has conducted these national surveys every three years, each time getting abortion data for the prior two years. For each interim year, Guttmacher has calculated estimates based on trends from its own figures and from other data.

The latest full summary of Guttmacher data came in the institute’s report titled “Abortion Incidence and Service Availability in the United States, 2020.” It includes figures for 2020 and 2019 and estimates for 2018. The report includes a methods section.

In addition, this post uses data from StatPearls, an online health care resource, on complications from abortion.

An exact answer is hard to come by. The CDC and the Guttmacher Institute have each tried to measure this for around half a century, but they use different methods and publish different figures.

The last year for which the CDC reported a yearly national total for abortions is 2021. It found there were 625,978 abortions in the District of Columbia and the 46 states with available data that year, up from 597,355 in those states and D.C. in 2020. The corresponding figure for 2019 was 607,720.

The last year for which Guttmacher reported a yearly national total was 2020. It said there were 930,160 abortions that year in all 50 states and the District of Columbia, compared with 916,460 in 2019.

• How the CDC gets its data: It compiles figures that are voluntarily reported by states’ central health agencies, including separate figures for New York City and the District of Columbia. Its latest totals do not include figures from California, Maryland, New Hampshire or New Jersey, which did not report data to the CDC. ( Read the methodology from the latest CDC report .)
• How Guttmacher gets its data: It compiles its figures after contacting every known abortion provider – clinics, hospitals and physicians’ offices – in the country. It uses questionnaires and health department data, then provides estimates for abortion providers that don’t respond. Guttmacher’s figures are higher than the CDC’s in part because they include data (and in some instances, estimates) from all 50 states. ( Read the institute’s latest full report and methodology .)

While the Guttmacher Institute supports abortion rights, its empirical data on abortions in the U.S. has been widely cited by  groups  and  publications  across the political spectrum, including by a  number of those  that  disagree with its positions .

These estimates from Guttmacher and the CDC are results of multiyear efforts to collect data on abortion across the U.S. Last year, Guttmacher also began publishing less precise estimates every few months , based on a much smaller sample of providers.

The figures reported by these organizations include only legal induced abortions conducted by clinics, hospitals or physicians’ offices, or those that make use of abortion pills dispensed from certified facilities such as clinics or physicians’ offices. They do not account for the use of abortion pills that were obtained  outside of clinical settings .

(Back to top)

The annual number of U.S. abortions rose for years after Roe v. Wade legalized the procedure in 1973, reaching its highest levels around the late 1980s and early 1990s, according to both the CDC and Guttmacher. Since then, abortions have generally decreased at what a CDC analysis called  “a slow yet steady pace.”

Guttmacher says the number of abortions occurring in the U.S. in 2020 was 40% lower than it was in 1991. According to the CDC, the number was 36% lower in 2021 than in 1991, looking just at the District of Columbia and the 46 states that reported both of those years.

(The corresponding line graph shows the long-term trend in the number of legal abortions reported by both organizations. To allow for consistent comparisons over time, the CDC figures in the chart have been adjusted to ensure that the same states are counted from one year to the next. Using that approach, the CDC figure for 2021 is 622,108 legal abortions.)

There have been occasional breaks in this long-term pattern of decline – during the middle of the first decade of the 2000s, and then again in the late 2010s. The CDC reported modest 1% and 2% increases in abortions in 2018 and 2019, and then, after a 2% decrease in 2020, a 5% increase in 2021. Guttmacher reported an 8% increase over the three-year period from 2017 to 2020.

As noted above, these figures do not include abortions that use pills obtained outside of clinical settings.

Guttmacher says that in 2020 there were 14.4 abortions in the U.S. per 1,000 women ages 15 to 44. Its data shows that the rate of abortions among women has generally been declining in the U.S. since 1981, when it reported there were 29.3 abortions per 1,000 women in that age range.

The CDC says that in 2021, there were 11.6 abortions in the U.S. per 1,000 women ages 15 to 44. (That figure excludes data from California, the District of Columbia, Maryland, New Hampshire and New Jersey.) Like Guttmacher’s data, the CDC’s figures also suggest a general decline in the abortion rate over time. In 1980, when the CDC reported on all 50 states and D.C., it said there were 25 abortions per 1,000 women ages 15 to 44.

That said, both Guttmacher and the CDC say there were slight increases in the rate of abortions during the late 2010s and early 2020s. Guttmacher says the abortion rate per 1,000 women ages 15 to 44 rose from 13.5 in 2017 to 14.4 in 2020. The CDC says it rose from 11.2 per 1,000 in 2017 to 11.4 in 2019, before falling back to 11.1 in 2020 and then rising again to 11.6 in 2021. (The CDC’s figures for those years exclude data from California, D.C., Maryland, New Hampshire and New Jersey.)

The CDC broadly divides abortions into two categories: surgical abortions and medication abortions, which involve pills. Since the Food and Drug Administration first approved abortion pills in 2000, their use has increased over time as a share of abortions nationally, according to both the CDC and Guttmacher.

The majority of abortions in the U.S. now involve pills, according to both the CDC and Guttmacher. The CDC says 56% of U.S. abortions in 2021 involved pills, up from 53% in 2020 and 44% in 2019. Its figures for 2021 include the District of Columbia and 44 states that provided this data; its figures for 2020 include D.C. and 44 states (though not all of the same states as in 2021), and its figures for 2019 include D.C. and 45 states.

Guttmacher, which measures this every three years, says 53% of U.S. abortions involved pills in 2020, up from 39% in 2017.

Two pills commonly used together for medication abortions are mifepristone, which, taken first, blocks hormones that support a pregnancy, and misoprostol, which then causes the uterus to empty. According to the FDA, medication abortions are safe  until 10 weeks into pregnancy.

Surgical abortions conducted  during the first trimester  of pregnancy typically use a suction process, while the relatively few surgical abortions that occur  during the second trimester  of a pregnancy typically use a process called dilation and evacuation, according to the UCLA School of Medicine.

In 2020, there were 1,603 facilities in the U.S. that provided abortions,  according to Guttmacher . This included 807 clinics, 530 hospitals and 266 physicians’ offices.

While clinics make up half of the facilities that provide abortions, they are the sites where the vast majority (96%) of abortions are administered, either through procedures or the distribution of pills, according to Guttmacher’s 2020 data. (This includes 54% of abortions that are administered at specialized abortion clinics and 43% at nonspecialized clinics.) Hospitals made up 33% of the facilities that provided abortions in 2020 but accounted for only 3% of abortions that year, while just 1% of abortions were conducted by physicians’ offices.

Looking just at clinics – that is, the total number of specialized abortion clinics and nonspecialized clinics in the U.S. – Guttmacher found the total virtually unchanged between 2017 (808 clinics) and 2020 (807 clinics). However, there were regional differences. In the Midwest, the number of clinics that provide abortions increased by 11% during those years, and in the West by 6%. The number of clinics  decreased  during those years by 9% in the Northeast and 3% in the South.

The total number of abortion providers has declined dramatically since the 1980s. In 1982, according to Guttmacher, there were 2,908 facilities providing abortions in the U.S., including 789 clinics, 1,405 hospitals and 714 physicians’ offices.

The CDC does not track the number of abortion providers.

In the District of Columbia and the 46 states that provided abortion and residency information to the CDC in 2021, 10.9% of all abortions were performed on women known to live outside the state where the abortion occurred – slightly higher than the percentage in 2020 (9.7%). That year, D.C. and 46 states (though not the same ones as in 2021) reported abortion and residency data. (The total number of abortions used in these calculations included figures for women with both known and unknown residential status.)

The share of reported abortions performed on women outside their state of residence was much higher before the 1973 Roe decision that stopped states from banning abortion. In 1972, 41% of all abortions in D.C. and the 20 states that provided this information to the CDC that year were performed on women outside their state of residence. In 1973, the corresponding figure was 21% in the District of Columbia and the 41 states that provided this information, and in 1974 it was 11% in D.C. and the 43 states that provided data.

In the District of Columbia and the 46 states that reported age data to  the CDC in 2021, the majority of women who had abortions (57%) were in their 20s, while about three-in-ten (31%) were in their 30s. Teens ages 13 to 19 accounted for 8% of those who had abortions, while women ages 40 to 44 accounted for about 4%.

The vast majority of women who had abortions in 2021 were unmarried (87%), while married women accounted for 13%, according to  the CDC , which had data on this from 37 states.

In the District of Columbia, New York City (but not the rest of New York) and the 31 states that reported racial and ethnic data on abortion to  the CDC , 42% of all women who had abortions in 2021 were non-Hispanic Black, while 30% were non-Hispanic White, 22% were Hispanic and 6% were of other races.

Looking at abortion rates among those ages 15 to 44, there were 28.6 abortions per 1,000 non-Hispanic Black women in 2021; 12.3 abortions per 1,000 Hispanic women; 6.4 abortions per 1,000 non-Hispanic White women; and 9.2 abortions per 1,000 women of other races, the  CDC reported  from those same 31 states, D.C. and New York City.

For 57% of U.S. women who had induced abortions in 2021, it was the first time they had ever had one,  according to the CDC.  For nearly a quarter (24%), it was their second abortion. For 11% of women who had an abortion that year, it was their third, and for 8% it was their fourth or more. These CDC figures include data from 41 states and New York City, but not the rest of New York.

Nearly four-in-ten women who had abortions in 2021 (39%) had no previous live births at the time they had an abortion,  according to the CDC . Almost a quarter (24%) of women who had abortions in 2021 had one previous live birth, 20% had two previous live births, 10% had three, and 7% had four or more previous live births. These CDC figures include data from 41 states and New York City, but not the rest of New York.

The vast majority of abortions occur during the first trimester of a pregnancy. In 2021, 93% of abortions occurred during the first trimester – that is, at or before 13 weeks of gestation,  according to the CDC . An additional 6% occurred between 14 and 20 weeks of pregnancy, and about 1% were performed at 21 weeks or more of gestation. These CDC figures include data from 40 states and New York City, but not the rest of New York.

About 2% of all abortions in the U.S. involve some type of complication for the woman , according to an article in StatPearls, an online health care resource. “Most complications are considered minor such as pain, bleeding, infection and post-anesthesia complications,” according to the article.

The CDC calculates  case-fatality rates for women from induced abortions – that is, how many women die from abortion-related complications, for every 100,000 legal abortions that occur in the U.S .  The rate was lowest during the most recent period examined by the agency (2013 to 2020), when there were 0.45 deaths to women per 100,000 legal induced abortions. The case-fatality rate reported by the CDC was highest during the first period examined by the agency (1973 to 1977), when it was 2.09 deaths to women per 100,000 legal induced abortions. During the five-year periods in between, the figure ranged from 0.52 (from 1993 to 1997) to 0.78 (from 1978 to 1982).

The CDC calculates death rates by five-year and seven-year periods because of year-to-year fluctuation in the numbers and due to the relatively low number of women who die from legal induced abortions.

In 2020, the last year for which the CDC has information , six women in the U.S. died due to complications from induced abortions. Four women died in this way in 2019, two in 2018, and three in 2017. (These deaths all followed legal abortions.) Since 1990, the annual number of deaths among women due to legal induced abortion has ranged from two to 12.

The annual number of reported deaths from induced abortions (legal and illegal) tended to be higher in the 1980s, when it ranged from nine to 16, and from 1972 to 1979, when it ranged from 13 to 63. One driver of the decline was the drop in deaths from illegal abortions. There were 39 deaths from illegal abortions in 1972, the last full year before Roe v. Wade. The total fell to 19 in 1973 and to single digits or zero every year after that. (The number of deaths from legal abortions has also declined since then, though with some slight variation over time.)

The number of deaths from induced abortions was considerably higher in the 1960s than afterward. For instance, there were 119 deaths from induced abortions in  1963  and 99 in  1965 , according to reports by the then-U.S. Department of Health, Education and Welfare, a precursor to the Department of Health and Human Services. The CDC is a division of Health and Human Services.

Note: This is an update of a post originally published May 27, 2022, and first updated June 24, 2022.

Support for legal abortion is widespread in many countries, especially in Europe

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• Published: 12 April 2024

Effect of peer health education intervention on HIV/AIDS knowledge amongst in-school adolescents in secondary schools in Imo State, Nigeria

• Chinelo Judith Ezelote 1 ,
• Nkechi Joy Osuoji 1 ,
• Adaku Joy Mbachu 1 ,
• Chikadibia Kizito Odinaka 1 ,
• Ogochukwu Mildred Okwuosa 1 ,
• Chinaemelum Juliet Oli 1 &
• Chimburuoma Georgina Ignatius 1  

BMC Public Health volume  24 , Article number:  1029 ( 2024 ) Cite this article

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Metrics details

Peer education is an approach to health promotion in which community members are supported to promote health-enhancing change among their peers. The study assessed the effect of peer health education on HIV/AIDS knowledge amongst in-school adolescents in secondary schools in Imo State.

This was an intervention study carried out among 296 and 287 in-school adolescents aged 15 to 19 years attending Akwakuma Girls Secondary School and Federal Government Girls College Owerri Imo State respectively. The study was in three stages: before intervention, intervention, and after intervention. The impact of peer education was evaluated twelve weeks after intervention. Data were collected using semi-structured questionnaires. The study utilized a quasi-experimental study design. The chi-square test and McNemar’s test were used to test the hypothesis with a significance level of p  ≤ 0.05.

The result from the study revealed that the majority (73%) of the respondents at Akwakuma Girls Secondary School (test group) had poor knowledge of HIV/AIDS mode of transmission and prevention at baseline. The overall good knowledge of respondents in the test group improved from 27 to 81% after the intervention. 36% of the respondents in the control group had good knowledge at baseline, the knowledge of 64% of them with poor knowledge at baseline were compared post-test to those in the test group who also had poor knowledge at baseline. The knowledge of only 27.7% of those in the control group increased post-test while the remaining 72.3% still had poor knowledge. The result of the inter-school comparison using Chi-square revealed that the p -value was statistically significant. Intra-school comparison using McNemar’s test revealed a statistical significance for all questions in the test group, while none was positively significant in the control group.

Conclusions

Peer health education improved the knowledge of the students at Akwakuma Girls Secondary School which was very low at the baseline. The knowledge of the students in the control group with poor knowledge at baseline didn’t increase post-study. Peer health education should be strengthened and expanded as one of the tools for behavior change among adolescents. There should be more focus on adolescents for HIV-targeted prevention.

Peer Review reports

Human Immunodeficiency Virus (HIV) and Acquired Immune Deficiency Syndrome (AIDS) are among the most complex health problems of the twenty-first century [ 1 ]. HIV/AIDS infection remains a historic public health issue globally, especially in low and middle-income countries like Nigeria where access to HIV/AIDS education and the use of Voluntary Counseling and Testing (VCT) is low [ 2 ]. The Human Immunodeficiency Virus (HIV) is a virus that targets the immune system and weakens people’s defense against many infections and some types of cancer that people with healthy immune systems can more easily fight [ 3 ]. HIV, the virus that causes acquired immunodeficiency syndrome (AIDS), is a slow-acting retrovirus. It is transmitted by unprotected sexual intercourse, contaminated blood used for blood transfusions, needles contaminated with HIV, prenatally/ perinatally, and by breastfeeding [ 4 ]. Peer health education has been shown from past studies to be an effective tool in improving knowledge, attitude, and some preventive practices towards HIV/AIDS among in-school adolescents [ 5 ].

Peer education is a health promotion approach in which community members are supported to promote healthy changes among their peers [ 6 ]. It is the teaching or sharing of health knowledge, values, and behaviors while educating others with similar social backgrounds or life experiences [ 7 , 8 ]. A peer is a person who has equal standing with another in age, background, social status, and interests [ 9 ]. It was noted that young people rarely talk to health personnel about sensitive issues because they often receive their information from peers and friends [ 10 ]. The use of peer educators for health improvement has been linked with the importance of peer influence in adolescence [ 11 ]. There is evidence that young people are more likely to seek help from informal sources of support such as friends in comparison to adults [ 12 ]. These findings showed that adolescents prefer to seek help for health-related concerns from their peers rather than adults or professionals. More often than not, they might receive the wrong information from their peers. This situation underscored the need for accurate sexual health information through a channel that will be welcoming and acceptable to them. Peer health education may help to bridge this gap. It will likely help to get young people to talk about their sexual activities and ensure that the right information is made available to them. The target population for this study was in-school adolescents who were presumed vulnerable to sexual health problems, partly due to inadequate sexual health knowledge and negative attitudes.

Adolescence is a transitional stage of physical and psychological development that generally occurs during the period from puberty to adulthood (typically corresponding to the age of majority) [ 13 ]. The World Health Organization definition officially designates an adolescent as someone between the ages of 10 and 19 [ 14 ]. Adolescents view themselves as being unique and as such immune to disease and death, with their thinking that something bad will happen to someone else, not me [ 15 ]. Young persons experience numerous physical and social changes, often making it difficult for them to know how to behave [ 16 ]. During adolescence, issues of emotional (if not physical) separation from parents arise 16 . More than a quarter (38.10%) of Nigeria's population belongs to the age group 15–24 years old [ 17 ]. UNICEF in 2015 noted that in Nigeria, only one in every four young women aged 15–24 years (24 percent) has comprehensive knowledge of HIV prevention [ 18 ]. This rate was found to be below the average for West and Central Africa (33 percent). According to UNICEF (2023), adolescents and young people represent a growing share of people living with HIV worldwide [ 19 ]. In 2022 alone, 480,000 (255,000–760,000) young people between the ages of 10 to 24 were newly infected with HIV, of whom 140,000 (35,000–250,000) were adolescents between the ages of 10 and 19 (UNICEF, 2023) 15 . The immediate school environment still serves as a fertile ground for high-risk sexual behavior [ 20 ]. The majority of the Institutions of Higher Education (IHE) in Nigeria are situated either in rural areas or on the perimeter of urban cities. The host communities in most rural areas are likely to exert an influence on the pattern and dynamics of HIV infection at IHE in Nigeria [ 21 ]. There is growing evidence from several countries where HIV prevalence is decreasing, it is the young people who are reversing the trends [ 22 ]. There is a need to ensure they are exposed more to positive behaviors at this stage of their life.

However, information on knowledge of HIV/AIDS mode of transmission and prevention among in-school adolescents in Imo State, Nigeria is scarce if not completely absent. Therefore, the objective of the study was to assess the effect of peer health education intervention on HIV/AIDS knowledge amongst in-school adolescents in secondary schools in Imo State, Nigeria.

This will guide the design of tailor-made HIV intervention programmes for HIV/AIDS among adolescents in Southeast Nigeria. The results of this study will create awareness of the positive effects of peer health education on knowledge of HIV mode of transmission and prevention among in-school adolescents. It will also be of immense benefit to programme planners who are constantly in search of a more effective and efficient strategy for communicating sexual health information to youths.

The study had test and control groups which comprised Akwakumma Girls Secondary School (AGSS) and Federal Government Girls College Owerri (FGGC) respectively. The two schools were randomly selected through simple random sampling without replacement. All the secondary schools in Owerri metropolis were written on a piece of paper, squeezed, and put in a ballot bag after which two were randomly picked. A quasi-experimental research study design was used, where the schools were randomly assigned to test and control groups by simple balloting. The students in the two schools were matched by their age, gender, class of study, and location of schools. The age range included in the study were all those within the age range of 14 to 19 years of age, and in SS1 to SS3 class. The schools were all female schools, they were both located in Owerri Urban, and the students were all Christians. The mean ages of the students were 17.06 and 16.82 at AGSS and FGGC respectively. The respondents at baseline were 583 in total, 296 and 287 in the test and the control group respectively. They comprised all the students in the senior class (SS1 to SS3) in both schools. The level of knowledge of the respondents on HIV/AIDS was assessed using the Aids Clinical Trials Group-18 (ACTG-18). ACTG is a well-validated instrument and was adopted from the study conducted by Reynolds et al. [ 23 ]. The questionnaire which comprised eighteen questions (18) and answer keys was attached in the Appendix . The questionnaire assessed the respondents’ level of knowledge of HIV; its mode of transmission and prevention. It was administered through a face-to-face method. The peer health educators were trained using the United Nations Population Fund’s (2005) Training of Trainers manual, and the United Nations Children Fund’s (UNICEF) Reproductive Health and HIV/AIDS prevention project’s manual for peer educators, produced for the National Youth Service Corps (NYSC) in Nigeria [ 24 , 25 ]. They were adapted for this study as the training instrument for the peer educators. The knowledge of the students was assessed at baseline using the ACTG-18 questionnaire. The respondents who didn’t answer one-third (33%) of the questions correctly were classified as poor knowledge. Those with poor knowledge in the test group were selected for the intervention, while those who scored above 75% were recruited as peer health educators. Those in the control group did not receive the peer health education. They were only educated on personal hygiene and environmental sanitation three times but there was no mention of HIV during the whole interactions.

Inclusion criteria

All secondary school students at FGGC Owerri and AGSS Owerri within the age range of 15 to 19.

Those who signed the informed consent.

Exclusion criteria

Those students at FGGC Owerri and AGSS Owerri within the age range of 15 to 19 who did not consent to this study.

Those students at FGGC Owerri and AGSS Owerri within the age range of 15 to 19 whose parents did not consent for them to participate in this study.

Those students in SS1 to SS3 at FGGC Owerri and AGSS Owerri who were more than 19 years old.

Data collection

Data was obtained using an adapted questionnaire. Ethical clearance was obtained before proceeding further with the research. The first meeting was at the office State Ministry of Education but we met the representative. The research was explained to her, including all the processes involved. She requested for the research proposal and asked us to return after a fortnight. During the second visit after two weeks, the researchers were granted oral permission to proceed with the research. The first visit was made to the schools which equate to an introductory visit. The researcher met the principal of the various schools, and staff, and explained the study in detail to them. They both asked for the ethical approval and research proposal which were given to them. The date for the second visit was fixed by the principals of the two schools. The principal of FGGC asked the researchers to return the next semester because they started their exam the following week. The researcher met the students at AGSS after a week of visiting the principal. The research objectives were explained to them, and all that was required of them. Their confidentiality and anonymity were well assured. The students started exams the following week, hence the research was paused. The researchers met the students at the two schools in the second week of school resumption and explained the research objectives to them. It was done twice at AGSS because it was assured the students might have forgotten about it during the holidays. Their informed consent was obtained, and the researchers proceeded to share the baseline questionnaires to them during the fourth time of visiting the school. The questionnaires were shared, filled, and collected on the spot. Four research assistants were recruited for this study. They were undergraduate female students of the Federal University of Technology Owerri. They were between the age ranges of 20 to 22 and in their final year in school. They helped to administer the questionnaire to the respondents. The principal researcher trained them twice a week for two weeks and explained the questionnaire to them. They helped the students who had any problem with comprehending any question. The ones they couldn’t answer were referred to the principal researcher. The study was carried out in three stages, namely, pre-intervention, intervention, and post-intervention stages. At the intervention stage, for the students in the test group, the recruitment and training of 30 students as peer educators (ten from each level) were carried out for two weeks. Those selected as peer educators were those with scored above 75% after the baseline assessment. Topics covered included rudiments information on HIV/AIDS, its mode of transmission and prevention, and other sexually transmitted infections. The training was in the form of lectures, motivational talks, and demonstrations using audiovisuals, posters, role plays, and practical demonstrations. After the training, the trained peer educators were provided with educational materials (such as hand bills, leaflets, posters, etc.) Meetings were held weekly by the peer educators in each of the classes where they discussed their progress and challenges with the researchers. The researchers provided them with supportive supervision. The students in the test group who passed just 1/3rd (33.3%) of the eighteen questions in the questionnaire after the baseline assessment were regarded as having poor knowledge and were those included in the intervention stage. Two hundred and fifty-one (251) students out of two hundred and ninety-six students (296) scored 33% and below, and they were the only ones included in the test group. The students that scored between 33.4% and 74% were 13, while those that scored 75% and above were 32 students. The peer health educators educated them on the correct information on HIV/AIDS about its mode of transmission and prevention. The researchers were around throughout the peer health education. They supervised the whole process to ensure that the right information was passed to the students from the peer health educators. At the post-intervention stage, the same questionnaire used in the pre-intervention stage was re-administered to only the students in the test group 12 weeks after intervention which was 2 weeks before their exam. The Statistical Package for the Social Sciences (SPSS) was used in the analysis of the data obtained from the study. Results were expressed in percentages, frequencies, tables, and charts. The chi-square test tool ( p  ≤ 0.05) and McNemar’s test were used to test the hypothesis to assess any significant change in their level of knowledge. A p -value < 0.05 was considered as significant.

The students in the control group were 287 at baseline. The same questionnaire was given to them and their HIV knowledge at baseline was assessed. One hundred and eighty-four students (184) correctly answered just 1/3rd (33.3%) and below of the eighteen questions in the questionnaire, and were regarded as having poor knowledge. Those that scored above 33.4% were 103 students, amongst whom 47 scored above 74% whereas 56 scored between 33.4% and 74%. Those 184 students were the ones included in the post-test. They were educated on personal hygiene and environmental sanitation three times (once a month), and there was no mention of HIV during the whole interaction. After three months, the questionnaire was re-administered to them; their response was collated, computed, and analyzed using regression analysis to assess any difference in their knowledge. The second data was collected exactly one week before their term exams.

The analysis as depicted in Table  1 contained the knowledge of the respondents at Akwakuma Girls Secondary School Owerri (the test group) pre and post-intervention. It showed that their knowledge of HIV (concept, its modes of transmission, and prevention) at baseline was abysmally low in all the 18 questions in the questionnaire which increased after peer health education intervention. Only 38(12.8%) of the respondents correctly stated that coughing and sneezing did not spread HIV during the pre-test, this increased to 183(85.1%) during the post-test. A person can get HIV by sharing a glass of water with someone who has HIV was correctly indicated as not a way of contracting HIV by 52(17.6%) of the respondents during pre-intervention while the number increased to 174(80.9%) in post-intervention. Pulling out the penis before a male ejaculate keeps a woman from getting HIV was correctly answered as false by 101(34.1%) at pre-test and increased to 189(87.9%) at post-intervention. Anal sex as one of the modes of acquiring HIV/AIDs was correctly answered pre-intervention by only 56(18.9%) of the respondents which increased to 149(69.3%) after the intervention. Few numbers of students (24%) knew that showering, or washing one’s genitals/private parts after sex cannot keep someone from getting HIV, this increased to 123(57.2%) after the intervention. Only 21.6% of the respondents knew that people who have been infected with HIV do not quickly show serious signs of being infected, their knowledge of that increased to 57.7% after the intervention. Only 3.7% of the respondents knew there is currently no vaccine that can stop adults from getting HIV, the number increased to 63.3% after the intervention. At pre-intervention, only 29(9.8%) of the adolescents disagreed that people are likely to get HIV by deep kissing, or putting their tongue in their partner has mouth if their partner has HIV while their knowledge increased to 172(80.0%) after intervention. To determine if a person can get HIV by sitting in a hot tub or swimming pool with a person who has HIV, only 25(8.4%) of the respondents answered correctly while after intervention the number of knowledgeable students increased to 176(81.9%). Very few (4.4%) of the students knew that a person could get HIV from oral sex while the number increased to 83.7% after intervention. Finally, it was found that 32(10.8%) of the respondents knew that using Vaseline or baby oil with a condom cannot lower the chance of getting HIV while after intervention 140(65.1%) of the students became aware that using Vaseline or baby oil with a condom cannot lower the chance of getting HIV.

Table 2 below indicates that the P -value is less than 0.05. This showed that there is a significant difference in the knowledge of the respondents before and after the test. The knowledge of the respondents increased after receiving peer-health education. This indicates that peer health education had a positive impact on the HIV/AIDS knowledge of respondents in the test group.

Knowledge of the students on HIV/AIDS at FGGC Owerri (Control Group)

The study evaluated the knowledge of students in the control group (FGGC Owerri) before and after an intervention. The findings are presented in Table  3 below. At the start of the study, the students had a fair understanding of HIV transmission and prevention. However, 184 students who scored 33.3% or less were assessed in a post-test, and the results showed that their knowledge had not improved.

Intra school comparison of the knowledge of the students on HIV/AIDS at FGGC Owerri using Regression analysis

At the post-test, respondents with poor knowledge at the beginning (≤ 33.3%) were re-assessed without any intervention to determine if their knowledge increased. The results, as presented in Table  4 , showed that only 27.7% of the respondents had increased knowledge, while the remaining 73.3% still had poor knowledge at the post-test. The P -value was 0.05, indicating a significant difference in the knowledge levels between the pre and post-test. The good knowledge level at baseline was higher compared to the post-test.

Overall knowledge of the respondents at pre and post test

Table 5 below shows the overall knowledge level of the respondents before and after the test. At the beginning of the study, 85% of the respondents at AGSS had poor knowledge (less than 33.3%). However, by the end of the study, this percentage decreased to 30.6. In the control group, 64.1% of the respondents had poor knowledge at baseline. Among those who had poor knowledge (< 33.3%) in the control group at the beginning of the study, 72.3% still had poor knowledge of HIV at the end of the study, while 27.7% showed an increase in knowledge (from < 33.3% to ≥ 33.4 and above).

Comparison of the respondents in the control group’s knowledge pre and post-intervention

An intra-school comparison was conducted to determine if there was a significant difference in students' knowledge of HIV/AIDS between two secondary schools before and after an intervention. A chi-square test of association was performed, and the results are presented in Table  6 . The p -value was less than 0.05 (95% confidence level) for all variables except X13 and X16. This indicates that there was a significant difference in knowledge levels between the test group (who received the intervention) and the control group at both the pre-test and post-test stages.

Chi square for the overall knowledge test of association

The overall knowledge of the respondents in the two schools was compared using the Chi-square test and presented in Table  7 below. They were grouped into three categories; those that scored ≥ 33.3%, those that scored between 33.4% – 74.9%, and those that scored ≥ 75%. The test results showed the values for all the groups were less than 0.05. There is a statistical difference in the knowledge of the respondents pre and post-test for all the categories.

Summary of the tables

Table 8 below depicts the summary of all the tables. The Chi-square analysis showed that the knowledge of the respondents was statistically significant pre and post-test.

Knowledge of HIV/AIDS among in-school adolescents at baseline

The result of this study as shown in Table  1 revealed that the knowledge of HIV/AIDS (concept, mode of transmission, and prevention) among in-school adolescents in Akwakuma Girls Secondary School Imo State was abysmally low at baseline, with only 27% of the students knowing about HIV/AIDS at baseline. They had low knowledge of all the eighteen questions in the questionnaire, while students in the control group had better knowledge of HIV compared to those in the test group at baseline. This is consistent with the study on HIV comprehensive knowledge and prevalence among 1818 young adolescents in Akwa Ibom State Nigeria using the AIDS indicator survey, 2017 [ 26 ]. The result of the study showed low levels of comprehensive HIV knowledge (9.4%) among young adolescents, and the majority (93%) of young adolescents perceived themselves not to be at risk of HIV. Another study on peer education as an effective behavior change strategy among in-school adolescents attending mixed secondary school in Osun State using a pretested semi-structured questionnaire, revealed that although the level of awareness about AIDS at the pre-intervention stage was very high with more than 9 out of 10 respondents in both the study and control groups being aware of the disease called AIDS, the comprehensive knowledge about HIV/AIDS was rather poor [ 27 ].

Knowledge of HIV/AIDS among in-school adolescents after Intervention

The findings showed that the knowledge of HIV/AIDS among the respondents in the test group rapidly increased after the peer-based health education intervention was conducted. From the result presented in Table  1 , the knowledge of the respondents in the test group increased for all the questions after the study intervention. Their knowledge increased from 27 to 81%. The result of the Chi-square test analysis comparing the students’ knowledge of HIV at baseline and after study intervention showed that the test was statistically significant for all the eighteen questions ( P  < 0.05) except questions 13 and 16. This means that the knowledge of the students in the test group increased for almost all the questions after the study intervention. The knowledge of the majority (73.3%) of the respondents in the control group which scored 33.3% at baseline didn’t increase for any of the questions at the post-test. This result was in line with an Intervention study conducted by Adeomi et al. which was conducted in three stages; before intervention, intervention, and after intervention 23 . The impact of peer education was evaluated 12 weeks after intervention. After the peer education intervention, those with good knowledge and positive attitudes towards HIV/AIDS increased significantly from 50.0% to 86.7% and from 49.0% to 85.6% respectively ( P  < 0.05). This finding is also consistent with the findings of Chizoba A.F et al. on the effects of peer and provider-based education interventions on HIV/AIDS knowledge and behaviour risk among in-school adolescents in Nigeria [ 28 ]. The researchers noted very significant differences between intervention groups and control groups after intervention. The study conducted in the Dominican Republic reported that respondents who received sex education (intervention group) were 1.72 times more likely to have high HIV/AIDS knowledge than respondents who reported not receiving sex education (control group) [ 29 ]. A program evaluation study of developing countries similarly demonstrated that participants who received HIV prevention education intervention reported superior knowledge when compared with the control group. The study conducted to assess the effects of peer education on AIDS knowledge and sexual behavior among youths on national service and secondary school students in Nigeria further showed that both youths and students who received HIV (prevention intervention) HPI reported superior knowledge of HIV/AIDS than their counterparts who did not. The result showed that the peer health education intervention had positive effects on both youths and students who received the intervention [ 30 ]. A study conducted on the Impact of a Peer Public Health Education Programme on Adolescent Students’ Knowledge of HIV/AIDS and Attitude Towards People Living with HIV/AIDS in Abia State, South East Nigeria revealed that the adolescent students who were given peer education training attained higher knowledge of HIV/AIDS and also showed a greater positive attitude towards people living with HIV/AIDS. The researchers noted that the result of the research proved that peer education training is evidenced in attaining higher knowledge of HIV/AIDS and in showing a greater positive attitude towards people living with HIV/AIDS [ 31 ]. Also, the study conducted in Khartoum, Suda on the effect of AIDS peer health education on knowledge, attitudes, and practices of secondary school students showed that the intervention program improved participants’ knowledge from 75.5% to 83.2%. The study concluded that school peer education is an effective approach to inform students of unsafe sexual behavior about HIV/AIDS [ 32 ].

Peer-health education is encouraged to be used as a means of improving HIV/AIDS knowledge/awareness among adolescents and young adults to achieve HIV pandemic control especially as adolescents/young adults are contributing to more than half of new infections.

This study showed that the knowledge of in-school adolescents in the Akwakumma Girls Secondary School Owerri in Imo State was low at the baseline. Baseline HIV knowledge among the adolescents was unimpressive, and this calls for urgent concern. Peer-based health education resulted in better knowledge of the students in the test group on information on HIV/AIDS; its mode of transmission and prevention. The knowledge of the students in the control group did not improve during the post-test. Adolescents are the leaders of the next generation hence they need to be adequately equipped with the right information on HIV/AIDS for targeted prevention.

Recommendation

It is recommended that this same study should be replicated in many areas, and peer-based health education should be inculcated in the curricula of secondary schools. This will ensure they are getting the right information. They are at the stage where they have access to much information, hence the need to ensure they are getting the right ones.

Availability of data and materials

All data generated or analyzed during this study are included in this published article.

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Chinelo Judith Ezelote, Nkechi Joy Osuoji, Adaku Joy Mbachu, Chikadibia Kizito Odinaka, Ogochukwu Mildred Okwuosa, Chinaemelum Juliet Oli & Chimburuoma Georgina Ignatius

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Ezelote Judith Chinelo conceptualized the idea, and was part of the study design initiation, study implementation, and drafting of the manuscript. MJA was part of the study design initiation. IGC was part of the study design initiation and implementation. OMO assisted with the implementation. OKC assisted with statistical expertise in conducting the primary statistical analysis. OJC assisted with statistical expertise in conducting the primary statistical analysis. OJN assisted to draft the manuscript.

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Ezelote, C.J., Osuoji, N.J., Mbachu, A.J. et al. Effect of peer health education intervention on HIV/AIDS knowledge amongst in-school adolescents in secondary schools in Imo State, Nigeria. BMC Public Health 24 , 1029 (2024). https://doi.org/10.1186/s12889-024-18536-4

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Exploring variations in IPC competencies: a cross-sectional study among healthcare professionals in Northwest China

• Qinglan Zhao 1 ,
• Xiaoqing Cui 2 ,
• Ting Liu 2 ,
• Hanxue Li 2 ,
• Miaoyue Shi 3 &
• Li Wang 2  

BMC Infectious Diseases volume  24 , Article number:  420 ( 2024 ) Cite this article

Metrics details

This cross-sectional study investigates infection prevention and control (IPC) competencies among healthcare professionals in northwest China, examining the influence of demographic factors, job titles, education, work experience, and hospital levels.

Data from 874 respondents across 47 hospitals were collected through surveys assessing 16 major IPC domains. Statistical analyses, including Mann-Whitney tests, were employed to compare competencies across variables.

Significant differences were identified based on gender, job titles, education, work experience, and hospital levels. Females demonstrated higher IPC competencies, while senior positions exhibited superior performance. Higher educational attainment and prolonged work experience positively correlated with enhanced competencies. Variances across hospital levels underscored context-specific competencies.

Demographic factors and professional variables significantly shape IPC competencies. Tailored training, considering gender differences and job roles, is crucial. Higher education and prolonged work experience positively impact proficiency. Context-specific interventions are essential for diverse hospital settings, informing strategies to enhance IPC skills and mitigate healthcare-associated infections effectively.

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Healthcare-associated infections (HAIs) represent a persistent and concerning threat to patient safety, emphasizing the critical need for robust infection prevention and control (IPC) measures within healthcare settings [ 1 , 2 ]. The prevalence of HAIs not only jeopardizes the well-being of patients but also poses challenges to the overall effectiveness and sustainability of healthcare systems [ 3 ]. In response to these challenges, healthcare institutions worldwide have established dedicated IPC teams comprising professionals with specialized knowledge and skills to design and implement effective strategies [ 4 , 5 ].Effective IPC practices directly impact patient outcomes and overall healthcare quality. By reducing the incidence and transmission of HAIs, IPC measures play a pivotal role in safeguarding patient safety and improving healthcare outcomes.Moreover, effective IPC practices alleviate the burden of HAIs on healthcare systems. HAIs lead to increased morbidity and mortality rates, prolonged hospital stays, elevated healthcare costs, and resource strain. By preventing infections, healthcare facilities can enhance efficiency, optimize resource utilization, and elevate the quality of care provided to patients.The individuals at the forefront of these efforts, known as IPC specialists, play a pivotal role in preventing and controlling the spread of infections within medical facilities [ 6 ]. As the complexity of healthcare delivery continues to evolve, the role of IPC professionals becomes increasingly intricate. Their core competencies form the foundation for safeguarding patients, healthcare workers, and the broader community from the detrimental impact of healthcare-associated infections [ 7 ].

In the context of the COVID-19 pandemic, the imperative to enhance the core competencies of IPC professionals becomes even more pronounced [ 8 , 9 , 10 ]. These professionals must not only confront traditional infectious challenges but also adeptly respond to emerging threats such as the novel coronavirus. Therefore, continuous improvement in their knowledge base and skills, coupled with the ongoing refinement of infection control strategies, holds strategic importance in protecting patients, healthcare workers, preserving public health, and effectively responding to the spread of infectious diseases such as COVID-19 [ 11 ]. In this context, enhancing the core competencies of IPC professionals within healthcare institutions is an urgent and indispensable task [ 8 ].

Understanding the specific core competencies required by IPC specialists is essential for tailoring education and training programs that empower these professionals to meet the dynamic demands of their roles. The ability to effectively respond to emerging infectious threats, conduct thorough epidemiological investigations, implement stringent environmental monitoring, and educate healthcare personnel on best practices are just a few examples of the multifaceted skills demanded by this profession [ 12 , 13 ].

This research endeavors to explore and delineate the essential core competencies of IPC specialists within healthcare institutions in Northwest China. By doing so, it aims to contribute valuable insights into the intricacies of their roles and responsibilities, paving the way for targeted training programs that address the specific needs of these professionals. The significance of this study lies in its potential to enhance the competency of IPC specialists, ultimately fortifying healthcare systems against the constant threat of healthcare-associated infections.

Materials and methods

This cross-sectional study collected a total of 1021 questionnaire responses from 47 hospitals in the Northwestern region of China, spanning the period from November 2022 to November 2023. Of these, 874 responses with complete information were deemed valid. The questionnaire encompassed 16 major sections and 64 sub-items(among the 16 major sections, the maximum number of sub-items is 7, while the minimum number is 2.), covering critical domains such as Infection Prevention and Control (IPC) Project Management and Leadership, Architectural Environment of Medical Institutions, Basic Microbiology, Prevention of Antibiotic Resistance, Monitoring Healthcare-Associated Infections, Standard Precautions, Transmission-Based Precautions, Cleaning and Reprocessing of Medical Devices, Prevention of Catheter-Related Bloodstream Infections, Prevention of Catheter-Associated Urinary Tract Infections, Prevention of Surgical Site Infections, Prevention of Healthcare-Associated Pneumonia, Prevention and Management of Healthcare-Associated Infection Outbreaks, IPC Education and Training, Quality and Patient Safety, and Occupational Health.We referenced the “WHO Core Competencies of Infection Prevention and Control Practitioners” and tailored our core competency questionnaire to align with the specific circumstances in China.

Each item included a self-assessment of the individual’s current proficiency in the respective core competency. Scores ranged from 1 (completely unacquainted) to 4 (fully proficient), with partial and basic proficiency represented by scores of 2 and 3, respectively. The total score for each participant across the 16 major sections was then calculated. All participants provided informed consent, and the study obtained approval from the Medical Ethics Committee.

For general information, frequencies, and percentages were utilized. Collective performance in each core competency was described using mean and standard deviation (SD). Additionally, to account for uncertainty, 95% confidence intervals (95% CI) were calculated for each core competency. Comparative analyses were presented using mean and SD, and inter-group comparisons were conducted using the Mann-Whitney test (GraphPad Prism version 9). A significance level of P  < 0.05 was considered indicative of statistical differences.

The participants in the survey had an average age of 38.29 ± 9.217 years, with an age range of 21 to 66 years. In terms of gender distribution, the results revealed that 10.65% were male, while 89.35% were female. Regarding professional positions, 28.84% were heads of Infection Control Departments, and 71.16% were staff members. Concerning professional titles, 44.39% held junior positions, 27.94% held intermediate positions, 16.48% held associate senior positions, and 11.20% held senior positions. Work experience varied, with 17.28% having ≤ 3 years, 10.42% having 3–6 years, 11.20% having 7–10 years, and 61.12% having ≥ 10 years. The participants had diverse professional backgrounds: 18.54% in clinical medicine, 61.01% in nursing, 12.13% in public health, 3.43% in preventive medicine, 1.49% in pharmacy, 3.09% in clinical laboratory science, and 0.34% in other fields. Educational backgrounds included 36.84% with diploma and below, 60.03% with a bachelor’s degree, and 3.09% with a master’s degree or higher. Regarding the duration of engagement in infection prevention and control work, 58.05% had ≤ 3 years, 19.33% had 3–6 years, 11.68% had 7–10 years, and 11.00% had ≥ 10 years. About participation in professional training during infection control work, 32.02% participated, while 67.98% did not. Lastly, with respect to hospital levels, 5.27% were from level 1 hospitals, 65.82% from level 2 hospitals, and 28.91% from level 3 hospitals (as shown in Table  1 ).

According to Table  2 , the overall performance of core competencies among healthcare infection prevention and control professionals is as follows: IPC Project Management and Leadership received an average score of 15.92 ± 5.259, Architectural Environment of Medical Institutions scored 11.66 ± 3.647, Basic Microbiology scored 4.333 ± 1.435, Prevention of Antibiotic Resistance scored 8.671 ± 2.809, Monitoring Healthcare-Associated Infections scored 11.29 ± 3.60, Standard Precautions scored 9.992 ± 3.285, Transmission-Based Precautions scored 9.695 ± 3.110, Cleaning and Reprocessing of Medical Devices scored 7.561 ± 2.406, Prevention of Catheter-Related Bloodstream Infections scored 11.36 ± 4.172, Prevention of Catheter-Associated Urinary Tract Infections scored 9.663 ± 3.181, Prevention of Surgical Site Infections scored 11.32 ± 4.025, Prevention of Healthcare-Associated Pneumonia scored 11.24 ± 3.864, Prevention and Management of Healthcare-Associated Infection Outbreaks scored 4.790 ± 1.586, IPC Education and Training scored 6.351 ± 2.352, Quality and Patient Safety scored 6.454 ± 2.351, and Occupational Health scored 7.373 ± 2.332.

Gender differences in core competencies among healthcare professionals in infection prevention and control (IPC) were examined (Table  3 ). While no significant disparities were noted in several domains, notable variations surfaced. IPC Project Management and Leadership revealed a significant difference ( p  = 0.0126), favoring females (16.09 ± 5.249) over males (14.43 ± 5.069). Standard Precautions ( p  = 0.0374) and Transmission-Based Precautions ( p  = 0.0213) also favored females. Cleaning and Reprocessing of Medical Devices exhibited a significant gender gap ( p  < 0.0001). Females demonstrated higher scores in Prevention of Catheter-Related Bloodstream Infections ( p  = 0.0047), Prevention of Catheter-Associated Urinary Tract Infections ( p  < 0.0001), Prevention of Surgical Site Infections ( p  = 0.0003), and Prevention of Healthcare-Associated Pneumonia ( p  = 0.0013). IPC Education and Training ( p  = 0.0256), Quality and Patient Safety ( p  = 0.0485), and Occupational Health ( p  = 0.0359) also favored females. These findings underline gender-specific variations in IPC competencies, suggesting tailored training approaches for enhanced professional development.

Differences in core competencies across various job titles among healthcare professionals in infection prevention and control (IPC) were investigated (Table  4 ). Striking disparities emerged, highlighting the impact of job titles on competencies. All domains exhibited significant differences between Junior and Senior Associate positions ( p  < 0.0001). Senior Associates consistently outperformed their Junior counterparts. IPC Project Management and Leadership demonstrated a notable distinction. Similarly, the Architectural Environment of Healthcare Institutions, Basic Microbiology, Prevention of Antibiotic Resistance, Monitoring Healthcare-Associated Infections, Standard Precautions, Transmission-Based Precautions, Cleaning and Reprocessing of Medical Devices and Equipment, Prevention of Catheter-Related Bloodstream Infections, Prevention of Catheter-Associated Urinary Tract Infections, Prevention of Surgical Site Infections, Prevention of Healthcare-Associated Pneumonia, Prevention and Management of Healthcare-Associated Infection Outbreaks, IPC Education and Training, Quality and Patient Safety, and Occupational Health all displayed significant differences favoring Senior Associates. These findings underscore the influence of job titles on the acquisition and application of IPC core competencies, emphasizing the need for targeted training and professional development programs tailored to specific job roles.

Educational levels’ impact on the proficiency of infection prevention and control (IPC) core competencies among healthcare professionals was assessed, revealing significant differences (Table  5 ). Across all domains, individuals with a Bachelor’s degree and above consistently exhibited higher mean scores compared to those with a diploma and below ( p  < 0.05). These findings emphasize the positive association between higher educational attainment and enhanced proficiency in IPC core competencies, underscoring the importance of educational qualifications in shaping competency levels among healthcare professionals.

The impact of years of work experience on the proficiency of infection prevention and control (IPC) core competencies among healthcare professionals was explored, revealing substantial differences (Table  6 ). For each core competency, individuals with more than 6 years of work experience consistently demonstrated higher mean scores compared to those with 6 years and below ( p  < 0.0001). These findings underscore the positive association between longer professional experience and heightened proficiency in IPC core competencies, emphasizing the importance of accumulated work experience in shaping competency levels among healthcare professionals.

According to the results in Table  7 , we found that all core competencies were higher among individuals with over 3 years of experience in infection prevention and control work compared to those with 3 years or less of experience. This trend was observed across various aspects ofIPC, including IPC project management and leadership, healthcare facility environment, basic microbiology, prevention of antimicrobial resistance, surveillance of healthcare-associated infections, standard precautions, transmission-based precautions, cleaning and reprocessing of medical devices, prevention of catheter-related bloodstream infections, prevention of catheter-associated urinary tract infections, prevention of surgical site infections, prevention of healthcare-associated pneumonia, prevention and management of healthcare-associated infection outbreaks, IPC education and training, quality and patient safety, and occupational health. These findings indicate that experienced professionals in infection prevention and control demonstrate higher scores across all core competencies, highlighting their proficiency in various aspects of IPC work.

The influence of participation in further education on the proficiency of infection prevention and control (IPC) core competencies was examined, highlighting significant differences across competencies (Table  8 ). Individuals who engaged in further education exhibited consistently higher mean scores compared to those who did not participate ( p  < 0.0001) across all core competencies. These findings underscore the positive association between active participation in further education and enhanced proficiency in IPC core competencies, emphasizing the importance of ongoing educational initiatives in maintaining and improving professional competency levels among healthcare practitioners.

The investigation into variations in infection prevention and control (IPC) core competencies based on hospital level (Table  9 ) revealed significant differences across diverse competencies. In Level 1 and 2 Hospitals compared to Level 3 Hospitals, there were notable distinctions ( p  < 0.05) in IPC Project Management and Leadership, Healthcare Facility Environment, Basic Microbiology, Prevention of Antimicrobial Resistance, Surveillance of Healthcare-Associated Infections, Standard Precautions, Transmission-Based Precautions, Prevention of Catheter-Associated Bloodstream Infections, Prevention of Catheter-Associated Urinary Tract Infections, Prevention of Surgical Site Infections, Prevention of Healthcare-Associated Pneumonia, Prevention and Management of Healthcare-Associated Infection Outbreaks, IPC Education and Training, Quality and Patient Safety, and Occupational Health. This underscores the impact of hospital level on the proficiency of healthcare practitioners in various IPC core competencies. These findings can inform targeted interventions and educational programs tailored to specific hospital settings, contributing to a more effective and contextually relevant enhancement of IPC skills and knowledge.

Our study, conducted in China’s northwest healthcare institutions, sheds light on the demographic dynamics influencing infection prevention and control (IPC) competencies among healthcare professionals in this region. The predominance of female participants (89.35%) and the representation of heads of Infection Control Departments (28.84%) underscore the need for gender-sensitive leadership programs. With a majority having over 10 years of experience (61.12%), the study reflects a seasoned workforce. However, the prevalence of nursing backgrounds (61.01%) signals a need for tailored training initiatives to accommodate diverse educational foundations within IPC. Varied engagement durations in IPC work and a significant portion (67.98%) not participating in professional training underscore the need for accessible and effective ongoing educational initiatives. Considering diverse experience levels and educational backgrounds in tailored training programs can comprehensively improve competencies. The distribution across hospital levels (65.82% from level 2 hospitals) emphasizes the regional perspective. Competency variations across hospital levels emphasize the need for context-specific training programs to address distinct challenges faced by healthcare professionals in different hospital settings within China’s northwest region.

The examination of gender differences in infection prevention and control (IPC) competencies among healthcare professionals in our study uncovers intriguing patterns. While several domains showed no significant disparities, notable variations emerged, emphasizing gender-specific nuances in IPC proficiency. Females exhibited superior scores in IPC Project Management and Leadership, Standard Precautions, Transmission-Based Precautions, Cleaning and Reprocessing of Medical Devices, and several infection prevention domains, indicating their enhanced competence in these critical areas. The observed gender-specific advantages highlight the need for tailored training programs acknowledging these differences. The significant gender gap in Cleaning and Reprocessing of Medical Devices suggests that female healthcare professionals excel in the intricacies of medical device sterilization and maintenance. Moreover, their higher scores in preventive measures against catheter-related infections and surgical site infections underscore their proficiency in ensuring patient safety during invasive procedures. The preference for females in IPC Education and Training, Quality and Patient Safety, and Occupational Health signifies their potential leadership in these realms. Research has shown that female physicians have outnumbered male participants in leading IPC programs [ 14 ]. Additionally, a study by P. Hlongwa indicates that females may have enhanced competence in some areas [ 15 ]. Furthermore, a study by Akan et al. found that the risk perception of males was significantly lower than that of females, indicating that females may have a better understanding of the risks associated with infection, which could contribute to their enhanced competence in IPC [ 16 ]. To optimize IPC competencies, healthcare institutions should recognize and leverage these gender-specific strengths, tailoring training initiatives to empower both male and female professionals effectively. This nuanced understanding contributes to fostering a diverse and skilled IPC workforce, ultimately enhancing healthcare outcomes.

Our investigation into the impact of job titles on infection prevention and control (IPC) competencies among healthcare professionals unveils substantial disparities, particularly between Junior and Senior Associate positions. The consistent outperformance of Senior Associates across all domains, including IPC Project Management and Leadership, Architectural Environment, Basic Microbiology, and various preventive measures, accentuates the pivotal influence of job roles on competency acquisition. This may be attributed to the self-perception and motivational abilities of individuals in higher-level positions, fostering IPC competence [ 17 , 18 ]. This underscores the necessity for targeted training and development initiatives tailored to specific professional levels, ensuring a more nuanced and effective enhancement of IPC skills.

Moving to educational levels, our findings reveal a clear association between higher educational attainment and increased proficiency in IPC core competencies. Individuals with a Bachelor’s degree and above consistently exhibited higher mean scores across all domains compared to those with a diploma and below. This underscores the importance of educational qualifications in shaping the competency landscape among healthcare professionals in infection prevention and control. It has been found that clinical nurse educators with higher levels of education and greater lengths of work experience often report higher self-assessed levels of competence, highlighting the impact of educational backgrounds on competence levels [ 19 ].Institutions and policymakers should recognize the pivotal role of educational backgrounds, encouraging and facilitating continuous learning to ensure a skilled and competent IPC workforce capable of addressing evolving healthcare challenges.

The exploration of the impact of work experience on infection prevention and control (IPC) core competencies reveals a compelling association between longer professional tenure and heightened proficiency. Individuals with over 6 years of experience consistently demonstrated superior mean scores across all competencies, emphasizing the pivotal role of accumulated work experience in shaping the competency levels among healthcare professionals in IPC. Reeves et al. suggests that with more work experience, professionals are likely to have engaged in more IPE, thereby strengthening their IPC core competency [ 20 ]. This underscores the importance of recognizing and leveraging the expertise gained through years of practical engagement, advocating for continued professional development and mentorship.

Turning to the influence of further education, our investigation illuminates a positive correlation between active participation in ongoing educational initiatives and enhanced proficiency in IPC core competencies. Individuals engaged in further education consistently exhibited higher mean scores across all competencies, underscoring the crucial role of continuous learning in maintaining and elevating professional competency levels among healthcare practitioners in infection prevention and control. The European Centre for Disease Prevention and Control (ECDC)-commissioned Training in Infection Control in Europe project emphasizes the need for education in infection control and sets the stage for harmonization of IPC activities by issuing a list of core competencies for IPC professionals, further supporting the significance of educational qualifications in IPC [ 21 ]. Moreover, a study on interprofessional collaboration demonstrates that exposure to interprofessional education activities holds promise for enhancing IPC in clinical settings, emphasizing the role of education in promoting collaborative competence among healthcare professionals [ 22 ]. This emphasizes the imperative for healthcare institutions to facilitate and encourage access to educational opportunities, ensuring practitioners stay abreast of evolving best practices and advancements in IPC.

Analyzing variations based on hospital levels unravels noteworthy distinctions in IPC competencies. Level 1 and 2 Hospitals, compared to Level 3 Hospitals, exhibit significant differences across various competencies. This could be attributed to higher-level hospitals having a more comprehensive approach to the daily training of healthcare personnel and the setup of departments. It implies that higher-level hospitals possess stronger overall capabilities in responding to infectious diseases, enabling them to better handle various types of illnesses and medical situations [ 23 ]. This underscores the impact of the hospital level on the proficiency of healthcare practitioners, emphasizing the need for tailored interventions and educational programs catering to specific hospital settings. Such targeted approaches can contribute significantly to enhancing IPC skills and knowledge in a manner that is both effective and contextually relevant to the diverse healthcare landscapes within different hospital levels.

Despite the valuable insights gained, this study has limitations. Its cross-sectional design hinders establishing causation and observing changes over time. The focus on healthcare professionals in northwest China may limit generalizability to other regions. Self-assessment introduces social desirability bias, and the study lacks exploration of specific training programs. Additionally, while identifying differences in IPC competencies, it does not delve into the underlying reasons. Recognizing these limitations is crucial for interpreting findings and guiding future research efforts.

In conclusion, this study provides a comprehensive analysis of infection prevention and control competencies among healthcare professionals in northwest China. Demographic factors, job titles, education, work experience, and hospital levels significantly influence these competencies. Gender-specific variations and the impact of further education underscore the need for tailored training. Disparities between junior and senior positions highlight the importance of targeted professional development. Higher education positively correlates with enhanced proficiency. Longer work experience contributes to heightened competencies. Variances based on hospital levels emphasize the context-specific nature of IPC skills. Acknowledging these factors is vital for designing effective interventions and improving healthcare-associated infection prevention strategies.

Data availability

Data is provided within the manuscript.

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Acknowledgements

This study was funded by Shaanxi Province key research and development plan project (No.2023-YBSF-087).

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Infection Management Office, Xinjiang Uygur Autonomous Region People’s Hospital, Urumqi, Xinjiang, China

Qinglan Zhao

Nosocomial Infection Management Office, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, China

Xiaoqing Cui, Ting Liu, Hanxue Li & Li Wang

College of Computer Science and Technology, Jilin University, Changchun, Jilin, China

Miaoyue Shi

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Contributions

Qinglan Zhao and Li Wang contributed to the conception and design of the study; Xiaoqing Cui, Ting Liu, Hanxue Li andMiaoyue Shi performed the experiments, collected and analyzed data; Qinglan Zhao wrote the manuscript; All authors reviewed and approved the final version of the manuscript.

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Correspondence to Li Wang .

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The study protocol was approved by the Ethics Committee of The Second Affiliated Hospital of Xi’an Jiaotong University. Informed consent was obtained from all the study subjects before enrollment.

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Zhao, Q., Cui, X., Liu, T. et al. Exploring variations in IPC competencies: a cross-sectional study among healthcare professionals in Northwest China. BMC Infect Dis 24 , 420 (2024). https://doi.org/10.1186/s12879-024-09288-y

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Received : 20 February 2024

Accepted : 03 April 2024

Published : 22 April 2024

DOI : https://doi.org/10.1186/s12879-024-09288-y

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