publishing literature review paper

Purdue Online Writing Lab Purdue OWL® College of Liberal Arts

Writing a Literature Review

Welcome to the Purdue OWL

This page is brought to you by the OWL at Purdue University. When printing this page, you must include the entire legal notice.

Copyright ©1995-2018 by The Writing Lab & The OWL at Purdue and Purdue University. All rights reserved. This material may not be published, reproduced, broadcast, rewritten, or redistributed without permission. Use of this site constitutes acceptance of our terms and conditions of fair use.

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

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

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

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

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

What are the parts of a lit review?

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

Introduction:

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

Conclusion:

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

How should I organize my lit review?

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

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

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

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

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

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

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

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

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

• How to Write a Literature Review | Guide, Examples, & Templates

How to Write a Literature Review | Guide, Examples, & Templates

Published on January 2, 2023 by Shona McCombes . Revised on September 11, 2023.

What is a literature review? A literature review is a survey of scholarly sources on a specific topic. It provides an overview of current knowledge, allowing you to identify relevant theories, methods, and gaps in the existing research that you can later apply to your paper, thesis, or dissertation topic .

There are five key steps to writing a literature review:

• Search for relevant literature
• Evaluate sources
• Identify themes, debates, and gaps
• Outline the structure
• Write your literature review

A good literature review doesn’t just summarize sources—it analyzes, synthesizes , and critically evaluates to give a clear picture of the state of knowledge on the subject.

Instantly correct all language mistakes in your text

Upload your document to correct all your mistakes in minutes

Table of contents

What is the purpose of a literature review, examples of literature reviews, step 1 – search for relevant literature, step 2 – evaluate and select sources, step 3 – identify themes, debates, and gaps, step 4 – outline your literature review’s structure, step 5 – write your literature review, free lecture slides, other interesting articles, frequently asked questions, introduction.

• Quick Run-through
• Step 1 & 2

When you write a thesis , dissertation , or research paper , you will likely have to conduct a literature review to situate your research within existing knowledge. The literature review gives you a chance to:

• Demonstrate your familiarity with the topic and its scholarly context
• Develop a theoretical framework and methodology for your research
• Position your work in relation to other researchers and theorists
• Show how your research addresses a gap or contributes to a debate
• Evaluate the current state of research and demonstrate your knowledge of the scholarly debates around your topic.

Writing literature reviews is a particularly important skill if you want to apply for graduate school or pursue a career in research. We’ve written a step-by-step guide that you can follow below.

Prevent plagiarism. Run a free check.

Writing literature reviews can be quite challenging! A good starting point could be to look at some examples, depending on what kind of literature review you’d like to write.

• Example literature review #1: “Why Do People Migrate? A Review of the Theoretical Literature” ( Theoretical literature review about the development of economic migration theory from the 1950s to today.)
• Example literature review #2: “Literature review as a research methodology: An overview and guidelines” ( Methodological literature review about interdisciplinary knowledge acquisition and production.)
• Example literature review #3: “The Use of Technology in English Language Learning: A Literature Review” ( Thematic literature review about the effects of technology on language acquisition.)
• Example literature review #4: “Learners’ Listening Comprehension Difficulties in English Language Learning: A Literature Review” ( Chronological literature review about how the concept of listening skills has changed over time.)

You can also check out our templates with literature review examples and sample outlines at the links below.

Download Word doc Download Google doc

Before you begin searching for literature, you need a clearly defined topic .

If you are writing the literature review section of a dissertation or research paper, you will search for literature related to your research problem and questions .

Make a list of keywords

Start by creating a list of keywords related to your research question. Include each of the key concepts or variables you’re interested in, and list any synonyms and related terms. You can add to this list as you discover new keywords in the process of your literature search.

• Social media, Facebook, Instagram, Twitter, Snapchat, TikTok
• Body image, self-perception, self-esteem, mental health
• Generation Z, teenagers, adolescents, youth

Search for relevant sources

Use your keywords to begin searching for sources. Some useful databases to search for journals and articles include:

• Your university’s library catalogue
• Google Scholar
• Project Muse (humanities and social sciences)
• Medline (life sciences and biomedicine)
• EconLit (economics)
• Inspec (physics, engineering and computer science)

You can also use boolean operators to help narrow down your search.

Make sure to read the abstract to find out whether an article is relevant to your question. When you find a useful book or article, you can check the bibliography to find other relevant sources.

You likely won’t be able to read absolutely everything that has been written on your topic, so it will be necessary to evaluate which sources are most relevant to your research question.

For each publication, ask yourself:

• What question or problem is the author addressing?
• What are the key concepts and how are they defined?
• What are the key theories, models, and methods?
• Does the research use established frameworks or take an innovative approach?
• What are the results and conclusions of the study?
• How does the publication relate to other literature in the field? Does it confirm, add to, or challenge established knowledge?
• What are the strengths and weaknesses of the research?

Make sure the sources you use are credible , and make sure you read any landmark studies and major theories in your field of research.

You can use our template to summarize and evaluate sources you’re thinking about using. Click on either button below to download.

Take notes and cite your sources

As you read, you should also begin the writing process. Take notes that you can later incorporate into the text of your literature review.

It is important to keep track of your sources with citations to avoid plagiarism . It can be helpful to make an annotated bibliography , where you compile full citation information and write a paragraph of summary and analysis for each source. This helps you remember what you read and saves time later in the process.

Don't submit your assignments before you do this

The academic proofreading tool has been trained on 1000s of academic texts. Making it the most accurate and reliable proofreading tool for students. Free citation check included.

Try for free

To begin organizing your literature review’s argument and structure, be sure you understand the connections and relationships between the sources you’ve read. Based on your reading and notes, you can look for:

• Trends and patterns (in theory, method or results): do certain approaches become more or less popular over time?
• Themes: what questions or concepts recur across the literature?
• Debates, conflicts and contradictions: where do sources disagree?
• Pivotal publications: are there any influential theories or studies that changed the direction of the field?
• Gaps: what is missing from the literature? Are there weaknesses that need to be addressed?

This step will help you work out the structure of your literature review and (if applicable) show how your own research will contribute to existing knowledge.

• Most research has focused on young women.
• There is an increasing interest in the visual aspects of social media.
• But there is still a lack of robust research on highly visual platforms like Instagram and Snapchat—this is a gap that you could address in your own research.

There are various approaches to organizing the body of a literature review. Depending on the length of your literature review, you can combine several of these strategies (for example, your overall structure might be thematic, but each theme is discussed chronologically).

Chronological

The simplest approach is to trace the development of the topic over time. However, if you choose this strategy, be careful to avoid simply listing and summarizing sources in order.

Try to analyze patterns, turning points and key debates that have shaped the direction of the field. Give your interpretation of how and why certain developments occurred.

If you have found some recurring central themes, you can organize your literature review into subsections that address different aspects of the topic.

For example, if you are reviewing literature about inequalities in migrant health outcomes, key themes might include healthcare policy, language barriers, cultural attitudes, legal status, and economic access.

Methodological

If you draw your sources from different disciplines or fields that use a variety of research methods , you might want to compare the results and conclusions that emerge from different approaches. For example:

• Look at what results have emerged in qualitative versus quantitative research
• Discuss how the topic has been approached by empirical versus theoretical scholarship
• Divide the literature into sociological, historical, and cultural sources

Theoretical

A literature review is often the foundation for a theoretical framework . You can use it to discuss various theories, models, and definitions of key concepts.

You might argue for the relevance of a specific theoretical approach, or combine various theoretical concepts to create a framework for your research.

Like any other academic text , your literature review should have an introduction , a main body, and a conclusion . What you include in each depends on the objective of your literature review.

The introduction should clearly establish the focus and purpose of the literature review.

Depending on the length of your literature review, you might want to divide the body into subsections. You can use a subheading for each theme, time period, or methodological approach.

As you write, you can follow these tips:

• Summarize and synthesize: give an overview of the main points of each source and combine them into a coherent whole
• Analyze and interpret: don’t just paraphrase other researchers — add your own interpretations where possible, discussing the significance of findings in relation to the literature as a whole
• Critically evaluate: mention the strengths and weaknesses of your sources
• Write in well-structured paragraphs: use transition words and topic sentences to draw connections, comparisons and contrasts

In the conclusion, you should summarize the key findings you have taken from the literature and emphasize their significance.

When you’ve finished writing and revising your literature review, don’t forget to proofread thoroughly before submitting. Not a language expert? Check out Scribbr’s professional proofreading services !

This article has been adapted into lecture slides that you can use to teach your students about writing a literature review.

Scribbr slides are free to use, customize, and distribute for educational purposes.

Open Google Slides Download PowerPoint

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

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

A literature review is a survey of scholarly sources (such as books, journal articles, and theses) related to a specific topic or research question .

It is often written as part of a thesis, dissertation , or research paper , in order to situate your work in relation to existing knowledge.

There are several reasons to conduct a literature review at the beginning of a research project:

• To familiarize yourself with the current state of knowledge on your topic
• To ensure that you’re not just repeating what others have already done
• To identify gaps in knowledge and unresolved problems that your research can address
• To develop your theoretical framework and methodology
• To provide an overview of the key findings and debates on the topic

Writing the literature review shows your reader how your work relates to existing research and what new insights it will contribute.

The literature review usually comes near the beginning of your thesis or dissertation . After the introduction , it grounds your research in a scholarly field and leads directly to your theoretical framework or methodology .

A literature review is a survey of credible sources on a topic, often used in dissertations , theses, and research papers . Literature reviews give an overview of knowledge on a subject, helping you identify relevant theories and methods, as well as gaps in existing research. Literature reviews are set up similarly to other  academic texts , with an introduction , a main body, and a conclusion .

An  annotated bibliography is a list of  source references that has a short description (called an annotation ) for each of the sources. It is often assigned as part of the research process for a  paper .  

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

McCombes, S. (2023, September 11). How to Write a Literature Review | Guide, Examples, & Templates. Scribbr. Retrieved September 23, 2024, from https://www.scribbr.com/dissertation/literature-review/

Is this article helpful?

Shona McCombes

Other students also liked, what is a theoretical framework | guide to organizing, what is a research methodology | steps & tips, how to write a research proposal | examples & templates, what is your plagiarism score.

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• CAREER FEATURE
• 04 December 2020
• Correction 09 December 2020

How to write a superb literature review

Andy Tay is a freelance writer based in Singapore.

You can also search for this author in PubMed   Google Scholar

Literature reviews are important resources for scientists. They provide historical context for a field while offering opinions on its future trajectory. Creating them can provide inspiration for one’s own research, as well as some practice in writing. But few scientists are trained in how to write a review — or in what constitutes an excellent one. Even picking the appropriate software to use can be an involved decision (see ‘Tools and techniques’). So Nature asked editors and working scientists with well-cited reviews for their tips.

Access options

Access Nature and 54 other Nature Portfolio journals

Get Nature+, our best-value online-access subscription

24,99 € / 30 days

cancel any time

Subscribe to this journal

Receive 51 print issues and online access

185,98 € per year

only 3,65 € per issue

Rent or buy this article

Prices vary by article type

Prices may be subject to local taxes which are calculated during checkout

doi: https://doi.org/10.1038/d41586-020-03422-x

Interviews have been edited for length and clarity.

Updates & Corrections

Correction 09 December 2020 : An earlier version of the tables in this article included some incorrect details about the programs Zotero, Endnote and Manubot. These have now been corrected.

Hsing, I.-M., Xu, Y. & Zhao, W. Electroanalysis 19 , 755–768 (2007).

Article   Google Scholar  

Ledesma, H. A. et al. Nature Nanotechnol. 14 , 645–657 (2019).

Article   PubMed   Google Scholar  

Brahlek, M., Koirala, N., Bansal, N. & Oh, S. Solid State Commun. 215–216 , 54–62 (2015).

Choi, Y. & Lee, S. Y. Nature Rev. Chem . https://doi.org/10.1038/s41570-020-00221-w (2020).

Download references

Related Articles

• Research management

I botched my poster presentation — how do I perform better next time?

Career Feature 27 SEP 24

Researchers in Hungary raise fears of brain drain after ‘body blow’ EU funding suspension

Career News 26 SEP 24

How I apply Indigenous wisdom to Western science and nurture Native American students

Career Q&A 25 SEP 24

More measures needed to ease funding competition in China

Correspondence 24 SEP 24

Gender inequity persists among journal chief editors

The human costs of the research-assessment culture

Career Feature 09 SEP 24

Data integrity concerns flagged in 130 women’s health papers — all by one co-author

News 25 SEP 24

‘Substandard and unworthy’: why it’s time to banish bad-mannered reviews

Career Q&A 23 SEP 24

Postdoctoral Fellow in Biomedical Optics and Medical Physics

We seek skilled and enthusiastic candidates for Postdoctoral Fellow positions in the Biomedical Optical Imaging for cancer research.

Dallas, Texas (US)

UT Southwestern Medical Center, BIRTLab

Associate Professor

J. Craig Venter Institute is conducting a faculty search for Associate Professors position in Rockville, MD and San Diego, CA campuses.

Rockville, Maryland or San Diego, California

J. Craig Venter Institute

Associate Professor position (Tenure Track), Dept. of Computational & Systems Biology, U. Pittsburgh School of Medicine. Please apply by Dec. 2, 2024.

University of Pittsburgh, Pittsburgh

University of Pittsburgh | DCSB

Assistant Professor

Assistant Professor position (Tenure Track), Dept. of Computational & Systems Biology, U. Pittsburgh School of Medicine. Please apply by Dec. 2, 2024.

Independent Group Leader Positions in Computational and/or Experimental Medical Systems Biology

NIMSB is recruiting up to 4 Independent Group Leaders in Computational and/or Experimental Medical Systems Biology

Greater Lisbon - Portugal

NOVA - NIMSB

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

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

A literature review surveys prior research published in books, scholarly articles, and any other sources relevant to a particular issue, area of research, or theory, and by so doing, provides a description, summary, and critical evaluation of these works in relation to the research problem being investigated. Literature reviews are designed to provide an overview of sources you have used in researching a particular topic and to demonstrate to your readers how your research fits within existing scholarship about the topic.

Fink, Arlene. Conducting Research Literature Reviews: From the Internet to Paper . Fourth edition. Thousand Oaks, CA: SAGE, 2014.

Importance of a Good Literature Review

A literature review may consist of simply a summary of key sources, but in the social sciences, a literature review usually has an organizational pattern and combines both summary and synthesis, often within specific conceptual categories . A summary is a recap of the important information of the source, but a synthesis is a re-organization, or a reshuffling, of that information in a way that informs how you are planning to investigate a research problem. The analytical features of a literature review might:

• Give a new interpretation of old material or combine new with old interpretations,
• Trace the intellectual progression of the field, including major debates,
• Depending on the situation, evaluate the sources and advise the reader on the most pertinent or relevant research, or
• Usually in the conclusion of a literature review, identify where gaps exist in how a problem has been researched to date.

Given this, the purpose of a literature review is to:

• Place each work in the context of its contribution to understanding the research problem being studied.
• Describe the relationship of each work to the others under consideration.
• Identify new ways to interpret prior research.
• Reveal any gaps that exist in the literature.
• Resolve conflicts amongst seemingly contradictory previous studies.
• Identify areas of prior scholarship to prevent duplication of effort.
• Point the way in fulfilling a need for additional research.
• Locate your own research within the context of existing literature [very important].

Fink, Arlene. Conducting Research Literature Reviews: From the Internet to Paper. 2nd ed. Thousand Oaks, CA: Sage, 2005; Hart, Chris. Doing a Literature Review: Releasing the Social Science Research Imagination . Thousand Oaks, CA: Sage Publications, 1998; Jesson, Jill. Doing Your Literature Review: Traditional and Systematic Techniques . Los Angeles, CA: SAGE, 2011; Knopf, Jeffrey W. "Doing a Literature Review." PS: Political Science and Politics 39 (January 2006): 127-132; Ridley, Diana. The Literature Review: A Step-by-Step Guide for Students . 2nd ed. Los Angeles, CA: SAGE, 2012.

Types of Literature Reviews

It is important to think of knowledge in a given field as consisting of three layers. First, there are the primary studies that researchers conduct and publish. Second are the reviews of those studies that summarize and offer new interpretations built from and often extending beyond the primary studies. Third, there are the perceptions, conclusions, opinion, and interpretations that are shared informally among scholars that become part of the body of epistemological traditions within the field.

In composing a literature review, it is important to note that it is often this third layer of knowledge that is cited as "true" even though it often has only a loose relationship to the primary studies and secondary literature reviews. Given this, while literature reviews are designed to provide an overview and synthesis of pertinent sources you have explored, there are a number of approaches you could adopt depending upon the type of analysis underpinning your study.

Argumentative Review This form examines literature selectively in order to support or refute an argument, deeply embedded assumption, or philosophical problem already established in the literature. The purpose is to develop a body of literature that establishes a contrarian viewpoint. Given the value-laden nature of some social science research [e.g., educational reform; immigration control], argumentative approaches to analyzing the literature can be a legitimate and important form of discourse. However, note that they can also introduce problems of bias when they are used to make summary claims of the sort found in systematic reviews [see below].

Integrative Review Considered a form of research that reviews, critiques, and synthesizes representative literature on a topic in an integrated way such that new frameworks and perspectives on the topic are generated. The body of literature includes all studies that address related or identical hypotheses or research problems. A well-done integrative review meets the same standards as primary research in regard to clarity, rigor, and replication. This is the most common form of review in the social sciences.

Historical Review Few things rest in isolation from historical precedent. Historical literature reviews focus on examining research throughout a period of time, often starting with the first time an issue, concept, theory, phenomena emerged in the literature, then tracing its evolution within the scholarship of a discipline. The purpose is to place research in a historical context to show familiarity with state-of-the-art developments and to identify the likely directions for future research.

Methodological Review A review does not always focus on what someone said [findings], but how they came about saying what they say [method of analysis]. Reviewing methods of analysis provides a framework of understanding at different levels [i.e. those of theory, substantive fields, research approaches, and data collection and analysis techniques], how researchers draw upon a wide variety of knowledge ranging from the conceptual level to practical documents for use in fieldwork in the areas of ontological and epistemological consideration, quantitative and qualitative integration, sampling, interviewing, data collection, and data analysis. This approach helps highlight ethical issues which you should be aware of and consider as you go through your own study.

Systematic Review This form consists of an overview of existing evidence pertinent to a clearly formulated research question, which uses pre-specified and standardized methods to identify and critically appraise relevant research, and to collect, report, and analyze data from the studies that are included in the review. The goal is to deliberately document, critically evaluate, and summarize scientifically all of the research about a clearly defined research problem . Typically it focuses on a very specific empirical question, often posed in a cause-and-effect form, such as "To what extent does A contribute to B?" This type of literature review is primarily applied to examining prior research studies in clinical medicine and allied health fields, but it is increasingly being used in the social sciences.

Theoretical Review The purpose of this form is to examine the corpus of theory that has accumulated in regard to an issue, concept, theory, phenomena. The theoretical literature review helps to establish what theories already exist, the relationships between them, to what degree the existing theories have been investigated, and to develop new hypotheses to be tested. Often this form is used to help establish a lack of appropriate theories or reveal that current theories are inadequate for explaining new or emerging research problems. The unit of analysis can focus on a theoretical concept or a whole theory or framework.

NOTE: Most often the literature review will incorporate some combination of types. For example, a review that examines literature supporting or refuting an argument, assumption, or philosophical problem related to the research problem will also need to include writing supported by sources that establish the history of these arguments in the literature.

Baumeister, Roy F. and Mark R. Leary. "Writing Narrative Literature Reviews."  Review of General Psychology 1 (September 1997): 311-320; Mark R. Fink, Arlene. Conducting Research Literature Reviews: From the Internet to Paper . 2nd ed. Thousand Oaks, CA: Sage, 2005; Hart, Chris. Doing a Literature Review: Releasing the Social Science Research Imagination . Thousand Oaks, CA: Sage Publications, 1998; Kennedy, Mary M. "Defining a Literature." Educational Researcher 36 (April 2007): 139-147; Petticrew, Mark and Helen Roberts. Systematic Reviews in the Social Sciences: A Practical Guide . Malden, MA: Blackwell Publishers, 2006; Torracro, Richard. "Writing Integrative Literature Reviews: Guidelines and Examples." Human Resource Development Review 4 (September 2005): 356-367; Rocco, Tonette S. and Maria S. Plakhotnik. "Literature Reviews, Conceptual Frameworks, and Theoretical Frameworks: Terms, Functions, and Distinctions." Human Ressource Development Review 8 (March 2008): 120-130; Sutton, Anthea. Systematic Approaches to a Successful Literature Review . Los Angeles, CA: Sage Publications, 2016.

Structure and Writing Style

I.  Thinking About Your Literature Review

The structure of a literature review should include the following in support of understanding the research problem :

• An overview of the subject, issue, or theory under consideration, along with the objectives of the literature review,
• Division of works under review into themes or categories [e.g. works that support a particular position, those against, and those offering alternative approaches entirely],
• An explanation of how each work is similar to and how it varies from the others,
• Conclusions as to which pieces are best considered in their argument, are most convincing of their opinions, and make the greatest contribution to the understanding and development of their area of research.

The critical evaluation of each work should consider :

• Provenance -- what are the author's credentials? Are the author's arguments supported by evidence [e.g. primary historical material, case studies, narratives, statistics, recent scientific findings]?
• Methodology -- were the techniques used to identify, gather, and analyze the data appropriate to addressing the research problem? Was the sample size appropriate? Were the results effectively interpreted and reported?
• Objectivity -- is the author's perspective even-handed or prejudicial? Is contrary data considered or is certain pertinent information ignored to prove the author's point?
• Persuasiveness -- which of the author's theses are most convincing or least convincing?
• Validity -- are the author's arguments and conclusions convincing? Does the work ultimately contribute in any significant way to an understanding of the subject?

II.  Development of the Literature Review

Four Basic Stages of Writing 1.  Problem formulation -- which topic or field is being examined and what are its component issues? 2.  Literature search -- finding materials relevant to the subject being explored. 3.  Data evaluation -- determining which literature makes a significant contribution to the understanding of the topic. 4.  Analysis and interpretation -- discussing the findings and conclusions of pertinent literature.

Consider the following issues before writing the literature review: Clarify If your assignment is not specific about what form your literature review should take, seek clarification from your professor by asking these questions: 1.  Roughly how many sources would be appropriate to include? 2.  What types of sources should I review (books, journal articles, websites; scholarly versus popular sources)? 3.  Should I summarize, synthesize, or critique sources by discussing a common theme or issue? 4.  Should I evaluate the sources in any way beyond evaluating how they relate to understanding the research problem? 5.  Should I provide subheadings and other background information, such as definitions and/or a history? Find Models Use the exercise of reviewing the literature to examine how authors in your discipline or area of interest have composed their literature review sections. Read them to get a sense of the types of themes you might want to look for in your own research or to identify ways to organize your final review. The bibliography or reference section of sources you've already read, such as required readings in the course syllabus, are also excellent entry points into your own research. Narrow the Topic The narrower your topic, the easier it will be to limit the number of sources you need to read in order to obtain a good survey of relevant resources. Your professor will probably not expect you to read everything that's available about the topic, but you'll make the act of reviewing easier if you first limit scope of the research problem. A good strategy is to begin by searching the USC Libraries Catalog for recent books about the topic and review the table of contents for chapters that focuses on specific issues. You can also review the indexes of books to find references to specific issues that can serve as the focus of your research. For example, a book surveying the history of the Israeli-Palestinian conflict may include a chapter on the role Egypt has played in mediating the conflict, or look in the index for the pages where Egypt is mentioned in the text. Consider Whether Your Sources are Current Some disciplines require that you use information that is as current as possible. This is particularly true in disciplines in medicine and the sciences where research conducted becomes obsolete very quickly as new discoveries are made. However, when writing a review in the social sciences, a survey of the history of the literature may be required. In other words, a complete understanding the research problem requires you to deliberately examine how knowledge and perspectives have changed over time. Sort through other current bibliographies or literature reviews in the field to get a sense of what your discipline expects. You can also use this method to explore what is considered by scholars to be a "hot topic" and what is not.

III.  Ways to Organize Your Literature Review

Chronology of Events If your review follows the chronological method, you could write about the materials according to when they were published. This approach should only be followed if a clear path of research building on previous research can be identified and that these trends follow a clear chronological order of development. For example, a literature review that focuses on continuing research about the emergence of German economic power after the fall of the Soviet Union. By Publication Order your sources by publication chronology, then, only if the order demonstrates a more important trend. For instance, you could order a review of literature on environmental studies of brown fields if the progression revealed, for example, a change in the soil collection practices of the researchers who wrote and/or conducted the studies. Thematic [“conceptual categories”] A thematic literature review is the most common approach to summarizing prior research in the social and behavioral sciences. Thematic reviews are organized around a topic or issue, rather than the progression of time, although the progression of time may still be incorporated into a thematic review. For example, a review of the Internet’s impact on American presidential politics could focus on the development of online political satire. While the study focuses on one topic, the Internet’s impact on American presidential politics, it would still be organized chronologically reflecting technological developments in media. The difference in this example between a "chronological" and a "thematic" approach is what is emphasized the most: themes related to the role of the Internet in presidential politics. Note that more authentic thematic reviews tend to break away from chronological order. A review organized in this manner would shift between time periods within each section according to the point being made. Methodological A methodological approach focuses on the methods utilized by the researcher. For the Internet in American presidential politics project, one methodological approach would be to look at cultural differences between the portrayal of American presidents on American, British, and French websites. Or the review might focus on the fundraising impact of the Internet on a particular political party. A methodological scope will influence either the types of documents in the review or the way in which these documents are discussed.

Other Sections of Your Literature Review Once you've decided on the organizational method for your literature review, the sections you need to include in the paper should be easy to figure out because they arise from your organizational strategy. In other words, a chronological review would have subsections for each vital time period; a thematic review would have subtopics based upon factors that relate to the theme or issue. However, sometimes you may need to add additional sections that are necessary for your study, but do not fit in the organizational strategy of the body. What other sections you include in the body is up to you. However, only include what is necessary for the reader to locate your study within the larger scholarship about the research problem.

Here are examples of other sections, usually in the form of a single paragraph, you may need to include depending on the type of review you write:

• Current Situation : Information necessary to understand the current topic or focus of the literature review.
• Sources Used : Describes the methods and resources [e.g., databases] you used to identify the literature you reviewed.
• History : The chronological progression of the field, the research literature, or an idea that is necessary to understand the literature review, if the body of the literature review is not already a chronology.
• Selection Methods : Criteria you used to select (and perhaps exclude) sources in your literature review. For instance, you might explain that your review includes only peer-reviewed [i.e., scholarly] sources.
• Standards : Description of the way in which you present your information.
• Questions for Further Research : What questions about the field has the review sparked? How will you further your research as a result of the review?

IV.  Writing Your Literature Review

Once you've settled on how to organize your literature review, you're ready to write each section. When writing your review, keep in mind these issues.

Use Evidence A literature review section is, in this sense, just like any other academic research paper. Your interpretation of the available sources must be backed up with evidence [citations] that demonstrates that what you are saying is valid. Be Selective Select only the most important points in each source to highlight in the review. The type of information you choose to mention should relate directly to the research problem, whether it is thematic, methodological, or chronological. Related items that provide additional information, but that are not key to understanding the research problem, can be included in a list of further readings . Use Quotes Sparingly Some short quotes are appropriate if you want to emphasize a point, or if what an author stated cannot be easily paraphrased. Sometimes you may need to quote certain terminology that was coined by the author, is not common knowledge, or taken directly from the study. Do not use extensive quotes as a substitute for using your own words in reviewing the literature. Summarize and Synthesize Remember to summarize and synthesize your sources within each thematic paragraph as well as throughout the review. Recapitulate important features of a research study, but then synthesize it by rephrasing the study's significance and relating it to your own work and the work of others. Keep Your Own Voice While the literature review presents others' ideas, your voice [the writer's] should remain front and center. For example, weave references to other sources into what you are writing but maintain your own voice by starting and ending the paragraph with your own ideas and wording. Use Caution When Paraphrasing When paraphrasing a source that is not your own, be sure to represent the author's information or opinions accurately and in your own words. Even when paraphrasing an author’s work, you still must provide a citation to that work.

V.  Common Mistakes to Avoid

These are the most common mistakes made in reviewing social science research literature.

• Sources in your literature review do not clearly relate to the research problem;
• You do not take sufficient time to define and identify the most relevant sources to use in the literature review related to the research problem;
• Relies exclusively on secondary analytical sources rather than including relevant primary research studies or data;
• Uncritically accepts another researcher's findings and interpretations as valid, rather than examining critically all aspects of the research design and analysis;
• Does not describe the search procedures that were used in identifying the literature to review;
• Reports isolated statistical results rather than synthesizing them in chi-squared or meta-analytic methods; and,
• Only includes research that validates assumptions and does not consider contrary findings and alternative interpretations found in the literature.

Cook, Kathleen E. and Elise Murowchick. “Do Literature Review Skills Transfer from One Course to Another?” Psychology Learning and Teaching 13 (March 2014): 3-11; Fink, Arlene. Conducting Research Literature Reviews: From the Internet to Paper . 2nd ed. Thousand Oaks, CA: Sage, 2005; Hart, Chris. Doing a Literature Review: Releasing the Social Science Research Imagination . Thousand Oaks, CA: Sage Publications, 1998; Jesson, Jill. Doing Your Literature Review: Traditional and Systematic Techniques . London: SAGE, 2011; Literature Review Handout. Online Writing Center. Liberty University; Literature Reviews. The Writing Center. University of North Carolina; Onwuegbuzie, Anthony J. and Rebecca Frels. Seven Steps to a Comprehensive Literature Review: A Multimodal and Cultural Approach . Los Angeles, CA: SAGE, 2016; Ridley, Diana. The Literature Review: A Step-by-Step Guide for Students . 2nd ed. Los Angeles, CA: SAGE, 2012; Randolph, Justus J. “A Guide to Writing the Dissertation Literature Review." Practical Assessment, Research, and Evaluation. vol. 14, June 2009; Sutton, Anthea. Systematic Approaches to a Successful Literature Review . Los Angeles, CA: Sage Publications, 2016; Taylor, Dena. The Literature Review: A Few Tips On Conducting It. University College Writing Centre. University of Toronto; Writing a Literature Review. Academic Skills Centre. University of Canberra.

Writing Tip

Break Out of Your Disciplinary Box!

Thinking interdisciplinarily about a research problem can be a rewarding exercise in applying new ideas, theories, or concepts to an old problem. For example, what might cultural anthropologists say about the continuing conflict in the Middle East? In what ways might geographers view the need for better distribution of social service agencies in large cities than how social workers might study the issue? You don’t want to substitute a thorough review of core research literature in your discipline for studies conducted in other fields of study. However, particularly in the social sciences, thinking about research problems from multiple vectors is a key strategy for finding new solutions to a problem or gaining a new perspective. Consult with a librarian about identifying research databases in other disciplines; almost every field of study has at least one comprehensive database devoted to indexing its research literature.

Frodeman, Robert. The Oxford Handbook of Interdisciplinarity . New York: Oxford University Press, 2010.

Another Writing Tip

Don't Just Review for Content!

While conducting a review of the literature, maximize the time you devote to writing this part of your paper by thinking broadly about what you should be looking for and evaluating. Review not just what scholars are saying, but how are they saying it. Some questions to ask:

• How are they organizing their ideas?
• What methods have they used to study the problem?
• What theories have been used to explain, predict, or understand their research problem?
• What sources have they cited to support their conclusions?
• How have they used non-textual elements [e.g., charts, graphs, figures, etc.] to illustrate key points?

When you begin to write your literature review section, you'll be glad you dug deeper into how the research was designed and constructed because it establishes a means for developing more substantial analysis and interpretation of the research problem.

Hart, Chris. Doing a Literature Review: Releasing the Social Science Research Imagination . Thousand Oaks, CA: Sage Publications, 1 998.

Yet Another Writing Tip

When Do I Know I Can Stop Looking and Move On?

Here are several strategies you can utilize to assess whether you've thoroughly reviewed the literature:

• Look for repeating patterns in the research findings . If the same thing is being said, just by different people, then this likely demonstrates that the research problem has hit a conceptual dead end. At this point consider: Does your study extend current research?  Does it forge a new path? Or, does is merely add more of the same thing being said?
• Look at sources the authors cite to in their work . If you begin to see the same researchers cited again and again, then this is often an indication that no new ideas have been generated to address the research problem.
• Search Google Scholar to identify who has subsequently cited leading scholars already identified in your literature review [see next sub-tab]. This is called citation tracking and there are a number of sources that can help you identify who has cited whom, particularly scholars from outside of your discipline. Here again, if the same authors are being cited again and again, this may indicate no new literature has been written on the topic.

Onwuegbuzie, Anthony J. and Rebecca Frels. Seven Steps to a Comprehensive Literature Review: A Multimodal and Cultural Approach . Los Angeles, CA: Sage, 2016; Sutton, Anthea. Systematic Approaches to a Successful Literature Review . Los Angeles, CA: Sage Publications, 2016.

• << Previous: Theoretical Framework
• Next: Citation Tracking >>
• Last Updated: Sep 27, 2024 1:09 PM
• URL: https://libguides.usc.edu/writingguide

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

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

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

What are Literature Reviews?

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

Goals of Literature Reviews

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

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

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

What kinds of sources require a Literature Review?

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

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

Types of Literature Reviews

What kinds of literature reviews are written?

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

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

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

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

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

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

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

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

Literature Reviews in the Health Sciences

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

Harvey Cushing/John Hay Whitney Medical Library

• Collections
• Research Help

YSN Doctoral Programs: Steps in Conducting a Literature Review

• Biomedical Databases
• Global (Public Health) Databases
• Soc. Sci., History, and Law Databases
• Grey Literature
• Trials Registers
• Data and Statistics
• Public Policy
• Google Tips
• Recommended Books
• Steps in Conducting a Literature Review

What is a literature review?

A literature review is an integrated analysis -- not just a summary-- of scholarly writings and other relevant evidence related directly to your research question.  That is, it represents a synthesis of the evidence that provides background information on your topic and shows a association between the evidence and your research question.

A literature review may be a stand alone work or the introduction to a larger research paper, depending on the assignment.  Rely heavily on the guidelines your instructor has given you.

Why is it important?

A literature review is important because it:

• Explains the background of research on a topic.
• Demonstrates why a topic is significant to a subject area.
• Discovers relationships between research studies/ideas.
• Identifies major themes, concepts, and researchers on a topic.
• Identifies critical gaps and points of disagreement.
• Discusses further research questions that logically come out of the previous studies.

APA7 Style resources

APA Style Blog - for those harder to find answers

1. Choose a topic. Define your research question.

Your literature review should be guided by your central research question.  The literature represents background and research developments related to a specific research question, interpreted and analyzed by you in a synthesized way.

• Make sure your research question is not too broad or too narrow.  Is it manageable?
• Begin writing down terms that are related to your question. These will be useful for searches later.
• If you have the opportunity, discuss your topic with your professor and your class mates.

2. Decide on the scope of your review

How many studies do you need to look at? How comprehensive should it be? How many years should it cover? 

• This may depend on your assignment.  How many sources does the assignment require?

3. Select the databases you will use to conduct your searches.

Make a list of the databases you will search. 

Where to find databases:

• use the tabs on this guide
• Find other databases in the Nursing Information Resources web page
• More on the Medical Library web page
• ... and more on the Yale University Library web page

4. Conduct your searches to find the evidence. Keep track of your searches.

• Use the key words in your question, as well as synonyms for those words, as terms in your search. Use the database tutorials for help.
• Save the searches in the databases. This saves time when you want to redo, or modify, the searches. It is also helpful to use as a guide is the searches are not finding any useful results.
• Review the abstracts of research studies carefully. This will save you time.
• Use the bibliographies and references of research studies you find to locate others.
• Check with your professor, or a subject expert in the field, if you are missing any key works in the field.
• Ask your librarian for help at any time.
• Use a citation manager, such as EndNote as the repository for your citations. See the EndNote tutorials for help.

Review the literature

Some questions to help you analyze the research:

• What was the research question of the study you are reviewing? What were the authors trying to discover?
• Was the research funded by a source that could influence the findings?
• What were the research methodologies? Analyze its literature review, the samples and variables used, the results, and the conclusions.
• Does the research seem to be complete? Could it have been conducted more soundly? What further questions does it raise?
• If there are conflicting studies, why do you think that is?
• How are the authors viewed in the field? Has this study been cited? If so, how has it been analyzed?

Tips: 

• Review the abstracts carefully.  
• Keep careful notes so that you may track your thought processes during the research process.
• Create a matrix of the studies for easy analysis, and synthesis, across all of the studies.
• << Previous: Recommended Books
• Last Updated: Jun 20, 2024 9:08 AM
• URL: https://guides.library.yale.edu/YSNDoctoral

How to... write a literature review

To write a literature review it is important to look at the relationships between different views, draw out the key themes and structure appropriately. See our step by step guide for some useful tips.

On this page

What is a literature review.

• Why write a literature review

Creating a literature review - step by step guide

Related topics.

Essentially, it is a description of work that has already been published in a particular field or on a specific topic. There are two main types of literature review:

Research literature review – This doesn’t contain new research but looks at experiments already published and reports on their findings. It gives an overview of what has been said, who the key writers are, the prevailing theories and hypotheses, the questions being asked, and the methods and methodologies that have proved useful. Systematic or evidence-based literature review – Especially popular in medicine, these reviews are designed to find the best form of intervention, or explore summaries and critiques leading to better future practice.

Why write a literature review?

For students, a literature review is often part of a thesis or dissertation, forming an early context-setting chapter. For academics, it is a necessary part of a research paper, setting the scene and showing how their own work contributes to the body of knowledge. 

This guide focuses on literature reviews that go on to be published as individual journal papers.  

Research literature reviews

The format can be purely descriptive, i.e. an annotated bibliography, or it might provide a critical assessment of the literature in a particular field, stating where the weaknesses and gaps are, contrasting the views of particular authors, or raising questions. 

Whichever format you choose, it’s crucial that the review doesn’t just list and paraphrase the content of the papers involved – it should also show evidence of evaluation, and explore relationships between the material so that key themes emerge.

Systematic or evidence-based literature reviews

These use explicit and transparent methods. They follow a standard protocol, or series of steps, often established in consultation with a panel.

All procedures are documented, i.e. there is a research audit trail of databases and search terms used, so that others can easily replicate the steps followed. The documented procedures might include:

• The search parameters
• Databases used
• How papers were analysed
• Criteria for inclusion in the final review

The systematic review was originally developed in the field of medicine, through the Cochrane Collaboration (Hemsley-Brown and Oplatka, 2006). While its origins lie in the field of evidence-based healthcare, it has also been adopted by some researchers. Because of its rigorous approach and transparent methodology, it helps to eliminate bias from the selection of literature. 

The following steps apply for all types of literature review.

Step 1. Define the problem

It’s important to establish a purpose for your literature review but the key is in finding the right balance – too narrow and you will have limited sources to review, too broad and the list will be endless. Some authors choose to confine their review to a specific time period. 

Step 2. Locate the key literature

It's important to be systematic - whether you follow a list of database references, or jump directly to the citations of a particular article, you need to keep records. These should not only be bibliographic (author, date, title of article/chapter, publication, volume and issue number, edition, etc.), but focus on the content too.

Keywords are a good search strategy and it helps if you are specific (don’t rely on general keywords and phrases). You can also search for key scholars in the field by name.

Too many results returned? Try filtering using the following methods:

• Metadata: Refine material by author, year of publication or geographic location. 
• Semantic: Remove words or terms that are spelled the same but differ in meaning.
• Evidence-grading: Apply a quality filter e.g. sift out non-peer reviewed, or opinion-based, rhetorical, and non-conclusive material. 
• Accessibility: Is the full-text article available or just the abstract?

You will want to search for relevant materials across a range of media. Possible sources include: 

• Books (monographs, text books, reference books)
• Journal articles
• Newspaper articles
• Historical records
• Commercial reports and statistical information
• Government reports and statistical information
• Theses and dissertations

If you are attached to a university, the library is probably the best place to start. You could also refer to other relevant library catalogues, such as the British Library catalogue, the National Union Catalogue (Library of Congress), and, through their URLs, other large academic libraries. Most libraries will also have indexes of periodicals, e.g. Business Periodicals Index, and abstracting services, e.g. Dissertation Abstracts.

While there are special circumstances for using old sources, for example in a historical study, or because the work is seminal, ideally you want to focus on the most recently published literature.

Step 3. Analyse the literature

When you are looking at your raw bibliographical data, there are some important points to consider:

• What are the author's credentials? Are they an expert in the field? Are they affiliated to a reputable organisation?
• What is the date of publication? Is it sufficiently current or will knowledge have moved on?
• If it’s a book, are you looking at the latest edition?
• Is the publisher a reputable, scholarly publisher?
• If it is a journal, has the content been peer reviewed?

As you move on to analysing the content, your questions change in tone. 

• Is the writer addressing a scholarly audience?
• Does the author review the relevant literature?
• Does the author write from an objective viewpoint, and are their views based on facts rather than opinions?
• If the author uses research, is the design sound?
• Is it primary or secondary material?
• What is the relationship of this work to other material on the same topic? Does it substantiate it or add a different perspective?
• Is the author's argument logically organised and clear to follow?
• If the author is writing from a practice-based perspective, what are the implications for practice?
• What themes emerge and what conclusions can be drawn?
• Are there any significant questions forming a basis for further investigation?

The Cornell University Library website contains some good pointers for evaluating material, including how to distinguish scholarly and non-scholarly publications.

Between the first and second stages, there should be a process of selection; not everything you read will go into your final literature review.

One useful way to find common strands and show up apparent contradictions is to create a table of your results with study references listed alongside a brief overview of findings. These could be:

• Statistical – results subjected to a set of statistical tests, i.e. meta-analysis. 
• Narrative – organised by theme, study type, etc.
• Conceptual – different concepts brought together and a new concept described.

Step 4. Structure your literature review

There are many ways to organise a literature review. Let’s take a closer look at one option:

Introduction: Define the topic, together with your reason for selecting the topic. You could also point out overall trends, gaps, particular themes that emerge, etc.

Body: this is where you discuss your sources. Here are some ways in which you could organise your discussion:

• Chronologically: For example, if writers' views have tended to change over time. There is little point in doing the review by order of publication unless this shows a clear trend.
• Thematically: Identify a series of themes.
• Research type: For example, academic versus practitioner.
• Dialectical: Contrast different views or theoretical debates.
• Methodologically: Here, the focus is on the methods of the researcher, for example, qualitative versus quantitative approaches.

As with any piece of writing, make sure that your structure is clear by explaining what you are going to do, and using appropriate headings.

Conclusion: Summarise the major contributions, evaluate the current position, and point out flaws in methodology, gaps in the research, contradictions, and areas for further study.

Writing simply

A manuscript or case study that is easy to follow will help readers absorb your key messages.

How to structure your journal submission

This guide explains the building blocks that are used to construct a journal article and why getting them right can boost your chances of publishing success. 

Proofreading

In this guide we explain what you should look for at the proofing stage.

• Resources Home 🏠
• Try SciSpace Copilot
• Search research papers
• Add Copilot Extension
• Try AI Detector
• Try Paraphraser
• Try Citation Generator
• April Papers
• June Papers
• July Papers

How To Write A Literature Review - A Complete Guide

Table of Contents

A literature review is much more than just another section in your research paper. It forms the very foundation of your research. It is a formal piece of writing where you analyze the existing theoretical framework, principles, and assumptions and use that as a base to shape your approach to the research question.

Curating and drafting a solid literature review section not only lends more credibility to your research paper but also makes your research tighter and better focused. But, writing literature reviews is a difficult task. It requires extensive reading, plus you have to consider market trends and technological and political changes, which tend to change in the blink of an eye.

Now streamline your literature review process with the help of SciSpace Copilot. With this AI research assistant, you can efficiently synthesize and analyze a vast amount of information, identify key themes and trends, and uncover gaps in the existing research. Get real-time explanations, summaries, and answers to your questions for the paper you're reviewing, making navigating and understanding the complex literature landscape easier.

In this comprehensive guide, we will explore everything from the definition of a literature review, its appropriate length, various types of literature reviews, and how to write one.

What is a literature review?

A literature review is a collation of survey, research, critical evaluation, and assessment of the existing literature in a preferred domain.

Eminent researcher and academic Arlene Fink, in her book Conducting Research Literature Reviews , defines it as the following:

“A literature review surveys books, scholarly articles, and any other sources relevant to a particular issue, area of research, or theory, and by so doing, provides a description, summary, and critical evaluation of these works in relation to the research problem being investigated.

Literature reviews are designed to provide an overview of sources you have explored while researching a particular topic, and to demonstrate to your readers how your research fits within a larger field of study.”

Simply put, a literature review can be defined as a critical discussion of relevant pre-existing research around your research question and carving out a definitive place for your study in the existing body of knowledge. Literature reviews can be presented in multiple ways: a section of an article, the whole research paper itself, or a chapter of your thesis.

A literature review does function as a summary of sources, but it also allows you to analyze further, interpret, and examine the stated theories, methods, viewpoints, and, of course, the gaps in the existing content.

As an author, you can discuss and interpret the research question and its various aspects and debate your adopted methods to support the claim.

What is the purpose of a literature review?

A literature review is meant to help your readers understand the relevance of your research question and where it fits within the existing body of knowledge. As a researcher, you should use it to set the context, build your argument, and establish the need for your study.

What is the importance of a literature review?

The literature review is a critical part of research papers because it helps you:

• Gain an in-depth understanding of your research question and the surrounding area
• Convey that you have a thorough understanding of your research area and are up-to-date with the latest changes and advancements
• Establish how your research is connected or builds on the existing body of knowledge and how it could contribute to further research
• Elaborate on the validity and suitability of your theoretical framework and research methodology
• Identify and highlight gaps and shortcomings in the existing body of knowledge and how things need to change
• Convey to readers how your study is different or how it contributes to the research area

How long should a literature review be?

Ideally, the literature review should take up 15%-40% of the total length of your manuscript. So, if you have a 10,000-word research paper, the minimum word count could be 1500.

Your literature review format depends heavily on the kind of manuscript you are writing — an entire chapter in case of doctoral theses, a part of the introductory section in a research article, to a full-fledged review article that examines the previously published research on a topic.

Another determining factor is the type of research you are doing. The literature review section tends to be longer for secondary research projects than primary research projects.

What are the different types of literature reviews?

All literature reviews are not the same. There are a variety of possible approaches that you can take. It all depends on the type of research you are pursuing.

Here are the different types of literature reviews:

Argumentative review

It is called an argumentative review when you carefully present literature that only supports or counters a specific argument or premise to establish a viewpoint.

Integrative review

It is a type of literature review focused on building a comprehensive understanding of a topic by combining available theoretical frameworks and empirical evidence.

Methodological review

This approach delves into the ''how'' and the ''what" of the research question —  you cannot look at the outcome in isolation; you should also review the methodology used.

Systematic review

This form consists of an overview of existing evidence pertinent to a clearly formulated research question, which uses pre-specified and standardized methods to identify and critically appraise relevant research and collect, report, and analyze data from the studies included in the review.

Meta-analysis review

Meta-analysis uses statistical methods to summarize the results of independent studies. By combining information from all relevant studies, meta-analysis can provide more precise estimates of the effects than those derived from the individual studies included within a review.

Historical review

Historical literature reviews focus on examining research throughout a period, often starting with the first time an issue, concept, theory, or phenomenon emerged in the literature, then tracing its evolution within the scholarship of a discipline. The purpose is to place research in a historical context to show familiarity with state-of-the-art developments and identify future research's likely directions.

Theoretical Review

This form aims to examine the corpus of theory accumulated regarding an issue, concept, theory, and phenomenon. The theoretical literature review helps to establish what theories exist, the relationships between them, the degree the existing approaches have been investigated, and to develop new hypotheses to be tested.

Scoping Review

The Scoping Review is often used at the beginning of an article, dissertation, or research proposal. It is conducted before the research to highlight gaps in the existing body of knowledge and explains why the project should be greenlit.

State-of-the-Art Review

The State-of-the-Art review is conducted periodically, focusing on the most recent research. It describes what is currently known, understood, or agreed upon regarding the research topic and highlights where there are still disagreements.

Can you use the first person in a literature review?

When writing literature reviews, you should avoid the usage of first-person pronouns. It means that instead of "I argue that" or "we argue that," the appropriate expression would be "this research paper argues that."

Do you need an abstract for a literature review?

Ideally, yes. It is always good to have a condensed summary that is self-contained and independent of the rest of your review. As for how to draft one, you can follow the same fundamental idea when preparing an abstract for a literature review. It should also include:

• The research topic and your motivation behind selecting it
• A one-sentence thesis statement
• An explanation of the kinds of literature featured in the review
• Summary of what you've learned
• Conclusions you drew from the literature you reviewed
• Potential implications and future scope for research

Here's an example of the abstract of a literature review

Is a literature review written in the past tense?

Yes, the literature review should ideally be written in the past tense. You should not use the present or future tense when writing one. The exceptions are when you have statements describing events that happened earlier than the literature you are reviewing or events that are currently occurring; then, you can use the past perfect or present perfect tenses.

How many sources for a literature review?

There are multiple approaches to deciding how many sources to include in a literature review section. The first approach would be to look level you are at as a researcher. For instance, a doctoral thesis might need 60+ sources. In contrast, you might only need to refer to 5-15 sources at the undergraduate level.

The second approach is based on the kind of literature review you are doing — whether it is merely a chapter of your paper or if it is a self-contained paper in itself. When it is just a chapter, sources should equal the total number of pages in your article's body. In the second scenario, you need at least three times as many sources as there are pages in your work.

Quick tips on how to write a literature review

To know how to write a literature review, you must clearly understand its impact and role in establishing your work as substantive research material.

You need to follow the below-mentioned steps, to write a literature review:

• Outline the purpose behind the literature review
• Search relevant literature
• Examine and assess the relevant resources
• Discover connections by drawing deep insights from the resources
• Structure planning to write a good literature review

1. Outline and identify the purpose of  a literature review

As a first step on how to write a literature review, you must know what the research question or topic is and what shape you want your literature review to take. Ensure you understand the research topic inside out, or else seek clarifications. You must be able to the answer below questions before you start:

• How many sources do I need to include?
• What kind of sources should I analyze?
• How much should I critically evaluate each source?
• Should I summarize, synthesize or offer a critique of the sources?
• Do I need to include any background information or definitions?

Additionally, you should know that the narrower your research topic is, the swifter it will be for you to restrict the number of sources to be analyzed.

2. Search relevant literature

Dig deeper into search engines to discover what has already been published around your chosen topic. Make sure you thoroughly go through appropriate reference sources like books, reports, journal articles, government docs, and web-based resources.

You must prepare a list of keywords and their different variations. You can start your search from any library’s catalog, provided you are an active member of that institution. The exact keywords can be extended to widen your research over other databases and academic search engines like:

• Google Scholar
• Microsoft Academic
• Science.gov

Besides, it is not advisable to go through every resource word by word. Alternatively, what you can do is you can start by reading the abstract and then decide whether that source is relevant to your research or not.

Additionally, you must spend surplus time assessing the quality and relevance of resources. It would help if you tried preparing a list of citations to ensure that there lies no repetition of authors, publications, or articles in the literature review.

3. Examine and assess the sources

It is nearly impossible for you to go through every detail in the research article. So rather than trying to fetch every detail, you have to analyze and decide which research sources resemble closest and appear relevant to your chosen domain.

While analyzing the sources, you should look to find out answers to questions like:

• What question or problem has the author been describing and debating?
• What is the definition of critical aspects?
• How well the theories, approach, and methodology have been explained?
• Whether the research theory used some conventional or new innovative approach?
• How relevant are the key findings of the work?
• In what ways does it relate to other sources on the same topic?
• What challenges does this research paper pose to the existing theory
• What are the possible contributions or benefits it adds to the subject domain?

Be always mindful that you refer only to credible and authentic resources. It would be best if you always take references from different publications to validate your theory.

Always keep track of important information or data you can present in your literature review right from the beginning. It will help steer your path from any threats of plagiarism and also make it easier to curate an annotated bibliography or reference section.

4. Discover connections

At this stage, you must start deciding on the argument and structure of your literature review. To accomplish this, you must discover and identify the relations and connections between various resources while drafting your abstract.

A few aspects that you should be aware of while writing a literature review include:

• Rise to prominence: Theories and methods that have gained reputation and supporters over time.
• Constant scrutiny: Concepts or theories that repeatedly went under examination.
• Contradictions and conflicts: Theories, both the supporting and the contradictory ones, for the research topic.
• Knowledge gaps: What exactly does it fail to address, and how to bridge them with further research?
• Influential resources: Significant research projects available that have been upheld as milestones or perhaps, something that can modify the current trends

Once you join the dots between various past research works, it will be easier for you to draw a conclusion and identify your contribution to the existing knowledge base.

5. Structure planning to write a good literature review

There exist different ways towards planning and executing the structure of a literature review. The format of a literature review varies and depends upon the length of the research.

Like any other research paper, the literature review format must contain three sections: introduction, body, and conclusion. The goals and objectives of the research question determine what goes inside these three sections.

Nevertheless, a good literature review can be structured according to the chronological, thematic, methodological, or theoretical framework approach.

Literature review samples

1. Standalone

2. As a section of a research paper

How SciSpace Discover makes literature review a breeze?

SciSpace Discover is a one-stop solution to do an effective literature search and get barrier-free access to scientific knowledge. It is an excellent repository where you can find millions of only peer-reviewed articles and full-text PDF files. Here’s more on how you can use it:

Find the right information

Find what you want quickly and easily with comprehensive search filters that let you narrow down papers according to PDF availability, year of publishing, document type, and affiliated institution. Moreover, you can sort the results based on the publishing date, citation count, and relevance.

Assess credibility of papers quickly

When doing the literature review, it is critical to establish the quality of your sources. They form the foundation of your research. SciSpace Discover helps you assess the quality of a source by providing an overview of its references, citations, and performance metrics.

Get the complete picture in no time

SciSpace Discover’s personalized suggestion engine helps you stay on course and get the complete picture of the topic from one place. Every time you visit an article page, it provides you links to related papers. Besides that, it helps you understand what’s trending, who are the top authors, and who are the leading publishers on a topic.

Make referring sources super easy

To ensure you don't lose track of your sources, you must start noting down your references when doing the literature review. SciSpace Discover makes this step effortless. Click the 'cite' button on an article page, and you will receive preloaded citation text in multiple styles — all you've to do is copy-paste it into your manuscript.

Final tips on how to write a literature review

A massive chunk of time and effort is required to write a good literature review. But, if you go about it systematically, you'll be able to save a ton of time and build a solid foundation for your research.

We hope this guide has helped you answer several key questions you have about writing literature reviews.

Would you like to explore SciSpace Discover and kick off your literature search right away? You can get started here .

Frequently Asked Questions (FAQs)

1. how to start a literature review.

• What questions do you want to answer?

• What sources do you need to answer these questions?

• What information do these sources contain?

• How can you use this information to answer your questions?

2. What to include in a literature review?

• A brief background of the problem or issue

• What has previously been done to address the problem or issue

• A description of what you will do in your project

• How this study will contribute to research on the subject

3. Why literature review is important?

The literature review is an important part of any research project because it allows the writer to look at previous studies on a topic and determine existing gaps in the literature, as well as what has already been done. It will also help them to choose the most appropriate method for their own study.

4. How to cite a literature review in APA format?

To cite a literature review in APA style, you need to provide the author's name, the title of the article, and the year of publication. For example: Patel, A. B., & Stokes, G. S. (2012). The relationship between personality and intelligence: A meta-analysis of longitudinal research. Personality and Individual Differences, 53(1), 16-21

5. What are the components of a literature review?

• A brief introduction to the topic, including its background and context. The introduction should also include a rationale for why the study is being conducted and what it will accomplish.

• A description of the methodologies used in the study. This can include information about data collection methods, sample size, and statistical analyses.

• A presentation of the findings in an organized format that helps readers follow along with the author's conclusions.

6. What are common errors in writing literature review?

• Not spending enough time to critically evaluate the relevance of resources, observations and conclusions.

• Totally relying on secondary data while ignoring primary data.

• Letting your personal bias seep into your interpretation of existing literature.

• No detailed explanation of the procedure to discover and identify an appropriate literature review.

7. What are the 5 C's of writing literature review?

• Cite - the sources you utilized and referenced in your research.

• Compare - existing arguments, hypotheses, methodologies, and conclusions found in the knowledge base.

• Contrast - the arguments, topics, methodologies, approaches, and disputes that may be found in the literature.

• Critique - the literature and describe the ideas and opinions you find more convincing and why.

• Connect - the various studies you reviewed in your research.

8. How many sources should a literature review have?

When it is just a chapter, sources should equal the total number of pages in your article's body. if it is a self-contained paper in itself, you need at least three times as many sources as there are pages in your work.

9. Can literature review have diagrams?

• To represent an abstract idea or concept

• To explain the steps of a process or procedure

• To help readers understand the relationships between different concepts

10. How old should sources be in a literature review?

Sources for a literature review should be as current as possible or not older than ten years. The only exception to this rule is if you are reviewing a historical topic and need to use older sources.

11. What are the types of literature review?

• Argumentative review

• Integrative review

• Methodological review

• Systematic review

• Meta-analysis review

• Historical review

• Theoretical review

• Scoping review

• State-of-the-Art review

12. Is a literature review mandatory?

Yes. Literature review is a mandatory part of any research project. It is a critical step in the process that allows you to establish the scope of your research, and provide a background for the rest of your work.

But before you go,

• Six Online Tools for Easy Literature Review
• Evaluating literature review: systematic vs. scoping reviews
• Systematic Approaches to a Successful Literature Review
• Writing Integrative Literature Reviews: Guidelines and Examples

You might also like

Consensus GPT vs. SciSpace GPT: Choose the Best GPT for Research

Literature Review and Theoretical Framework: Understanding the Differences

Types of Essays in Academic Writing - Quick Guide (2024)

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

The PMC website is updating on October 15, 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Perspect Med Educ
• v.7(1); 2018 Feb

Writing an effective literature review

Lorelei lingard.

Schulich School of Medicine & Dentistry, Health Sciences Addition, Western University, London, Ontario Canada

In the Writer’s Craft section we offer simple tips to improve your writing in one of three areas: Energy, Clarity and Persuasiveness. Each entry focuses on a key writing feature or strategy, illustrates how it commonly goes wrong, teaches the grammatical underpinnings necessary to understand it and offers suggestions to wield it effectively. We encourage readers to share comments on or suggestions for this section on Twitter, using the hashtag: #how’syourwriting?

This Writer’s Craft instalment is the first in a two-part series that offers strategies for effectively presenting the literature review section of a research manuscript. This piece alerts writers to the importance of not only summarizing what is known but also identifying precisely what is not, in order to explicitly signal the relevance of their research. In this instalment, I will introduce readers to the mapping the gap metaphor, the knowledge claims heuristic, and the need to characterize the gap.

Mapping the gap

The purpose of the literature review section of a manuscript is not to report what is known about your topic. The purpose is to identify what remains unknown— what academic writing scholar Janet Giltrow has called the ‘knowledge deficit’ — thus establishing the need for your research study [ 1 ]. In an earlier Writer’s Craft instalment, the Problem-Gap-Hook heuristic was introduced as a way of opening your paper with a clear statement of the problem that your work grapples with, the gap in our current knowledge about that problem, and the reason the gap matters [ 2 ]. This article explains how to use the literature review section of your paper to build and characterize the Gap claim in your Problem-Gap-Hook. The metaphor of ‘mapping the gap’ is a way of thinking about how to select and arrange your review of the existing literature so that readers can recognize why your research needed to be done, and why its results constitute a meaningful advance on what was already known about the topic.

Many writers have learned that the literature review should describe what is known. The trouble with this approach is that it can produce a laundry list of facts-in-the-world that does not persuade the reader that the current study is a necessary next step. Instead, think of your literature review as painting in a map of your research domain: as you review existing knowledge, you are painting in sections of the map, but your goal is not to end with the whole map fully painted. That would mean there is nothing more we need to know about the topic, and that leaves no room for your research. What you want to end up with is a map in which painted sections surround and emphasize a white space, a gap in what is known that matters. Conceptualizing your literature review this way helps to ensure that it achieves its dual goal: of presenting what is known and pointing out what is not—the latter of these goals is necessary for your literature review to establish the necessity and importance of the research you are about to describe in the methods section which will immediately follow the literature review.

To a novice researcher or graduate student, this may seem counterintuitive. Hopefully you have invested significant time in reading the existing literature, and you are understandably keen to demonstrate that you’ve read everything ever published about your topic! Be careful, though, not to use the literature review section to regurgitate all of your reading in manuscript form. For one thing, it creates a laundry list of facts that makes for horrible reading. But there are three other reasons for avoiding this approach. First, you don’t have the space. In published medical education research papers, the literature review is quite short, ranging from a few paragraphs to a few pages, so you can’t summarize everything you’ve read. Second, you’re preaching to the converted. If you approach your paper as a contribution to an ongoing scholarly conversation,[ 2 ] then your literature review should summarize just the aspects of that conversation that are required to situate your conversational turn as informed and relevant. Third, the key to relevance is to point to a gap in what is known. To do so, you summarize what is known for the express purpose of identifying what is not known . Seen this way, the literature review should exert a gravitational pull on the reader, leading them inexorably to the white space on the map of knowledge you’ve painted for them. That white space is the space that your research fills.

Knowledge claims

To help writers move beyond the laundry list, the notion of ‘knowledge claims’ can be useful. A knowledge claim is a way of presenting the growing understanding of the community of researchers who have been exploring your topic. These are not disembodied facts, but rather incremental insights that some in the field may agree with and some may not, depending on their different methodological and disciplinary approaches to the topic. Treating the literature review as a story of the knowledge claims being made by researchers in the field can help writers with one of the most sophisticated aspects of a literature review—locating the knowledge being reviewed. Where does it come from? What is debated? How do different methodologies influence the knowledge being accumulated? And so on.

Consider this example of the knowledge claims (KC), Gap and Hook for the literature review section of a research paper on distributed healthcare teamwork:

KC: We know that poor team communication can cause errors. KC: And we know that team training can be effective in improving team communication. KC: This knowledge has prompted a push to incorporate teamwork training principles into health professions education curricula. KC: However, most of what we know about team training research has come from research with co-located teams—i. e., teams whose members work together in time and space. Gap: Little is known about how teamwork training principles would apply in distributed teams, whose members work asynchronously and are spread across different locations. Hook: Given that much healthcare teamwork is distributed rather than co-located, our curricula will be severely lacking until we create refined teamwork training principles that reflect distributed as well as co-located work contexts.

The ‘We know that …’ structure illustrated in this example is a template for helping you draft and organize. In your final version, your knowledge claims will be expressed with more sophistication. For instance, ‘We know that poor team communication can cause errors’ will become something like ‘Over a decade of patient safety research has demonstrated that poor team communication is the dominant cause of medical errors.’ This simple template of knowledge claims, though, provides an outline for the paragraphs in your literature review, each of which will provide detailed evidence to illustrate a knowledge claim. Using this approach, the order of the paragraphs in the literature review is strategic and persuasive, leading the reader to the gap claim that positions the relevance of the current study. To expand your vocabulary for creating such knowledge claims, linking them logically and positioning yourself amid them, I highly recommend Graff and Birkenstein’s little handbook of ‘templates’ [ 3 ].

As you organize your knowledge claims, you will also want to consider whether you are trying to map the gap in a well-studied field, or a relatively understudied one. The rhetorical challenge is different in each case. In a well-studied field, like professionalism in medical education, you must make a strong, explicit case for the existence of a gap. Readers may come to your paper tired of hearing about this topic and tempted to think we can’t possibly need more knowledge about it. Listing the knowledge claims can help you organize them most effectively and determine which pieces of knowledge may be unnecessary to map the white space your research attempts to fill. This does not mean that you leave out relevant information: your literature review must still be accurate. But, since you will not be able to include everything, selecting carefully among the possible knowledge claims is essential to producing a coherent, well-argued literature review.

Characterizing the gap

Once you’ve identified the gap, your literature review must characterize it. What kind of gap have you found? There are many ways to characterize a gap, but some of the more common include:

• a pure knowledge deficit—‘no one has looked at the relationship between longitudinal integrated clerkships and medical student abuse’
• a shortcoming in the scholarship, often due to philosophical or methodological tendencies and oversights—‘scholars have interpreted x from a cognitivist perspective, but ignored the humanist perspective’ or ‘to date, we have surveyed the frequency of medical errors committed by residents, but we have not explored their subjective experience of such errors’
• a controversy—‘scholars disagree on the definition of professionalism in medicine …’
• a pervasive and unproven assumption—‘the theme of technological heroism—technology will solve what ails teamwork—is ubiquitous in the literature, but what is that belief based on?’

To characterize the kind of gap, you need to know the literature thoroughly. That means more than understanding each paper individually; you also need to be placing each paper in relation to others. This may require changing your note-taking technique while you’re reading; take notes on what each paper contributes to knowledge, but also on how it relates to other papers you’ve read, and what it suggests about the kind of gap that is emerging.

In summary, think of your literature review as mapping the gap rather than simply summarizing the known. And pay attention to characterizing the kind of gap you’ve mapped. This strategy can help to make your literature review into a compelling argument rather than a list of facts. It can remind you of the danger of describing so fully what is known that the reader is left with the sense that there is no pressing need to know more. And it can help you to establish a coherence between the kind of gap you’ve identified and the study methodology you will use to fill it.

Acknowledgements

Thanks to Mark Goldszmidt for his feedback on an early version of this manuscript.

PhD, is director of the Centre for Education Research & Innovation at Schulich School of Medicine & Dentistry, and professor for the Department of Medicine at Western University in London, Ontario, Canada.

• Research Guides
• Vanderbilt University Libraries

Peabody Library

Literature reviews.

• Publishing in Academic Journals
• Steps to Success: The Literature Review Process
• Literature Reviews Webinar Recording
• Literature Review Table
• Writing Like an Academic
• Managing Citations This link opens in a new window

Calls for Papers

• H-Net CFPs in the social sciences and humanities.
• CFPS from AERA Compiled by the American Educational Research Association.

Why Publish?

As a grad/professional student, you will write (or already have written!) a LOT of papers. Rather than tossing that paper aside after you've received your grade, consider turning it into a manuscript submission. There are many benefits for publishing as a grad student:

• You've already done a lot of the work! You may just need to update your research a bit, and re-format your paper for the journal you're submitting to (more on that below).
• It's a great CV booster. Whether you're looking for a job, applying to another graduate program, or just looking for an internship, having a published or accepted publication on your resume looks great.
• Publishing is also a great way to grow your professional network. While the peer review process means you most likely won't know the identify of your reviewers, journal editors are often available to discuss why your article might be a good fit for their journal. Not only are they providing valuable feedback on your writing, you now have a new professional contact!

Things To Think About

• Where Should You Publish?
• What is Impact Factor?
• Crafting Your Submission
• Copyright for Authors

Things to consider:

• Reputation - The reputation of the publisher, journal, editor and editorial board can give an indication of the quality of the journal.
• Scope and focus of the journal - It is important that your article reaches the readers who can most benefit from it and who can most benefit you. The scope and aim of the journal will give an indication of who the journal’s readers are, e.g. national or international, limited to a select area of research or with a multidisciplinary focus.
• Turnaround time - What is the length of the review process? Average length of time from submission to acceptance or rejection; from acceptance to publication? Frequency of publication?
• Included in prominent indexes - Are articles from the journal indexed in journal databases relevant to your field, or in citation databases such as Scopus or Web of Science?
• Editorial standards / Journal information - The competence of a journal’s editorial office can hugely influence the success or failure of an article. Make sure that the “Instructions to Authors” are easily accessible and that they set out clearly what is expected from authors. Does the journal come out on time or is it often two or three years behind? Is the journal carefully produced with a professional appearance, or does it have many typing errors, poor paper quality and other signs of neglect? Does the journal accept electronic submissions? This simplifies the submission process, allows swift management of manuscripts and makes it possible for authors to track the position of their manuscripts in the review process.
• Acceptance rate - The acceptance rate gives an indication of how competitive a journal is. Journals with a low acceptance rate are considered to be amongst the most prestigious in their field, the assumption being that only the very best articles are selected.
• Cost - Be aware that some journals charge either a submission fee, an acceptance fee, page fees or fees for use of colour images or other special media formats.
• Rights for authors - Check the journal website or their copyright form for information on author rights. Are you allowed to re-use the article after publication or to submit the post-print to the University’s research repository?
• Type of publication - Some journals only accept certain types of articles for publication.

More resources:

ISSN, publisher, language, subject, abstracting & indexing coverage, full-text database coverage, tables of contents, and reviews written by librarians on periodicals of all types: academic and scholarly journals, e-journals, peer-reviewed titles, popular magazines, newspapers, newsletters, etc.  

Comprehensive coverage of all open access scientific and scholarly journals that use a quality control system to guarantee the content.

• Cabell's directory of publishing opportunities in educational psychology and administration Call Number: Peabody Reference Z286 .E3 C323
• Vanderbilt University Institutional Repository Vanderbilt's Institutional Repository. This is a great way to gain a wider audience for your work.

A  journal's  Impact Factor  ind icates the average number of times articles from the publication have been cited over a two-year period.  The higher the journal's impact factor, the more prominent the journal is in the canon of literature.

• Web of Knowledge Journal Citation Reports The official entity providing a journal's impact factor.
• CiteFactor A repository of open access journals, including publication information about journals, such as impact factor.
• Altmetrics Altmetrics considers how scholarship-based activities such as blog posts, tweets, downloads, shares, and views may provide important information about the significance of a research article or other research "product." Find more information on this research guide.

Author guidelines of most journals can be found on the journal website, which can be found via Google or by using Ulrich's.

Make sure to follow the guidelines closely and prepare your submission in accordance with these guidelines. Things like word count, format of the manuscript (text, illustrations, etc), and citation style are all very important.

Make sure to also check if the journal accepts submissions on a rolling basis, or if you need to inquire with the editor first.

• Journal of scholarly publishing Provides practical advice for authors, editors and publishers for a wide range of topics.
• Tips from Editors on Getting Published

• More information on Copyright Videos include: What are Copyright and Using Others' Work
• Authors' Rights Find out more about your rights as an artist, author, or creator of any sort
• Open Access Publishing Guide from Vanderbilt Library
• << Previous: Writing Like an Academic
• Next: Managing Citations >>
• Last Updated: Sep 27, 2024 2:53 PM
• URL: https://researchguides.library.vanderbilt.edu/peabody/litreviews

Our systems are now restored following recent technical disruption, and we’re working hard to catch up on publishing. We apologise for the inconvenience caused. Find out more: https://www.cambridge.org/universitypress/about-us/news-and-blogs/cambridge-university-press-publishing-update-following-technical-disruption

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings .

Login Alert

• > Guide to Publishing in Psychology Journals
• > Writing a Literature Review

Book contents

• Frontmatter
• PART ONE INTRODUCTION
• 1 Writing an Empirical Article
• 2 Writing a Literature Review
• PART TWO PARTS OF AN ARTICLE
• PART THREE DEALING WITH REFEREES
• PART FOUR CONCLUSION

2 - Writing a Literature Review

Published online by Cambridge University Press:  05 February 2012

Writing a literature review requires a somewhat different set of skills than writing an empirical research article. Indeed, some people who are very good at writing empirical research reports are not skilled at composing review papers. What are the characteristics that differentiate literature reviews that are likely to be published and make a difference from those that are difficult to publish and make a limited contribution?

I have been thinking about this topic quite a lot recently. As the current editor of Psychological Bulletin , the literature review journal of the American Psychological Association, I constantly deal with the issue of evaluating review papers. I had written a number of literature reviews myself prior to becoming editor; however, in the process of editing the journal I have had to consolidate what were vague, sometimes unverbalized cognitions regarding the properties of an excellent review into criteria for guiding editorial decisions.

Before writing this chapter, I started to outline my recommendations. As a last step before beginning to write, I read a similar paper written by Daryl Bem that was published in Psychological Bulletin in 1995. I was surprised at how similar Bem's and my ideas were; sometimes he even used the same words that I have used when talking about writing reviews to groups at conferences and to my students. Based on this similarity, one could conclude either that great minds think alike or that there is considerable interrater reliability between people who have been editors about what constitutes a high-quality review paper.

Access options

Save book to kindle.

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle .

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service .

• Writing a Literature Review
• By Nancy Eisenberg
• Edited by Robert J. Sternberg , Yale University, Connecticut
• Book: Guide to Publishing in Psychology Journals
• Online publication: 05 February 2012
• Chapter DOI: https://doi.org/10.1017/CBO9780511807862.003

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox .

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive .

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

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

Table of Contents

What is the purpose of literature review , a. habitat loss and species extinction: , b. range shifts and phenological changes: , c. ocean acidification and coral reefs: , d. adaptive strategies and conservation efforts: .

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

Frequently asked questions 

What is a literature review .

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

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

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

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

Find academic papers related to your research topic faster. Try Research on Paperpal

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

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

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

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

Literature review example 

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

Literature Review on Climate Change Impacts on Biodiversity:  

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

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

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

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

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

Strengthen your literature review with factual insights. Try Research on Paperpal for free!

How to write a good literature review 

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

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

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

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

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

Write and Cite as yo u go with Paperpal Research. Start now for free!

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

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

Conducting a literature review 

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

Choose a Topic and Define the Research Question:  

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

Decide on the Scope of Your Review:  

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

Select Databases for Searches:  

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

Conduct Searches and Keep Track:  

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

Review the Literature:  

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

Organize and Write Your Literature Review:  

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

Whether you’re exploring a new research field or finding new angles to develop an existing topic, sifting through hundreds of papers can take more time than you have to spare. But what if you could find science-backed insights with verified citations in seconds? That’s the power of Paperpal’s new Research feature!  

How to write a literature review faster with Paperpal?  

Paperpal, an AI writing assistant, integrates powerful academic search capabilities within its writing platform. With the Research | Cite feature, you get 100% factual insights, with citations backed by 250M+ verified research articles, directly within your writing interface. It also allows you auto-cite references in 10,000+ styles and save relevant references in your Citation Library. By eliminating the need to switch tabs to find answers to all your research questions, Paperpal saves time and helps you stay focused on your writing.   

Here’s how to use the Research feature:  

• Ask a question: Get started with a new document on paperpal.com. Click on the “Research | Cite” feature and type your question in plain English. Paperpal will scour over 250 million research articles, including conference papers and preprints, to provide you with accurate insights and citations. 

• Review and Save: Paperpal summarizes the information, while citing sources and listing relevant reads. You can quickly scan the results to identify relevant references and save these directly to your built-in citations library for later access. 
• Cite with Confidence: Paperpal makes it easy to incorporate relevant citations and references in 10,000+ styles into your writing, ensuring your arguments are well-supported by credible sources. This translates to a polished, well-researched literature review. 

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

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

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

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

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

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

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

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

References 

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

Paperpal is a comprehensive AI writing toolkit that helps students and researchers achieve 2x the writing in half the time. It leverages 22+ years of STM experience and insights from millions of research articles to provide in-depth academic writing, language editing, and submission readiness support to help you write better, faster.  

Get accurate academic translations, rewriting support, grammar checks, vocabulary suggestions, and generative AI assistance that delivers human precision at machine speed. Try for free or upgrade to Paperpal Prime starting at US$19 a month to access premium features, including consistency, plagiarism, and 30+ submission readiness checks to help you succeed.  

Experience the future of academic writing – Sign up to Paperpal and start writing for free!  

Related Reads:

• Empirical Research: A Comprehensive Guide for Academics 
• How to Write a Scientific Paper in 10 Steps 
• How Long Should a Chapter Be?
• How to Use Paperpal to Generate Emails & Cover Letters?

6 Tips for Post-Doc Researchers to Take Their Career to the Next Level

Self-plagiarism in research: what it is and how to avoid it, you may also like, machine translation vs human translation: which is reliable..., what is academic integrity, and why is it..., how to make a graphical abstract, academic integrity vs academic dishonesty: types & examples, dissertation printing and binding | types & comparison , what is a dissertation preface definition and examples , the ai revolution: authors’ role in upholding academic..., the future of academia: how ai tools are..., how to write a research proposal: (with examples..., how to write your research paper in apa....

Do you really want to publish your literature review? Advice for PhD students

Why publishing your literature review as your first paper may not be a good idea

Tatiana Andreeva - Sun 20 Jun 2021 08:20 (updated Wed 30 Aug 2023 10:03)

[Guest post by CYGNA member Tatiana Andreeva ]

Almost every PhD student I met had an idea that the literature review paper would be the first academic paper they publish. They thought of it being the first paper for two reasons - naturally literature review was the first stage of their PhD journey, but also they thought it was something relatively straightforward to do. To reinforce these ideas, in some PhD programmes I know publication of the literature review is routinely put as a milestone in the PhD progression plans.

At the same time, if you talk to academics who actually tried to publish a literature review, you would most often hear that it is a very challenging thing to do. Moreover, I recently realized that we rarely teach our graduate students how to do a literature review , let alone how to publish it . A weird mismatch, isn’t it? So, dear PhD students, I’d like to put some clarity around it for you. There are two key reasons why publishing literature review as your first paper may not be a good idea.

Not all literature reviews are made equal

First, the literature review you do as a first step of your PhD journey and publishable literature review are two different beasts: they have a different purpose, focus and audience.

The literature review you do as a first step of your PhD aims to inform you as a novice about existing literature and to help you identify an interesting research question or situate it better in the existing research landscape. You are likely to read different literatures and/or focus on different aspects, as you are trying to find your own research voice and space. As your PhD progresses and you get new ideas or unexpected empirical findings, you are likely to review the literature again (and again…)

Even if you do this literature review(s) following the best standards , it is very likely that parts of it will never be published – neither as a separate article, nor even as a literature review section of an empirical paper. Not because they are bad, but because they may end up being not so relevant for the final focus of your PhD. I know it is heartbreaking to discard pieces of work, especially our own writing, but if you think of them as steppingstones rather than final products, it becomes easier.  

In contrast, the literature review that is done for publication aims to inform others - many of whom are likely to be experts in the field - about something beyond existing literature and to propose future research agenda for them (and maybe for you as well, but it is not the main goal). Therefore, it needs a clear and single focus - on a specific research problem within a specific body of literature. And, if all goes well, it should be published – at least, that is the plan.

The table below briefly summarizes these ideas:

Easy publication of literature reviews is a myth

Another reason why I think that planning to publish a literature review as a first paper in the suite of PhD publications is not a good idea is: the notion that such papers are easy to publish is a myth! I think it is actually even more difficult to publish a literature review than an empirical paper.

In an empirical paper, you always have an element of uniqueness, which is your empirical data. Indeed, nobody has collected something like this so it is unique. Sometimes when your data is interesting, it could happen that reviewers come back to you and say: " you need to improve your theory and develop a much stronger positioning of the paper, but your data itself is very interesting, so we give you a chance for R&R ".

In my experience, this would never happen with a literature review paper – because your data is not unique, it is something that has been already written and published. Everybody, if they want, can access it. So with the literature reviews is really becomes critical that from the very start you have a very clear and strong idea of what is the problem that hasn’t been solved that your literature review solves, and what would be your theoretical contribution. This is a challenging task for everyone, not only for a PhD student, so it might be too risky to start from it your publication journey.

All that said, it does not mean that you cannot - or should not - do a literature review publication. Indeed, at some point it may stem from the literature review you did for your PhD. I hope that understanding the differences between these beasts may help you to master both – and plan your PhD publication portfolio better.

Related blogposts

• Resources on doing a literature review
• Want to publish a literature review? Think of it as an empirical paper
• How to keep up-to-date with the literature, but avoid information overload?
• Is a literature review publication a low-cost project?
• Using Publish or Perish to do a literature review
• How to conduct a longitudinal literature review?
• New: Publish or Perish now also exports abstracts
• A framework for your literature review article: where to find one?

Find the resources on my website useful?

I cover all the expenses of operating my website privately. If you enjoyed this post and want to support me in maintaining my website, consider buying a copy of one of my books (see below) or  supporting the Publish or Perish software .

Copyright © 2023 Tatiana Andreeva . All rights reserved. Page last modified on Wed 30 Aug 2023 10:03

Tatiana Andreeva's profile and contact details >>

The use of multi-task learning in cybersecurity applications: a systematic literature review

• Open access
• Published: 27 September 2024

Cite this article

You have full access to this open access article

• Shimaa Ibrahim 1 ,
• Cagatay Catal   ORCID: orcid.org/0000-0003-0959-2930 1 &
• Thabet Kacem   ORCID: orcid.org/0000-0003-3656-9525 2  

Cybersecurity is crucial in today’s interconnected world, as digital technologies are increasingly used in various sectors. The risk of cyberattacks targeting financial, military, and political systems has increased due to the wide use of technology. Cybersecurity has become vital in information technology, with data protection being a major priority. Despite government and corporate efforts, cybersecurity remains a significant concern. The application of multi-task learning (MTL) in cybersecurity is a promising solution, allowing security systems to simultaneously address various tasks and adapt in real-time to emerging threats. While researchers have applied MTL techniques for different purposes, a systematic overview of the state-of-the-art on the role of MTL in cybersecurity is lacking. Therefore, we carried out a systematic literature review (SLR) on the use of MTL in cybersecurity applications and explored its potential applications and effectiveness in developing security measures. Five critical applications, such as network intrusion detection and malware detection, were identified, and several tasks used in these applications were observed. Most of the studies used supervised learning algorithms, and there were very limited studies that focused on other types of machine learning. This paper outlines various models utilized in the context of multi-task learning within cybersecurity and presents several challenges in this field.

Explore related subjects

• Artificial Intelligence

Avoid common mistakes on your manuscript.

1 Introduction

In today’s digital age, the significance of cybersecurity cannot be overstated. It serves as a critical defense for digital systems, networks, and data, protecting them against unauthorized access, theft, or corruption. The rapid growth of digital technology has made cybersecurity more crucial than ever, as cyber threats can disrupt organizations, starting with personal data breaches to interference with financial transactions, leading to significant financial losses and reputational damage. In the last 5 years, the FBI’s IC3 (Internet Crime Complaint Center) has been consistently registering an annual average of 652,000 complaints. Since 2018, the total number of complaints has reached 3.26 million, resulting in losses amounting to $27.6 billion [ 1 ]. Prioritizing cybersecurity is essential for both individuals and organizations to mitigate these threats.

Cybersecurity covers an extensive variety of procedures, methods, and technologies that collaborate to defend against attacks on networks, software, and data’s availability, confidentiality, and integrity. It involves the development of robust security protocols, sophisticated encryption models, and proactive countermeasures. Cyber defense mechanisms operate across hosts, networks, applications, and data. Multiple solutions are available, working side by side to prevent threats and identify security breaches. These include firewalls, anti-virus solutions, intrusion detection systems (IDSs), and intrusion protection systems (IPSs) [ 2 ]. A race has persisted between cybercriminals and defenders ever since the discovery of the first computer virus in 1970 [ 3 ]. The battle against cybersecurity threats and the challenge of keeping pace with their increasing speed have become demanding overtime.

Recently, cybersecurity experts have become more interested in artificial intelligence (AI) because it can effectively analyze and organize considerable amounts of internet traffic data [ 4 ]. According to estimations, the global market size for AI in the cybersecurity sector reached USD 14.9 billion in 2021, with a projected market value of USD 133.8 billion by 2030 [ 5 ]. AI and machine learning (ML) techniques are being incredibly integrated into the domain of cybersecurity [ 6 , 7 , 8 ]. ML is a subset of AI that employs computer programs to learn from historical data for modeling, control, or prediction. It includes reinforcement learning, supervised learning, unsupervised learning and semisupervised learning [ 9 ]. Deep learning (DL) relies on the utilization of multiple layers (e.g., convolutional layer and batch normalization layer) and has emerged as a critical component in addressing complex cybersecurity challenges [ 10 ]. However, DL is well-known for its extensive data requirements and computational demands, which can be mitigated through the implementation of multi-task learning (MTL).

MTL refers to ML training approach where models are concurrently trained using data from multiple tasks. This is achieved by leveraging shared layers, enabling models to recognize the main correlations across a set of interconnected tasks [ 11 ]. MTL initially aims to address the data sparsity problem by aggregating labeled data from all tasks, reducing manual labeling costs, and reusing existing knowledge. As Big Data emerges in different areas of AI such as computer vision and natural language processing (NLP), deep MTL models can provide higher performance than single-task models. MTL utilizes more data from different tasks, learning more robust representations and strong models in terms of overfitting risk and performance [ 12 ].

MTL offers several advantages over single-task learning (STL), such as leveraging similarities and relationships between tasks, acting as a regularizer, improving generalization, and reducing the risk of overfitting [ 11 , 13 ]. By jointly learning multiple tasks, the model can take advantage of further information available in the training data, leading to more robust and accurate predictions. MTL also presents the challenge of limited data, as it requires the model to learn from related tasks, thereby facilitating the transfer of knowledge and enhancing the learning process for individual malware detection tasks [ 14 ].

In ML/DL, optimizing a single task can lead to reasonable performance, but it can be costly and difficult to cover edge cases. Taking into consideration that training complicated tasks requires significant computational resources. Multi-task learning can help address these issues by providing more diverse data and reducing training time and resources. Using multiple tasks can provide more data in general and increase diversity in data, thus enhancing the overall performance of the system [ 15 ].

Several systematic review studies have explored the application of existing classification algorithms to detect cyber threats. For instance, the study [ 16 ] conducted a systematic review of AI and ML techniques for cybersecurity, while [ 17 ] presented a systematic review of defensive and offensive cybersecurity with ML. A number of articles have also looked into how ML and DL can be used in specific areas of cybersecurity, such as (i) detecting malware on the Internet of Things [ 18 ], (ii) detecting malware on Android mobile devices [ 19 ], (iii) cloud security [ 20 ], and (iv) detecting phishing [ 21 ].

However, these systematic reviews only looked at certain areas of cybersecurity that used a single-task learning approach and failed to consider the potential advantages of MTL. To address this gap, our study aims to provide an overview exploration of the cybersecurity domain utilizing MTL techniques. The primary objective of this paper is to conduct an SLR on MTL in cybersecurity through ten research questions. This review delivers a variety of multi-task models for tasks related to cybersecurity, including network-based and host-based intrusion detection, cyber threat detection, cyberbullying detection, malware detection, and critical infrastructure attack detection.

To the best of our knowledge, this is the first SLR that offers a comprehensive overview of MTL’s application across various cybersecurity application domains.

In this study, we investigate the application of multi-task learning techniques in cybersecurity, following a systematic literature review (SLR) to identify and synthesize relevant research methodologies. This SLR provides a comprehensive overview of the state of the art, supporting our investigation into effective multi-task learning strategies for improving prediction models in cybersecurity. The insights gathered from this SLR study are significant for recognizing new trends and establishing more effective cybersecurity strategies in these critical domains.

The following ten research questions were formulated in this research:

What are the potential applications (e.g., detection of malware, detection of network intrusion) of MTL in the cybersecurity domain?

What advantages does MTL offer for cybersecurity?

What type of tasks are used in MTL-based models?

What types of machine learning techniques (e.g., unsupervised) are used for MTL models in cybersecurity?

What are the most frequently used machine learning and deep learning algorithms for MTL in cybersecurity?

Which model provides the best performance for MTL in cybersecurity?

What datasets are used for evaluating multi-task learning models in cybersecurity?

What kind of evaluation approaches and parameters are used?

Which implementation platforms are used in MTL studies?

What are the challenges and possible solutions for multi-task learning in cybersecurity?

In this study, we investigate the application of multi-task learning techniques in cybersecurity, following a systematic literature review (SLR) to identify and synthesize relevant research methodologies. This SLR provides a comprehensive overview of the state-of-the-art, supporting our investigation into effective multi-task learning strategies for improving prediction models in cybersecurity.

The data extraction from 28 paper and analysis technique utilized in this study is both quantitative synthesis and qualitative analysis methodologies. We recognize that the methods applied for data extraction and analysis can vary across studies; consequently, we have adopted a detailed approach to address this diversity. Our synthesis includes quantitative techniques to systematically assess and summarize numerical data, while also employing qualitative analysis methods to explore the aspects and outcomes within the literature. Furthermore, our review compiles the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) reporting guidelines, ensuring equality in our methodology [ 22 , 23 ]. For a detailed presentation of our data extraction and analysis procedures, readers are encouraged to refer to the Research Methodology section, where we provide in-depth insight into our approach.

The main contributions of this study are listed as follows:

This article presents the first SLR in the literature on the implementation of multi-task learning in cybersecurity and offers insights into existing research, methodologies, and challenges.

This research identifies and categorizes five critical applications where MTL is applied, shedding light on specific areas such as network intrusion detection and malware detection. This categorization helps in understanding the diverse applications of MTL in cybersecurity.

This SLR not only synthesizes existing knowledge but also evaluates research trends, highlighting the predominant use of supervised learning algorithms and identifying research challenges and potential research areas for future exploration.

Building on the fundamental ideas presented in Sect. 2 ’s background, this article examines the important facets of multi-task learning (MTL) in cybersecurity in the following sections. Section 2 presents background and related work. In particular, Subsect. 2.1 discuses several cybersecurity issues which are the main focus of our investigation. Subsection 2.2 explores the complexities of multi-task learning, explaining its foundational ideas and cybersecurity applications. Section 3 describes the research methodology, emphasizing the review protocol. Section 4 presents the detailed outcomes obtained from this SLR study, where Subsect. 4.1 elaborates on the primary papers selected, while Subsect. 4.2 provides the detailed answers to the predefined research questions. Subsection 4.3 presents the threats to validity. Section 5 discusses the conclusion and future work.

2 Background and related work

This section explores the foundational aspects of cybersecurity and multi-task learning, as well as an examination of related work in the field. It begins with a brief overview of cybersecurity fundamentals, followed by an exploration of multi-task learning concepts. Later, it reviews relevant studies that employ multi-task learning techniques, providing insights into their methodologies and findings.

2.1 Cybersecurity problems

To briefly address cybersecurity challenges, we will enumerate the most significant types of cyberattacks. These include phishing attacks, impersonation attacks, malware attacks, denial-of-service (DoS) attacks, and hacking and unauthorized access. Additionally, there are specific threats to social media platforms, such as hashtag hijack attacks and black market tweet detection services. Each of these poses unique threats to digital security and integrity, encompassing various forms of unauthorized access, malicious software, misleading strategies, and social manipulation.

A cyberattack refers to an intentional action aimed at compromising the confidentiality, integrity, or availability of IT infrastructures, including their hardware, software, or electronic data. These attacks involve criminal operations leveraging digital technology, including computers, cellphones, the internet, and other digital devices. It is important to note that such attacks not only compromise the CIA triad but also affect other key security properties such as authenticity and non-repudiation [ 24 ].

The Cyber Security Breaches Survey, aligned with the UK’s National Cyber Strategy, informs government policy to enhance cyber resilience among businesses, charities, and educational institutions [ 25 ]. It examines their cyber security policies, processes, and responses to various cyberattacks and crimes. They provided the most common types of cyberattacks in 2024, which include phishing attacks, impersonation attacks, malware attacks, denial-of-service (DoS) attacks, and hacking and unauthorized access. These attacks vary in their methods and impacts, affecting a significant portion of businesses and charities. Table 1 summarizes these common cyberattacks, detailing their distinct characteristics and impacts.

Cybercrimes cover a wide spectrum of activities, each introducing unique threats to cybersecurity. Network intrusion, for instance, involves unauthorized access to digital networks can lead to loss of valuable resources and cause the risk data security. These intrusions usually go through a series of steps, beginning with gathering information and ending with the compromise of data [ 26 ].

Malware, a common type of cyber threat, is software that is specifically created to carry out harmful actions on targeted computers, resulting in disruptions. Some of malware types are viruses, worms ransomware, spyware, adware and scareware [ 27 ].

Phishing is a sneaky strategy used by attackers to trick users into giving away important information, like personal details, banking credentials, IDs, and passwords. They do this by pretending to be trustworthy websites from reputable organizations. Phishing attacks come in various forms, such as deceptive phishing and technical subterfuge [ 28 ].

Spam, known for its unsolicited and unwanted messages sent in bulk, serves a variety of purposes, from advertising products or services to promoting fraudulent schemes. Spam can be spread across several channels including social media platforms, causing inconvenience and potential risks to users [ 29 ].

On the contrary, cyberbullying entails the dissemination of offensive and discriminatory language on various social media platforms. Cyberbullying has the potential to extend beyond personal harm and cause wider social disruptions, and in some cases, it can even play a role in political violence [ 30 ].

Social media platforms also encounter unique cyber threats, such as hashtag hijack attacks on mobile social networks. These attacks can disrupt users’ search for relevant content and potentially result in the spread of irrelevant spam or unrelated topics [ 31 ]. In addition, Tweet Detection services bring attention to the problem of fabricating content evaluations, such as likes, retweets, and quotes, through unnatural engagement. This brings challenges to the authenticity of online interactions [ 32 , 33 ].

Furthermore, critical infrastructure systems, including hospitals, telecommunications, energy, banking, finance, and postal sectors, are prime targets for cyberattacks. The definition of a cyberattack on infrastructure can be ambiguous, resulting in the categorization of four distinct types. These types are based on the means of attack (physical or cyber-physical) and the resulting damage (physical or functional) [ 34 ].

Researchers increasingly rely on AI techniques, particularly ML and DL methods, to deal with the rising threat of cybercrimes. Recent progress in cybersecurity research have offered valuable insights into emerging threats and effective mitigation strategies. Several significant studies in malware detection, particularly in the field of Obfuscated Memory Malware (OMM) [ 35 ], provide a concise and efficient method for identifying new malware in embedded and IoT devices that have limited resources. They achieve this by utilizing hybrid models to outperform existing detection methods. In the same vein, studying denial-of-service (DoS) and distributed denial-of-service (DDoS) attacks [ 36 , 37 ] offers valuable knowledge on mitigating the effects of disruptive attacks on digital systems.

MTL is a powerful method that deals with many different types of cybercrimes, such as network intrusion, malware, cyber threats, cyberbullying, and vulnerabilities in critical infrastructure. In the Results section (referenced as 4 ), an in-depth investigation is conducted on the utilization of MTL in these domains.

2.2 Multi-task learning

MTL aims to enhance the performance learning tasks by using shared information between task [ 38 ]. MTL can learn multiple output targets based on a single input source, a single output target based on multiple input sources, or a combination of these two approaches [ 39 ]. To illustrate the concept of MTL in a practical context, consider the scenario of a security analyst tasked with identifying spam and phishing emails. These are related tasks, but with distinct characteristics:

Spam: Unsolicited bulk advertising.

Phishing: Attempts to trick recipients into revealing personal information or clicking malicious links.

In traditional single-task learning, separate models might be trained for each task. However, with multi-task learning (MTL), a single model can be trained to handle both tasks simultaneously. For instance, the model could analyze email content for common indicators of spam (e.g., keywords, sender information) and phishing (e.g., urgency, suspicious attachments), leveraging shared knowledge between the tasks.

By employing MTL, the model can improve its accuracy in identifying both spam and phishing emails while also benefiting from increased efficiency through shared learning. This example illustrates how MTL can be a valuable approach in cybersecurity for addressing related threats more effectively.

MTL is an approach to ML that leverages information from relevant learning tasks to solve multiple tasks concurrently [ 40 , 41 ]. By incorporating domain information into the training signals for related tasks, this approach improves generalization by providing an inductive bias [ 41 ]. The idea behind this approach is that the knowledge gained from each task can enhance the learning process for other tasks [ 40 ]. MTL can be useful when tasks have similarities, but it has also been proven to be advantageous for learning tasks that are not related to each other [ 42 ].

MTL is distinct from STL, as shown in Fig. 1 , in that each task is handled independently and model parameters are learned separately. MTL depends on the interrelation of tasks to effectively learn them all together. Training signals from related tasks can significantly enhance the learning of model parameters for each task [ 38 , 43 ]. IT has been proven to upgrade model performance, especially in scenarios with limited training examples and associated tasks [ 44 , 45 ].

Comparison of single- and multi-task learning frameworks [ 46 ]

Various MTL scenarios have been applied, such as multi-task unsupervised learning, multi-task active learning and multi-task reinforcement learning [ 14 ]. In the context of multi-task supervised learning, each task involves a supervised learning scenario where models map data instances to corresponding labels. Noteworthy MTL models corresponding to each setting are discussed in [ 14 ].

Two key factors that have a significant impact on MTL are the relationship between tasks and how tasks are defined. Understanding the relationships between different tasks and shaping the design of MTL models accordingly is crucial for task-relatedness. Tasks can be classified into different categories, such as supervised tasks like classification and regression and unsupervised tasks like clustering [ 14 ]. MTL has been applied in various fields, such as computer vision [ 47 ], bioinformatics [ 48 ], drug discovery [ 49 ], health informatics [ 50 ], speech recognition [ 51 ], natural language processing [ 52 ], and web applications [ 53 ], leading to improved application performance.

In the case of DL, MTL is commonly implemented using a shared feature extractor and several task-specific layers. The shared feature extractor processes the input data, while task-specific inputs generate predictions for each task [ 11 ].

Two methods for MTL in deep neural networks. a Approach with hard parameter sharing. b Approach with soft parameter sharing [ 54 ]

The current approaches MTL in DL are commonly categorized into two distinct groups: hard parameter sharing and soft parameter sharing. Hard parameter sharing is the process of distributing model weights across several tasks so that each weight is trained to mutually minimize the number of loss functions, as shown in Fig. 2 [ 54 ]. This approach minimizes the risk of overfitting by compelling the model to capture a representation that fits all tasks simultaneously. For soft parameter sharing, each task is associated with its own specific model that has distinct weights, as illustrated in Fig. 2 . However, the joint objective function integrates the distance between the model parameters of different tasks. These architectural decisions align with MTL’s mechanisms, emphasizing the importance of simultaneous learning, preventing overfitting, and optimistic representations to multiple tasks [ 13 ].

The variety of MTL’s mechanisms serves to further emphasize its effectiveness. Implicit data augmentation is achieved by concurrently learning multiple tasks, expanding the sample size, and avoiding overfitting. Attention focusing enables the model to focus on relevant features, which is crucial in scenarios with noisy or limited data [ 40 ]. Eavesdropping facilitates the learning of complex features by learning from other tasks. Representation bias introduces a preference for representations by multiple tasks which enhancing generalization [ 55 ]. MTL acts as a regularizer, reducing the risk of overfitting and minimizing the model’s sensitivity to random noise, thereby enhancing its overall generalization ability. The way these mechanisms work together with the architectural choices made for parameter sharing methods shows a complete way to use MTL in deep neural network [ 13 ].

The standard expression for a conventional MTL algorithm [ 12 , 40 , 56 , 57 ] is presented in the following equation:

The input vector \(X^m \in \mathbb {R}^{N_m \times D}\) represents the m-th task, whereas the output vector \(y^m \in \mathbb {R}^{N_m \times 1}\) corresponds to the m-th task. The weight vector \(w^m \in \mathbb {R}^{D \times 1}\) denotes the regression parameters for the m-th task, which is utilized to map \(X^m \rightarrow y^m\) . The variables \(N^m\) , D, and M represent the quantities of samples, features, and tasks, respectively, in the context of input matrices. The regularizer, identified as Reg( W ), is used to include prior knowledge of the data and various hypotheses about the interaction between tasks in order to create different constraints on the parameter matrix W. The regularization parameter \(\lambda\) controls the balance between the loss function and the regularizer. If \(\lambda\) is set to zero, the resulting solution does not include any assumptions or prior knowledge about the relatedness of tasks. This approach may only provide sufficient outcomes based on the training set. When \(\lambda\) is set too large, a generic solution that meets the task-relatedness assumption could be produced, but it might not work effectively for every prediction task. The determination of the regularization parameter and other hyper-parameters is often achieved via the use of inner cross-validation using the training samples [ 58 ]. Overall, Eq. 1 has two terms, namely the data fidelity term, and the regularization term [ 12 , 59 ].

2.3 Related work

Several review papers have contributed to a deep understanding of the application of ML and DL in the field of cybersecurity. These reviews have covered various aspects and sub-domains within cybersecurity. These reviews in Table 2 offer a brief understanding of the application of ML and DL techniques in various domains of cybersecurity. They offer valuable insights for researchers, experts, and individuals interested in exploring the ever-changing field of cybersecurity. Remarkably, our extensive review revealed a notable gap in the existing literature. Although there has been extensive research on ML techniques in the field of cybersecurity, we did not come across any systematic literature review paper that specifically focuses on the key problem of multi-task learning in this area.

Our review revealed numerous notable papers that explore the use of ML techniques in cybersecurity. However, it is clear that there is a lack of a comprehensive and focused systematic literature review (SLR) dedicated to investigating the application of ML in this specific domain.

3 Research methodology

In this paper, we systematically reviewed the use of MTL in cybersecurity applications by adopting a methodology based on Kitchenham et al. [ 64 ], which is widely recognized for its effectiveness in software engineering research.

3.1 Review protocol

Establishing a defined review protocol was the first phase in our research approach. This step included the formulation of specific research questions that would serve as an outline for our study. The purpose of our study questions was to gain a knowledge of the practical applications, advantages, and challenges of MTL in the field of cybersecurity.

3.2 Data sources and search strategy

A search strategy is a methodical process for locating relevant resources, which involves choosing databases, keywords, and search strings. It guarantees thorough inclusion and reduces bias. In order to collect related research papers, we ran an extensive search across multiple academic databases, which included:

Google Scholar

Web of Science

Science Direct

IEEE Xplore

ACM Digital Library

The selection of these databases was based on their extensive coverage of academic publications and their direct relevance to the domains of cybersecurity and machine learning.

We developed search strings suited to our research questions to ensure comprehensive coverage of relevant studies. The search strings included terms like "Multi-Task Learning," "cybersecurity," "machine learning," "deep learning," "cyber threats," and "cyber attacks." Boolean operators and wildcards were used to refine the searches, ensuring a broad yet focused retrieval of relevant articles. The search process involved multiple iterations to optimize the search strategy, reducing the likelihood of missing important studies and minimizing irrelevant ones.

3.3 Inclusion and exclusion criteria

To manage the large amount of literature, we established specific inclusion and exclusion criteria, as detailed in Table 3 . These criteria are predefined rules that determine which studies are suitable for inclusion in the review and which are not. They consider factors such as publication date, study design, study language, outcomes, and their relevance to our research questions. This approach ensures that only the most relevant and high-quality studies are selected for our review.

3.4 Data extraction and synthesis

After selecting the relevant studies, we systematically gathered essential data, including authorship, publication year, type of study, and information answers to our research questions. Following that, we proceeded with data synthesis, which involves integrating and analyzing the data from the selected studies. This process may include statistical techniques or other methods to draw meaningful conclusions, identify patterns, or explore variations in the evidence. The detailed explanation of the data extraction and synthesis process, along with the analysis, is provided in the results section.

3.5 Review process

After implementing the aforementioned selection criteria, a total of 85 publications from various data sources were included for further evaluation. Following the removal of duplicate records, we proceeded to evaluate the titles and abstracts of the remaining 73 publications. The 35 articles that were obtained were carefully examined through a comprehensive study of their complete texts. We obtained A manual search in backward snowballing and forward snowballing articles which led to the discovery of additional articles. Then, as illustrated in Fig. 3 , a total of 28 papers were evaluated for their quality and included in the study.

The PRISMA flow diagram [ 23 ]

3.6 Quality assessment

As the last step of the research methodology, quality assessment was performed and quality criteria specified in Table 4 were applied to the selected paper. A paper that fully answers the question receives a score of 1. A score of 0.5 indicates a response that only partially meets the specified criterion. A score of 0 signifies inadequacy in fulfilling the quality criterion.

The quality evaluation scores of the selected papers are illustrated in Fig. 4 . The x-axis of the graph shows the quality scores of the papers, while the y-axis indicates the frequency of papers corresponding to each level in addition to the threshold for inclusion of a paper was set to 4. Most papers scored above 6 points, indicating high quality. No papers scoring below 4 were included in the final analysis.

The quality score of selected papers

In this section, we present an analysis of the findings derived in response to the research questions formulated at the beginning of this study. Before presenting the answers, additional details on the identified articles are provided, includes their annual distribution and publication distribution. According to the data shown in Fig. 5 , there is a consistent upward trend in the number of papers published each year. Despite concluding our search procedure from 2017 to early 2023, the highest volume of papers occurs in 2020 and begins to rise again in 2022. This trend suggests a growing interest among cybersecurity experts in the application of multi-task learning for cybercrime detection.

Number of article from 2017 to 2023

4.1 Selected primary studies

This section presents the primary research papers that have been used to answer our 10 research questions. Table 5 presents a compilation of primary articles that have been carefully chosen, along with their respective publication years. We included this table to enhance the repeatability and transparency of our research, providing an overview of the selected primary papers. It is important to note that papers published after our search period are not analyzed in this research paper.

4.2 Response to research questions

4.2.1 rq1: what are the potential applications of multi-task learning in the cybersecurity domain.

A broad range of potential applications for multi-task learning were brought to light through the selected articles (Fig. 6 ). In the context of network intrusion detection [ 66 , 68 , 71 , 74 , 79 , 80 , 84 , 86 , 88 ], the majority of approaches consider the classification of network traffic as well as the detection of malicious traffic. Similar to the previous example, the prospective areas for malware detection [ 69 , 70 , 73 , 75 , 78 ] included Internet of Things malware detection as well as malware classification. One of the areas that Cyber Threat Detection [ 31 , 32 , 33 ] has extended into is the identification of risks on social media platforms and the detection of tweets related to black markets. Additionally, the identification of hate speech was the primary emphasis of the cyberbullying detection program [ 72 , 77 ]. A significant amount of diversity was included in the Critical Infrastructure Protection [ 67 , 72 , 81 , 82 , 83 , 85 , 87 , 89 ]. This included the detection of cyberattacks in smart grids, the defense of critical infrastructure, the protection of Internet of Things-based systems, the identification of electricity fraud in advanced metering infrastructure (AMI), the implementation of speaker verification, and the detection of spoofing attempts. All of these activities contributed to the strengthening of critical infrastructure in a variety of different ways.

Multi-task learning applications in cybersecurity

4.2.2 RQ2: What advantages does MTL offer for cybersecurity?

MTL is a highly adaptable and potent method in the field of cybersecurity, offering a range of advantages that significantly strengthen security systems. Through a fully analysis of selected studies, MTL facilitates the simultaneous detection of multiple attack types in addition to support more efficient learning processes by using multiple layers for feature representation [ 66 , 68 , 84 , 86 ].

One key benefits of MTL is its ability to handle diverse tasks within cybersecurity, including encrypted traffic classification and network intrusion detection, while adapting various data types as input [ 69 , 70 , 71 ]. By integrating multiple tasks into an end-to-end training solution, MTL not only addresses different providers’ requirements but also minimizes computational overhead and redundancies through shared feature learning architectures [ 73 , 74 , 76 , 79 ]. This holistic approach as it streamlines problem resolution and optimizes resource utilization, it also enhances feature learning capabilities [ 67 , 81 ].

Moreover, the adaptability of MTL is a significant asset in addressing evolving cybersecurity challenges. By automating alarm region determination and quickly adapting to new types of malware without complete retraining, MTL ensures practical applicability to real-world security scenarios [ 67 , 79 , 80 ]. This dynamic adaptability makes MTL an invaluable tool in the arsenal of cybersecurity professionals, offering resilience and flexibility in the face of rapidly changing threat landscapes.

In basic terms, MTL in cybersecurity improves the efficiency and effectiveness of security systems, providing practical benefits like adaptability, efficiency, and robustness. Through the utilization of shared representations and multiple layers for feature learning, MTL has proven to be a highly adaptable and essential approach in tackling the complex obstacles of cybersecurity.

4.2.3 RQ3: What type of tasks are used?

Various tasks are employed in the selected primary papers to enhance its security. A promising way to support cybersecurity efforts is through multi-tasking, which makes it possible to address multiple security-related issues at once. Table 6 offers a summary of these tasks in which they can be applied to improve threat detection, allocate resources optimally, enhance model accuracy, manage complicated data, and promote cooperation in the cybersecurity domain. The tasks cover a wide range of domains within cybersecurity, each targeting significant aspects of detecting and protecting against threats.

Network intrusion detection, malware detection, and VPN encapsulation detection seek to identify, classify, and analyze potentially malicious activities or anomalies within network traffic. Tasks like Trojan detection and classification aim to spot and categorize specific types of malicious software. Other tasks, such as traffic type recognition, software application classification, and bandwidth prediction, explore network behaviors and predicting network patterns.

Cyber threat detection tasks like text matching, hashtag hijack identification, and cyber intelligence recognition work toward identifying and analyzing threats within textual and social media data. Cyberbullying detection tasks focus on identifying hate speech, offensive language, racism, and sexism within online interactions. Critical infrastructure protection tasks includes graph classification, feature extraction, and image and video analysis to protect critical systems from various threats, including fraud, spoofing, and large-scale data flow classification and analysis. In addition, tasks associated with automatic speaker verification and anti-spoofing address concerns regarding voice authentication and protecting against spoofing attempts. Each of these tasks reflects a crucial domain within cybersecurity, contributing to the overall objective of identifying and safeguarding against threats to the infrastructure.

4.2.4 RQ4: What type of machine learning techniques are used in MTL?

MTL is a widely used ML approach for cybersecurity tasks. It can be built based on the following ML types:

Supervised learning: Employs labeled datasets to train models for various tasks.

Unsupervised learning: Utilizes techniques such as clustering or anomaly detection, where patterns are identified without the use of labeled data.

Reinforcement learning: Uses agents to make sequential decisions in and maximize a cumulative reward through trial and error.

Semi-supervised learning: Optimizes model performance using a mix of labeled and unlabeled examples across different tasks. These approaches can help enhance predictive accuracy, uncover patterns and relationships, and adapt to evolving cyber threats.

Machine learning techniques in multi-task Learning for cybersecurity

Figure 7 highlights the prevalent application of supervised learning methodologies in multi-task learning across the cybersecurity field. However, there is a noticeable lack in the use of reinforcement learning, unsupervised learning, and semi-supervised learning methods within this field.

4.2.5 RQ5: What are the most frequently used machine learning and deep learning algorithms for MTL?

In our study, we identified several ML and DL algorithms that are frequently employed to address various security challenges. These algorithms play a crucial role in enhancing the functionality of cybersecurity systems. We categorized these algorithms into ML and DL groups. Figures 8 illustrate the number of research papers that apply ML and deep learning algorithms across each cybersecurity problem.

The ML algorithms include Sequential Minimal Optimization (SMO), Support Vector Machines (SVM), K-Means, Neural Networks (NN), Random Forests (RF), Simple Logistic Regression (SLR), Decision Trees (DT), k-Nearest Neighbors (k-NN), Fuzzy Logic, Naive Bayes (NB), AdaBoost, and Logistic Regression (LR). On the other hand, the DL algorithms include Bidirectional Long Short-Term Memory Networks (BiLSTM), Bidirectional Encoder Representations from Transformers (BERT), Deep Reinforcement Learning (DRL), Autoencoders (AE), Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN), Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNN), Bidirectional Gated Recurrent Unit (BiGRU), Graph Neural Networks (GNN), Convolutional Graph Neural Networks (CGNN), Graph Autoencoders (GAE), Feedforward Neural Networks (NewFF), and Multilayer Perceptrons (MLP).

The main security challenges:

P1: Network intrusion problems.

P2: Malware problems.

P3: Cyber threat problems.

P4: Cyberbullying problems.

P5: Critical infrastructure problems.

As illustrated in Fig. 8 a, Support Vector Machine (SVM) appears as the most frequently utilized algorithm for addressing problems P1, P2, P4, and P5. Conversely, for problem P3, Random Forest stands out as the predominant algorithm. In Fig. 8 b, Convolutional Neural Network (CNN) takes the lead as the most commonly employed DL algorithm for problems P1 and P5. As for problem P2, CNN, Deep Neural Networks (DNN), and Autoencoder (AE) are the primary algorithms, each applied once. Remarkably, for problem P3, Multi-Layer Perceptron (MLP) emerges as the prevalent DL algorithm. It is worth distinguishing between MLP and DNN, as MLP refers to a specific type of neural network with multiple layers, while DNN is a broader term covering neural networks with a considerable number of layers and various architectures. For problem P4, the BERT (Bidirectional Encoder Representations from Transformers) algorithm takes the majority, and for problem P5, CNN remains the most commonly used algorithm.

Most frequently used machine learning and deep learning algorithms for multi-task learning

4.2.6 RQ6: Which model provides the best performance for MTL?

Each key research study has presented a unique sort of model. Table 7 outlines the various models utilized in multi-task learning within cybersecurity, detailing their specific advantages in building security systems. While most research papers introduce new models with special names, some are simply named as multi-task learning (MTL) models.

As shown in Table 7 , it is clear that the majority of multi-task models outperform their single-task models, indicating their efficacy in effectively reducing complexity and efficiently handling multiple tasks.

4.2.7 RQ7: What datasets are used for evaluating MTL models?

In terms of cybersecurity-related challenges, we identified several public datasets and sources in the papers we reviewed. Table  8 presents an overview of these datasets. Following that, we provide a summary of their main characteristics.

Regarding Network Intrusion detection, a wide range of datasets have been utilized: UNSW-NB15 [ 66 , 71 , 80 ], a hybrid dataset merging benign network traffic with simulated cyberattacks across nine different types; CICIDS2017 [ 66 , 69 , 76 , 80 , 84 ], housing 14 attack categories such as denial-of-service (DoS) attack, distributed denial-of-service (DDoS) attack, web attack, and botnet intrusions; Bot-IoT stands out [ 71 ], providing a combination of actual and simulated Internet of Things (IoT) network activity that includes intrusion attempts such as distributed denial-of-service (DDoS) attacks and stealing of information. Additionally, ISCX2012 [ 71 ] and the ISCX VPN-nonVPN [ 79 , 84 ] datasets present scenarios of network infiltration and encrypted traffic detection, respectively.

For malware detection, datasets such as IoT-23 and VARIoT [ 69 ] focus on IoT device traffic, while StratosphereIPS and MTA share diverse malware and traffic mixes. The Microsoft Malware Classification Challenge dataset [ 78 ] Provides a diverse collection of malware samples organized into distinct groups, presented as disassembled and binary data files, which enhance the complexity of the detection tasks in this extensive collection.

In cyber threat detection, a variety of datasets have been developed: Study [ 31 ] constructed a dataset by aggregating microblogs from Weibo, China’s major microblog platform, amassing 11,508 relevant microblogs through API searches and random selection, while study [ 33 ] gathered 31,281 tweets. Study [ 32 ] collected 2,690 tweets from black market sites and 2,000 genuine tweets from users’ timelines, focusing on non-English tweets and those with adequate length.

In cyberbullying detection, studies [ 72 , 77 ] used datasets such as ST-Bully, BullySent, and collections obtained from Hatebase.org, Twitter API, and diverse social media platforms. The datasets were selected to include a wide range of online interactions, covering everything from casual conversations to intense debates, in order to accurately capture samples of bullying, hate speech, aggression, and harassment.

Concerning critical infrastructure protection, datasets such as IEEE bus system [ 65 ], railway images [ 81 ], FaceForensics [ 83 ], and ASVspoof 2017 [ 85 ] were utilized. These datasets represent a variety of information, including power grid simulations, video manipulation datasets, and transactional communication data. These datasets have played a major part in revealing previously unknown security threats and vulnerabilities. They provide beneficial information that are essential for protecting critical infrastructure from potential risks and attacks.

4.2.8 RQ8: Which evaluation approaches and parameters are used in MTL?

The assessment criteria utilized important metrics essential in cybersecurity analysis, including accuracy, precision, recall, F1 score, specificity, Area under Curve (AUC), false positive and negative rates, mean absolute and squared errors, detection rate, error rate, and task-specific loss functions. The choice of evaluation methods varied depending on the specific cybersecurity tasks and research aims, emphasizing precision, recall, and AUC for precise threat detection while minimizing false positives.

Our findings highlight the diverse range of evaluation approaches and parameters utilized in assessing multi-task learning models in cybersecurity, demonstrating the adaptability and resilience necessary for facing complex and evolving cyber threats. In Fig. 10 , we show the frequency of each evaluation parameter used across identified cybersecurity problems. For network intrusion detection (P1), malware detection (P2), and critical infrastructure protection (P5), the accuracy metric was mostly used, whereas cyberbullying detection (P3) leaned toward F1 score, recall, and precision. Conversely, cyber threat detection (P4) mainly utilizes accuracy, F1 score, macro-F1, and weighted-F1 as primary evaluation metrics.

Usage of evaluation metrics for the cybersecurity problems

In Fig. 9 , the distribution of validation approaches is illustrated, showing a clear inclination toward cross-validation as the preferred method in the majority of papers. Nevertheless, some studies failed to mention the validation approach, potentially affecting the accuracy of experimental studies. This highlights the importance of providing thorough explanations in research papers.

Evaluation approaches

Figures 9 and 10 illustrate that the evaluation metrics (accuracy and F-measure) and cross-validation are commonly used as a preferred validation approach.

4.2.9 RQ9: Which implementation platforms are used to develop MTL models?

We investigated the various implementation platforms employed by research papers to develop and deploy cybersecurity solutions. The choice of implementation platform is a critical consideration as it directly impacts the efficiency and scalability of the solutions. Based on our findings, we can see the implementation platforms that have been used in Fig. 11 .

Distribution of selected implementation platforms

Number of scholarly articles have emphasized the use of open-source ML and DL frameworks, such as TensorFlow ( https://www.tensorflow.org/ ), PyTorch ( https://pytorch.org/ ), and Keras ( https://keras.io/ ), for the implementation of multi-task learning models. These frameworks offer an extensive array of tools, libraries, and pre-trained models, thereby expediting the development process of applications.

In the selected primary research papers, unfortunately, 48% of the studies did not explicitly mention the framework used, while 24% employed Keras, 14% utilized PyTorch, and another 14% employed TensorFlow. The choice of the implementation platform often depends on the specific objectives and requirements of the cybersecurity task at hand. Researchers tend to select platforms that align with factors such as data volume, real-time processing demands, and available resources. Furthermore, considerations pertaining to data privacy, regulatory compliance, and the necessity for robust security measures significantly affect the platform selection process. The widespread adoption of Keras in the selected MTL-based papers can be attributed to its high-level, user-friendly Application Programming Interface (API), modularity allowing compatibility with various DL frameworks like TensorFlow, and its provision of a user-friendly experimental environment, which makes it a popular choice compared to other platforms.

4.2.10 RQ10: What are the challenges and possible solutions for multi-task learning in cybersecurity?

To effectively address the challenges of MTL in the cybersecurity field, we conducted a systematic analysis and synthesis of the challenges identified in the selected paper. This synthesis process enables us to extract the main difficulties and their corresponding solutions, offering a systematic framework for understanding and dealing with the complexity of applying MTL in cybersecurity. In order to respond to the subsequent research question, We provide Table 9 , which summarizes the challenges along with their corresponding resolutions across the chosen papers.

4.3 Threats to validity

This systematic literature review aimed to comprehensively examine the existing research on the use of multi-task learning in the field of cybersecurity. The primary objective of this study was to respond to 10 research questions posed at the beginning of this research, determine the obstacles encountered, and evaluate the effectiveness of these ideas using established measures. The findings were aggregated in a manner that successfully addressed the research questions. The identification of difficulties has the potential to facilitate future research work and enhance the development of more effective solutions. Including the datasets used, as well as the ML classifier utilized and its corresponding accuracy, would provide a clear path for future research.

It is possible that some articles were not included in the search results owing to the absence of similar terms being used. Certain observations may have been altered due to variations in the use of terminology across various publications. The involvement of all authors in the article selection process and the subsequent formation of a consensus helped mitigate any bias. In order to mitigate the risks of conclusion validity, the authors of this research drew their findings from conversations during several meetings. Consequently, the individual basis upon which the data were interpreted was minimized.

5 Conclusion and future work

Efficient detection models utilizing multi-task learning algorithms were created through advancements in multi-task algorithms. Despite the large number of models that have been built so far, there are still certain problems that lack clear answers. We conducted a systematic literature review to respond to 10 research questions, investigated 28 high-quality articles, and assessed the applications and implementations of multi-task learning algorithms. We discussed the difficulties and potential solutions in cybercrime detection algorithms based on multi-task learning. A thorough identification was made of the most used multi-task learning algorithms, frequently used datasets, ML categories, development platforms, assessment measures, validation strategies, and data sources. Moreover, research gaps and challenges were provided.

The results of our SLR have significant implications for various fields. In the military sector, it is of utmost importance to guarantee the protection of sensitive data in order to maintain national security. Reliable cybersecurity solutions play an essential role in the financial services industry to protect financial data. Data protection is essential in political systems to mitigate the risk of cyberattacks. This SLR emphasizes many emerging trends in cybersecurity that are applicable to these disciplines.

Our study has also identified several areas for future research and development. These include creating semi-supervised and unsupervised models for diverse detection systems, advancing hybrid multi-task learning architectures, establishing a robust framework for comparative analysis, and addressing specific challenges in identifying and mitigating diverse cyber threats. We emphasize the necessity of larger datasets that are accessible to the public for thorough evaluations and experiments. Furthermore, we recommend that future research should prioritize the creation of innovative detection models based on multi-task learning (MTL) to improve the identification and mitigation of a wide range of cyber threats in different areas of cybersecurity.

Additionally, further research may explore the potential effects of emerging technologies, such as Generative Artificial Intelligence (Gen AI) and Large Language Models (LLMs), on the use of machine learning and deep learning techniques in the field of cybersecurity. Incorporating recently published articles could further enhance the scope of this systematic literature review. An analysis of real-world cybersecurity systems, including case studies, success stories, and lessons learned, could also provide valuable insights into the potential impact of multi-task learning in future.

Data availability

Data are available upon request.

Langan T (2022) Internet Crime Report www.ic3.gov

Shaukat K, Luo S, Varadharajan V, Hameed IA, Xu M (2020) A survey on machine learning techniques for cyber security in the last decade. IEEE Access 8:222310–222354. https://doi.org/10.1109/ACCESS.2020.3041951

Article   Google Scholar  

Szor P (2005) The art of computer virus research and defense. Addison-Wesley Professional

Gümüşbaş D, Yıldırım T, Genovese A, Scotti F (2021) A comprehensive survey of databases and deep learning methods for cybersecurity and intrusion detection systems. IEEE Syst J 15(2):1717–1731. https://doi.org/10.1109/JSYST.2020.2992966

Ansari MF, Dash B, Sharma P, Yathiraju N (2022) The impact and limitations of artificial intelligence in cybersecurity: a literature review. IJARCCE 11:1–2. https://doi.org/10.17148/ijarcce.2022.11912

Taddeo M (2019) Three ethical challenges of applications of artificial intelligence in cybersecurity. Minds Mach 29:187–191. https://doi.org/10.1007/S11023-019-09504-8/METRICS

Sagar R, Jhaveri R, Borrego C (2020) Applications in security and evasions in machine learning: a survey. Electronics. https://doi.org/10.3390/electronics9010097

Shaukat K, Luo S, Varadharajan V, Hameed IA, Chen S, Liu D, Li J (2020) Performance comparison and current challenges of using machine learning techniques in cybersecurity. Energies. https://doi.org/10.3390/en13102509

Abioye SO, Oyedele LO, Akanbi L, Ajayi A, Davila Delgado JM, Bilal M, Akinade OO, Ahmed A (2021) Artificial intelligence in the construction industry: a review of present status, opportunities and future challenges. J Build Eng 44:103299. https://doi.org/10.1016/j.jobe.2021.103299

Macas M, Wu C, Fuertes W (2022) A survey on deep learning for cybersecurity: progress, challenges, and opportunities. Comput Netw 212:109032. https://doi.org/10.1016/j.comnet.2022.109032

Crawshaw M (2020) Multi-task learning with deep neural networks: a survey. CoRR arXiv:2009.09796

Zhang Y, Yang Q (2021) A Survey on multi-task Learning. IEEE Trans Knowl Data Eng 34(12):5586–5609

Ruder S (2017) An overview of multi-task learning in deep neural networks. arXiv preprint arXiv:1706.05098

Zhang Y, Yang Q (2017) An overview of multi-task learning. Natl Sci Rev 5(1):30–43. https://doi.org/10.1093/nsr/nwx105

Yan A, Wang X, Yang Y, Fox R, Wang X, Gonzalez J (2020) Multi-task learning architectures and applications. Master’s thesis, EECS Department, University of California, Berkeley (May 2020). http://www2.eecs.berkeley.edu/Pubs/TechRpts/2020/EECS-2020-54.html

Ali R, Ali A, Iqbal F, Khattak AM, Aleem S (2020) A systematic review of artificial intelligence and machine learning techniques for cyber security. In: Tian Y, Ma T, Khan MK (eds) Big data and security. Springer, Singapore, pp 584–593

Chapter   Google Scholar  

Aiyanyo ID, Samuel H, Lim H (2020) A systematic review of defensive and offensive cybersecurity with machine learning. Appl Sci. https://doi.org/10.3390/app10175811

Ahmad R, Alsmadi I (2021) Machine learning approaches to iot security: a systematic literature review. Internet Things 14:100365. https://doi.org/10.1016/j.iot.2021.100365

Senanayake J, Kalutarage H, Al-Kadri MO (2021) Android mobile malware detection using machine learning: a systematic review. Electronics. https://doi.org/10.3390/electronics10131606

Nassif AB, Talib MA, Nasir Q, Albadani H, Dakalbab FM (2021) Machine learning for cloud security: A systematic review. IEEE Access 9:20717–20735. https://doi.org/10.1109/ACCESS.2021.3054129

Catal C, Giray G, Tekinerdogan B, Kumar S, Shukla S, Tekinerdogan B (2022) Applications of deep learning for phishing detection: a systematic literature review. Knowl Inf Syst 64:1457–1500. https://doi.org/10.1007/s10115-022-01672-x

Kitchenham B, Pearl Brereton O, Budgen D, Turner M, Bailey J, Linkman S (2009) Systematic literature reviews in software engineering - a systematic literature review. Inf Softw Technol 51(1):7–15. https://doi.org/10.1016/j.infsof.2008.09.009

The prisma 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372 (2021) https://doi.org/10.1136/bmj.n71 https://www.bmj.com/content/372/bmj.n71.full.pdf

Moore R (2014) Cybercrime: investigating high-technology computer crime. Taylor and Francis, Canada, pp 1–318

Book   Google Scholar  

Cyber security breaches survey 2024 (2024). https://www.gov.uk/government/statistics/cyber-security-breaches

Khraisat A, Gondal I, Vamplew P, Kamruzzaman J (2019) Survey of intrusion detection systems: techniques, datasets and challenges. Cybersecurity. https://doi.org/10.1186/s42400-019-0038-7

Mvula PK, Branco P, Jourdan G-V, Viktor HL (2023) A systematic literature review of cyber-security data repositories and performance assessment metrics for semi-supervised learning. Discov Data. https://doi.org/10.1007/s44248-023-00003-x

Alkhalil Z, Hewage C, Nawaf L, Khan I (2021) Phishing attacks: a recent comprehensive study and a new anatomy. Front Media SA. https://doi.org/10.3389/fcomp.2021.563060

Jáñez-Martino F, Alaiz-Rodríguez R, González-Castro V, Fidalgo E, Alegre E (2023) A review of spam email detection: analysis of spammer strategies and the dataset shift problem. Artif Intell Rev 56(2):1145–1173

Smith PK, Mahdavi J, Carvalho M, Fisher S, Russell S, Tippett N (2008) Cyberbullying: its nature and impact in secondary school pupils. J Child Psychol Psychiatry 49(4):376–385. https://doi.org/10.1111/j.1469-7610.2007.01846.x

Qu Z, Lyu C, Chi CH (2022) Multi-task learning framework for detecting hashtag hijack attack in mobile social networks. Institute of Electrical and Electronics Engineers Inc., New Jersey, pp 90–98

Google Scholar  

Arora U, Paka WS, Chakraborty T (2019) Multitask learning for blackmarket tweet detection. Association for Computing Machinery Inc, New York, pp 127–130

Dionísio N, Alves F, Ferreira P, Bessani A (2020) Towards end-to-end cyberthreat detection from twitter using multi-task learning. IEEE Computational Intelligence (IEEE WCCI) 2020

Viganò E, Loi M, Yaghmaei E (2020) Cybersecurity of critical infrastructure. The Ethics of Cybersecurity, pp 157–177

Shafin SS, Karmakar G, Mareels I (2023) Obfuscated memory malware detection in resource-constrained iot devices for smart city applications. Sensors. https://doi.org/10.3390/s23115348

Shafin SS, Prottoy SA, Abbas S, Hakim SB, Chowdhury A, Rashid MM (2021) Distributed denial of service attack detection using machine learning and class oversampling. In: Mahmud M, Kaiser MS, Kasabov N, Iftekharuddin K, Zhong N (eds) Appl Intell Inform. Springer, Cham, pp 247–259

Alghazzawi D, Bamasag O, Ullah H, Asghar MZ (2021) Efficient detection of ddos attacks using a hybrid deep learning model with improved feature selection. Appl Sci. https://doi.org/10.3390/app112411634

Thung KH, Wee CY (2018) A brief review on multi-task learning. Multimedia Tools Appl 77(22):29705–29725

Firdausi I, lim C, Erwin A, Nugroho AS (2010) Analysis of machine learning techniques used in behavior-based malware detection. In: 2010 second international conference on advances in computing, control, and telecommunication technologies, pp 201–203 . https://doi.org/10.1109/ACT.2010.33

Caruana R (1997) Multitask learning. Mach Learn 28:41–75

Evgeniou T, Pontil M (2004) Regularized multi-task learning. KDD ’04. Association for Computing Machinery, New York, NY, USA, pp 109–117

Paredes BR, Argyriou A, Berthouze N, Pontil M (2012) Exploiting unrelated tasks in multi-task learning. In: Lawrence ND, Girolami M (eds.) Proceedings of the fifteenth international conference on artificial intelligence and statistics. Proceedings of machine learning research. PMLR, La Palma, Canary Islands vol. 22, pp 951–959. https://proceedings.mlr.press/v22/romera12.html

Baxter J (2000) A model of inductive bias learning. J Artif Intell Res 12:149–198. https://doi.org/10.1613/jair.731

Article   MathSciNet   Google Scholar  

Ben-David S, Schuller R (2003) Exploiting task relatedness for multiple task learning. In: Schölkopf B, Warmuth MK (eds) Learning theory and kernel machines. Springer, Berlin, Heidelberg, pp 567–580

Thrun S (1995) Is learning the n-th thing any easier than learning the first?. In: Proceedings of the 8th international conference on neural information processing systems. NIPS’95. MIT Press, Cambridge, MA, USA pp 640–646

Park C, Kim Y, Park Y, Kim SB (2018) Multitask learning for virtual metrology in semiconductor manufacturing systems. Comput Ind Eng 123:209–219. https://doi.org/10.1016/j.cie.2018.06.024

Ganin Y, Lempitsky V (2015) Unsupervised domain adaptation by backpropagation. In: Bach F, Blei D (eds.) Proceedings of the 32nd international conference on machine learning. Proceedings of machine learning research. PMLR, Lille, France vol. 37, pp 1180–1189. https://proceedings.mlr.press/v37/ganin15.html

Widmer C, Toussaint NC, Altun Y, Rätsch G (2010) Inferring latent task structure for multitask learning by multiple kernel learning. BMC Bioinform 11:1–8. https://doi.org/10.1186/1471-2105-11-S8-S5/TABLES/3

Ramsundar B, Kearnes S, Riley P, Webster D, Konerding D, Pande V (2015) Massively multitask networks for drug discovery. arXiv preprint arXiv:1502.02072

Widmer C, Leiva J, Altun Y, Rätsch G (2010) Leveraging sequence classification by taxonomy-based multitask learning. In: Berger B (ed) Research in computational molecular biology. Springer, Berlin, Heidelberg, pp 522–534

Deng L, Hinton G, Kingsbury B (2013) New types of deep neural network learning for speech recognition and related applications: an overview. In: 2013 IEEE international conference on acoustics, speech and signal processing, pp 8599–8603 . https://doi.org/10.1109/ICASSP.2013.6639344

Collobert R, Weston J (2008) A unified architecture for natural language processing: deep neural networks with multitask learning. In: Proceedings of the 25th international conference on machine learning. ICML ’08. Association for Computing Machinery, New York, NY, USA pp 160–167. https://doi.org/10.1145/1390156.1390177

Chapelle O, Shivaswamy P, Vadrevu S, Weinberger K, Zhang Y, Tseng B (2010) Multi-task learning for boosting with application to web search ranking. In: Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining. KDD ’10. Association for Computing Machinery, New York, NY, USA, pp 1189–1198. https://doi.org/10.1145/1835804.1835953

Wang S, Wang Q, Gong M (2020) Multi-task learning based network embedding. Front Neurosci. https://doi.org/10.3389/fnins.2019.01387

Hu Z, Zhao Z, Yi X, Yao T, Hong L, Sun Y, Chi EH (2022) Improving multi-task generalization via regularizing spurious correlation. Adv Neural Inform Proc Syst 35:11450–11466

Argyriou A, Evgeniou T, Pontil M (2006) Multi-task feature learning. In: Schölkopf B, Platt J, Hoffman T (eds.) Advances in neural information processing systems, vol. 19. MIT Press, Cambridge. https://doi.org/10.1145/1835804.1835953

Thung KH, Wee CY (2018) A brief review on multi-task learning. Multimed Tools Appl 77:29705–29725. https://doi.org/10.1007/s11042-018-6463-x

Weiss K, Khoshgoftaar TM, Background DW (2016) A survey of transfer learning. J Big Data. https://doi.org/10.1186/s40537-016-0043-6

Ke GY, Pan Y, Yin J, Huang CQ (2017) Optimizing evaluation metrics for multitask learning via the alternating direction method of multipliers. IEEE Trans Cybern 48(3):993–1006

Idriss I, Azizi M, Moussaoui O (2020) IoT security with deep learning-based intrusion detection systems: a systematic literature review. In: 2020 Fourth international conference on intelligent computing in data sciences (ICDS), IEEE, pp 1–10

Catal C, Giray G, Tekinerdogan B (2022) Applications of deep learning for mobile malware detection: a systematic literature review. Neural Comput Appl 34(2):1007–1032

Wang Z, Liu Q, Chi Y (2020) Review of android malware detection based on deep learning. IEEE Access 8:181102–181126. https://doi.org/10.1109/ACCESS.2020.3028370

Andročec D, Vrček N (2019) Machine learning for the internet of things security: a systematic review. SciTePress, Portugal, pp 563–570

Kitchenham B, Pretorius R, Budgen D, Pearl Brereton O, Turner M, Niazi M, Linkman S (2010) Systematic literature reviews in software engineering - a tertiary study. Inf Softw Technol 52(8):792–805. https://doi.org/10.1016/j.infsof.2010.03.006

Takiddin A, Atat R, Ismail M, Davis K, Serpedin E (2023) A graph neural network multi-task learning-based approach for detection and localization of cyberattacks in smart grids. Institute of Electrical and Electronics Engineers (IEEE), New Jersey, pp 1–5

Albelwi SA (2022) An intrusion detection system for identifying simultaneous attacks using multi-task learning and deep learning. In: 2022 2nd International conference on computing and information technology (ICCIT), IEEE, pp 349–353

Hamdan S, Almajali S, Ayyash M, Salameh HB, Jararweh Y (2023) An intelligent edge-enabled distributed multi-task learning architecture for large-scale iot-based cyber-physical systems. Simul Modell Pract Theory. https://doi.org/10.1016/j.simpat.2022.102685

Liu Q, Wang D, Jia Y, Luo S, Wang C (2022) A multi-task based deep learning approach for intrusion detection. Knowl Based Syst. https://doi.org/10.1016/j.knosys.2021.107852

Ali S, Abusabha O, Ali F, Imran M, Abuhmed T (2023) Effective multitask deep learning for iot malware detection and identification using behavioral traffic analysis. IEEE Trans Netw Serv Manag 20:1199–1209. https://doi.org/10.1109/TNSM.2022.3200741

Bader O, Lichy A, Hajaj C, Dubin R, Dvir A (2022) MalDIST: from encrypted traffic classification to malware traffic detection and classification. In: 2022 IEEE 19th annual consumer communications & networking conference (CCNC), IEEE, pp 527–533

Lan J, Liu X, Li B, Sun J, Li B, Zhao J (2022) Member: a multi-task learning model with hybrid deep features for network intrusion detection. Comput Secur. https://doi.org/10.1016/j.cose.2022.102919

Maity K, Sen T, Saha S, Bhattacharyya P (2022) Mtbullygnn: a graph neural network-based multitask framework for cyberbullying detection. IEEE Trans Comput Soc Syst. https://doi.org/10.1109/tcss.2022.3230974

Bensaoud A, Kalita J (2022) Deep multi-task learning for malware image classification. J Inf Secur Appl 64:103057

Aceto G, Ciuonzo D, Montieri A, Pescapé A (2021) Distiller: encrypted traffic classification via multimodal multitask deep learning. J Netw Comput Appl. https://doi.org/10.1016/j.jnca.2021.102985

Lee SH, Lan SC, Huang HC, Hsu CW, Chen YS, Shieh S (2021) Ec-model: an evolvable malware classification model. In 2021 IEEE Conference on dependable and secure computing (DSC), IEEE, pp 1–8

Barut O, Luo Y, Zhang T, Li W, Li P (2021) Multi-task hierarchical learning based network traffic analytics. In: ICC 2021-IEEE International conference on communications, IEEE, pp 1–6

Kapil P, Ekbal A (2020) A deep neural network based multi-task learning approach to hate speech detection. Knowl Based Syst. https://doi.org/10.1016/j.knosys.2020.106458

Wang S, Wang Q, Jiang Z, Wang X, Jing R (2020) A weak coupling of semi-supervised learning with generative adversarial networks for malware classification. Institute of Electrical and Electronics Engineers Inc., New Jersey, pp 3775–3782

Rezaei S, Liu X (2020) Multitask learning for network traffic classification. In: 2020 29th International Conference on computer communications and networks (ICCCN), IEEE, pp 1–9

Sun L, Zhou Y, Wang Y, Zhu C, Zhang W (2020) The effective methods for intrusion detection with limited network attack data: multi-task learning and oversampling. IEEE Access 8:185384–185398. https://doi.org/10.1109/ACCESS.2020.3029100

Li X, Zhu L, Yu Z, Guo B, Wan Y (2020) Vanishing point detection and rail segmentation based on deep multi-task learning. IEEE Access 8:163015–163025. https://doi.org/10.1109/ACCESS.2020.3019318

Hu T, Guo Q, Shen X, Sun H, Wu R, Xi H (2019) Utilizing unlabeled data to detect electricity fraud in ami: a semisupervised deep learning approach. IEEE Trans Neural Netw Learn Syst 30:3287–3299. https://doi.org/10.1109/TNNLS.2018.2890663

Nguyen HH, Fang F, Yamagishi J, Echizen I (2019) Multi-task learning for detecting and segmenting manipulated facial images and videos. In: 2019 IEEE 10th international conference on biometrics theory, applications and systems (BTAS), IEEE, pp 1–8

Zhao Y, Chen J, Wu D, Teng J, Yu S (2019) Multi-task network anomaly detection using federated learning. Association for Computing Machinery, New York, pp 273–279

Li J, Sun M, Zhang X (2019) Multi-task learning of deep neural networks for joint automatic speaker verification and spoofing detection. In: 2019 Asia-pacific signal and information processing association annual summit and conference ASC, IEEE, pp 1517–1522

Huang H, Deng H, Chen J, Han L, Wang W (2018) Automatic multi-task learning system for abnormal network traffic detection. Int J Emerg Technol Learn 13:4–20. https://doi.org/10.3991/ijet.v13i04.8466

Demertzis K, Iliadis L, Anezakis VD (2018) MOLESTRA: a multi-task learning approach for real-time big data analytics. In: 2018 Innovations in intelligent systems and applications (INISTA), IEEE, pp 1–8

Li B, Lin Y, Zhang S (2017) Multi-task learning for intrusion detection on web logs. J Syst Archit 81:92–100. https://doi.org/10.1016/j.sysarc.2017.10.011

Yu J, Zhang B, Kuang Z, Lin D, Fan J (2017) Iprivacy: image privacy protection by identifying sensitive objects via deep multi-task learning. IEEE Trans Inf Forensics Secur 12:1005–1016. https://doi.org/10.1109/TIFS.2016.2636090

Moustafa N, Slay J (2015) Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In: 2015 military communications and information systems conference (MilCIS), pp 1–6. https://doi.org/10.1109/MilCIS.2015.7348942

IDS 2017 | Datasets | Research | Canadian Institute for Cybersecurity | UNB. https://www.unb.ca/cic/datasets/ids-2017.html

IDS 2018 | Datasets | Research | Canadian Institute for Cybersecurity | UNB. https://www.unb.ca/cic/datasets/ids-2018.html

Garcia S, Gomaa A, Babayeva K. Slips, Behavioral machine learning-based Python IPS. Available at: https://github.com/stratosphereips/StratosphereLinuxIPS/tree/master . Accessed 13 Mar 2024

VPN 2016 | Datasets | Research | Canadian Institute for Cybersecurity | UNB. https://www.unb.ca/cic/datasets/vpn.html

Tor 2016 | Datasets | Research | Canadian Institute for Cybersecurity | UNB. https://www.unb.ca/cic/datasets/tor.html

IDS 2012 | Datasets | Research | Canadian Institute for Cybersecurity | UNB. https://www.unb.ca/cic/datasets/ids.html

The Bot-IoT Dataset | UNSW Research. https://research.unsw.edu.au/projects/bot-iot-dataset

NSL-KDD | Datasets | Research | Canadian Institute for Cybersecurity | UNB. https://www.unb.ca/cic/datasets/nsl.html

The CTU-13 Dataset. A Labeled Dataset with Botnet, Normal and Background traffic. - Stratosphere IPS. https://www.stratosphereips.org/datasets-ctu13

CESNET-QUIC22: a large one-month QUIC network traffic dataset from backbone lines. https://zenodo.org/records/7409924

IoT-23 Dataset: a labeled dataset of Malware and Benign IoT Traffic. - Stratosphere IPS. https://www.stratosphereips.org/datasets-iot23

Janiszewski M, Felkner A, Lewandowski P, Rytel M, Romanowski H (2021) Automatic actionable information processing and trust management towards safer internet of things. Sensors. https://doi.org/10.3390/s21134359

Malware-traffic-analysis.net. https://malware-traffic-analysis.net/

Ronen R (2018) Microsoft malware classification challenge. arXiv preprint arXiv:1802.10135

Davidson T, Warmsley D, Macy M, Weber I (2017) Automated hate speech detection and the problem of offensive language. In: Proceedings of the international AAAI conference on web and social media, vol. 11, pp 512–515

Waseem Z, Hovy D (2016) Hateful symbols or hateful people? Predictive features for hate speech detection on twitter. In: Proceedings of the NAACL student research workshop, pp 88–93

Kumar R, Reganti AN, Bhatia A, Maheshwari T (2018) Aggression-annotated corpus of hindi-english code-mixed data. arXiv preprint arXiv:1803.09402

Zampieri M, Malmasi S, Nakov P, Rosenthal S, Farra N, Kumar R (2019) Predicting the type and target of offensive posts in social media. arXiv preprint arXiv:1902.09666

Golbeck J, Ashktorab Z, Banjo RO, Berlinger A, Bhagwan S, Buntain C, Cheakalos P, Geller AA, Gnanasekaran RK, Gunasekaran RR et al (2017) A large labeled corpus for online harassment research. In: Proceedings of the 2017 ACM on web science conference, pp 229–233

Maity K, Saha S (2021) BERT-capsule model for cyberbullying detection in code-mixed Indian Languages, pp 147–155. https://doi.org/10.1007/978-3-030-80599-9_13

Rössler A, Cozzolino D, Verdoliva L, Riess C, Thies J, Nießner M (2018) Faceforensics: a large-scale video dataset for forgery detection in human faces. arxiv 2018. arXiv preprint arXiv:1803.09179

Rossler A, Cozzolino D, Verdoliva L, Riess C, Thies J, Nießner M (2019) Faceforensics++: learning to detect manipulated facial images. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 1–11

| ASVspoof. https://www.asvspoof.org/index2017.html

Download references

Acknowledgements

This publication was supported by Qatar University Internal Grant No. QUUG-CENG-CSE-2022. The findings achieved herein are solely the responsibility of the authors.

Open Access funding provided by the Qatar National Library. Open access funding provided by the Qatar National Library.

Author information

Authors and affiliations.

Department of Computer Science and Engineering, Qatar University, 2713, Doha, Qatar

Shimaa Ibrahim & Cagatay Catal

Department of Computer Science and Information Technology, University of the District of Columbia, Washington, DC, 20008, USA

Thabet Kacem

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Cagatay Catal .

Ethics declarations

Conflict of interest.

Authors declare no conflict of interest.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Ibrahim, S., Catal, C. & Kacem, T. The use of multi-task learning in cybersecurity applications: a systematic literature review. Neural Comput & Applic (2024). https://doi.org/10.1007/s00521-024-10436-3

Download citation

Received : 21 December 2023

Accepted : 03 September 2024

Published : 27 September 2024

DOI : https://doi.org/10.1007/s00521-024-10436-3

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Cybersecurity
• Multi-task learning
• Cyber threats
• Deep learning
• Find a journal
• Publish with us
• Track your research

• Open access
• Published: 27 September 2024

Narrative Medicine: theory, clinical practice and education - a scoping review

• Ilaria Palla 1 ,
• Giuseppe Turchetti 1 &
• Stefania Polvani 2 , 3  

BMC Health Services Research volume  24 , Article number:  1116 ( 2024 ) Cite this article

Metrics details

The origin of Narrative Medicine dates back to more than 20 years ago at an international level. Narrative Medicine is not an alternative to evidence-based medicine, however these two approaches are integrated. Narrative Medicine is a methodology based on specific communication skills where storytelling is a fundamental tool to acquire, understand and integrate several points of view related to persons involving in the disease and in the healthcare process. Narrative Medicine, henceforth NM, represents a union between disease and illness between the doctor’s clinical knowledge and the patient’s experience. According to Byron Good, “we cannot have direct access to the experience of others’ illness , not even through in-depth investigations: one of the ways in which we can learn more from the experience of others is to listen to the stories of what has happened to other people.” Several studies have been published on NM; however, to the best of our knowledge, no scoping review of the literature has been performed.

This paper aims to map and synthetize studies on NM according to theory, clinical practice and education/training.

The scoping review was carried out in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) checklist. A search was conducted in PubMed, APA PsycNet and Jstor. Two authors independently assessed the eligibility and methodological quality of the studies and extracted the data. This review refers to the period from 1998 to 2022.

A total of 843 abstracts were identified of which 274 papers were selected based on the title/abstract. A total of 152 papers in full text were evaluated and 76 were included in the review. Papers were classified according to three issues:

✘ Nineteen studies focused on the definition and concept of NM (Theoretical).

✘ Thirty-eight papers focused on the collection of stories, projects and case reports (Clinical practice).

✘ Nineteen papers focused on the implementation of the Narrative Medicine approach in the education and training of medical doctors (Education and training).

Conclusions

This scoping review presents an overview of the state of the art of the Narrative Medicine. It collect studies performed mainly in Italy and in the United States as these are the countries developing the Narrative Medicine approach in three identified areas, theoretical, clinical practice and education and training. This scoping review will help to promote the power of Narrative Medicine in all three areas supporting the development of methods to evaluate and to measure the Narrative Medicine approach using key performance indicators.

Peer Review reports

Introduction

The role and involvement of patients in healthcare have changed, as has their relationship with healthcare professionals. The patient is no longer a passive subject but part of the healthcare process. Over the years, many approaches to patients’ involvement in healthcare have been developed in the literature, with significant differences in terms of concept and significance.

NM represents a focus on the patient’s needs and the empowerment of their active participation in the healthcare process.

Narrative Medicine enables patients to share their stories with healthcare professionals so that the latter can gain the necessary skills to recognize, interpret and relate to patients [ 1 ]. Stories of illness have an important impact on patients and their caregivers, healthcare professionals and organisational systems [ 2 ].

Trisha Greenhalgh, an academic in primary healthcare who trained as a General Practitioner, and Brian Hurwitz, an Emeritus Professor of Medicine and The Arts at King’s College (London) [ 3 , 4 ], affirmed that the core clinical skills in terms of listening, questioning, outlining, collecting, explaining and interpreting can provide a way of navigating among the very different worlds of patients and health professionals. These tasks need to be performed well because they can affect disease outcomes from the patient’s perspective and the scientific aspects of diagnosis and treatment.

In 2013, Rita Charon, a general internist and professor at Columbia University (New York), and Brian Hurwitz promoted “a narrative future for healthcare” , the first global conference on Narrative Based Medicine (NBM). The global conference took place in London in June 2013, where experts in humanities, social sciences and professionals interested in shaping a narrative future for healthcare discussed several topics, such as increasing the visibility of narrative-based concepts and methods; developing strategies that can influence traditional clinical institutions; spreading appreciation for the role of creativity in caring for the sick; articulating the risks of narrative practices in health care; providing a space for Narrative Medicine in the context of other fields, including personalized medicine; and sharing goals for training, research, and clinical care. The conference was the first important opportunity to share different points of view and perspectives at the global level involving several stakeholders with different backgrounds [ 5 ].

In the early 2000s, the first Italian experience of Narrative Medicine occurred in Florence with NaMe, a project endorsed by the Local Health Authority aimed at diffusing the culture of patient-centered medicine and integrating strategies to improve doctor‒patient communication in clinical practice [ 6 ]. This project was inspired by the articles of Hurwitz and Greenhalgh [ 3 , 4 ]. In addition, significant input was derived from Arthur Kleinman [ 7 ] and Byron Good [ 8 ], psychiatrists and anthropologists who studied medicine as a cultural system, as a set of symbolic meanings involving the story of the sick person. Health and illness represent the subjective experience of the person.

Kleinmann [ 7 ] defines three dimensions to explain the illness using three different significances:

✘ Disease: “only as an alteration in biological structure or functioning” .

✘ Illness: the subjective experience of suffering and discomfort.

✘ Sickness: the social representation.

Narrative Medicine can be used in several areas such as prevention, diagnosis, treatment, and rehabilitation; adherence to treatment; organization of the care team; awareness of the professional role and the emotional world by health and social workers; prevention of the burnout of professionals and caregivers; promotion and implementation of Patient Care Pathways (PCPs); and prevention of legal disputes and defensive medicine.

The Italian guidelines established by the National Institute of Health in 2015 [ 9 ] represent a fundamental step in the process of diffusion and implementation of Narrative Medicine in Italy and currently represents the only document. The guidelines define Narrative Medicine as an intervention methodology based on specific communication skills. Storytelling is a fundamental instrument for acquiring, understanding and integrating the different perspectives of those involved in the disease and in the healthcare process. Storytelling represents a moment of contact between a healthcare professional and the patient’s world. The story told involves people, those who narrate and those who listen. Telling stories is a way of transferring knowledge and experience, connecting, reflecting and feeling emotions.

In the last few years, several studies have been carried out with different objectives and perspectives, but no literature review on Medicine Narrative has been performed. We founded the study of Rui et al. [ 10 ] performing a bibliometric analysis of the literature on medical narratives published from 2011 to 2021 showing that the field of narrative medicine is dominated by a few countries. Respect to 736 studies included in the review, 48% (369) are performed in US and 98 papers in Italy.

The objective of scoping review was to map and synthetize studies on NM according to theory, clinical practice and education/training, three settings where NM was developed.

The research questions formulated: (1) What is Narrative Medicine?; (2) How is Narrative Medicine implemented in clinical practice?; (3) What is the role of Narrative Medicine in education and training for medical doctors?

The study protocol follows the PRISMA-ScR checklist (PRISMA extension for Scoping Reviews) but it is not registered (Additional file 1).

We included peer-reviewed papers published from 1998 to December 2022 written in Italian or in English. We excluded papers written in other languages. We included articles according to one of these issues: studies on theory of Narrative Medicine, on clinical practice or education/training of Narrative Medicine. We excluded books, case reports, reviews. To identify potentially relevant studies, the following databases were searched from 1998 to December 2022: PubMed, APA PsycNet and Jstor. The search strategy can be founded in Additional file 2. A data charting form was developed by two reviewers to define which variables can be extracted. The reviewers independently charted the data and discussed the results. We grouped the studies by type of application related to the Narrative Medicine and summarized objective, methods and reflections/conclusions. The scoping review maps the evidence on Narrative Medicine according one of the three fields of diffusion and implementation (Fig.  1 ). Furthermore, the studies classified in “theoretical field “are grouped in subcategories to explain in best way the concepts and permit a clearer and more streamlined reading.

Categories of Narrative Medicine

Review process

After removing duplicates, 843 abstracts from PubMed, Jstor and APA PsycNet were screened. A total of 274 papers were screened based on the abstracts, of which 122 were excluded. A total of 152 full texts were evaluated, and 76 were included in the review (Fig.  2 ).

PRISMA Flow-chart

The studies included were classified into the three fields where the Narrative Medicine is implemented:

✘ Theoretical studies: 19.

✘ Clinical Practice: 38.

✘ Education and training: 19.

The scoping review did not present the results of papers included but the main objectives and the methods used as the aim of the scoping review was to map the studies performed in terms of theory, clinical practice and education/training. We have tried to organize the studies published so far, making it increasingly clear how Narrative Medicine has developed.

Theoretical studies

This section presents the 19 selected theoretical studies grouped into subcategories (Additional file 3).

Narrative Medicine: advantages

In this section, we present seven papers that highlight the benefits of narrative medicine.

Of the seven papers considered, four were performed by Rita Charon emphasizing the value of Narrative Medicine in four different contexts. In the first [ 11 ], the study by Goupy et al. evaluated a Narrative Medicine elective course at the Paris-Descartes School of Medicine. In the second [ 12 ], Charon rewrote a patient’s family illness to demonstrate how medicine that respects the narrative dimension of illness and care can improve the care of individual patients, their colleagues and effective medical practice. The third paper [ 13 ] describes a visit to the Rothko Room at the Tate Modern in London as a pretext to emphasize how for narrative medicine, creativity is at the heart of health care and that the care of the sick is a work of art.

In the fourth [ 14 ], Charon provides the elements of narrative theory through a careful reading of the form and content of an excerpt from a medical record. This is part of an audio-recorded interview with a medical student and a reflection on a short section of a modernist novel to show how to determine the significance of patients’ situations.

According to Abettan [ 15 ], Narrative Medicine can play a key role in the reform of current medical practice, although to date, there has been little focus on how and why it can deliver results and be cost-effective.

Cenci [ 16 ] underlines that the existential objective of the patient is fundamental to know the person’s life project and how they would like to live their future years.

Zaharias [ 17 ], whose main sources are Charon and Launer, has published three articles on NM as a valid approach that, if practiced more widely by general practitioners, could significantly benefit both patients and doctors. If the patient’s condition is central, the NM shifts the doctor’s focus from the need to solve the problem to the need to understand. Consequently, the patient‒physician relationship is strengthened, and patients’ needs and concerns are addressed more effectively and with better results.

Narrative Medicine: the role of digital technologies

This section includes 3 papers on the role of digital technologies in Narrative Medicine. Digital narrative medicine is diffusing in care relationship as presents an opportunity for the patient and the clinician. The patient has more time to reflect on his/her needs and communicate in best way with the healthcare professionals. The clinician can access to more information as quantitative and qualitative information and data provided by the patient. These information represent an instrument for the clinician to personalize the care and respond to patient’s unmet needs.

The use of digital technologies, particularly the digital health storymap tool described by Cenci [ 16 ], for obtaining a multidisciplinary understanding of the patient’s medical history facilitates communication between the patient and caregiver. According to Charon [ 18 ], the relentless specialization and technologization of medicine damages the therapeutic importance of recognizing the context of patients’ lives and witnessing their suffering.

Rosti [ 19 ] affirms that e-health technologies will build new bridges and permit professionals to have more time to use narrative techniques with patients.

The increased use of digital technologies could reduce the opportunity for narrative contact but provide a starting point for discussion through the use of electronically transmitted patient pain diaries.

Narrative Medicine: integration with evidence-based medicine

Greenhalgh’s [ 20 ] and Rosti’s [ 19 ] studies address one of the most significant issues, the integration of Narrative Medicine with Evidence Based Medicine. Narrative Medicine is not an alternative to Evidence Based Medicine, they coexist and can complement each other in clinical practice.

Greenhalgh’s work [ 20 ] clearly shows how NM and EBM can be integrated. EBM requires an interpretative paradigm in which the patient experiences the disease in a unique and contextual way and the clinician can draw on all aspects of the evidence and thus arrive at an integrated clinical judgement.

Rosti [ 19 ] believes that even “evidence-based” physicians sustain the importance of competence and clinical judgement. Clinicians also need to rely on patients’ narratives to integrate more objective clinical results. Clinical methods are not without their limitations, which Narrative Medicine can help to overcome. Lederman [ 21 ] enphatises the importance of social sciences to analyze the stories and to improve the care.

Narrative-based Medicine: insidious

Three papers in this section focus on the possible risks of the Narrative Medicine approach. It is needing a more awareness on role of Narrative Medicine as a robust methodology.

The study by Kalitzus [ 22 ] shows how a narrative approach in medicine will be successful only if it has a positive effect on daily clinical practice instead of merely increasing existing problems.

Complex narratives on diseases published in biographies or collected by social scientists are useful only for training and research purposes. NM requires time and effort and cannot be considered the only important issue in medicine. According to Abettan [ 15 ], Narrative Medicine can make the treatment more personalised for each patient, but it is not the only way.

Zaharias [ 17 ] affirms that Narrative Medicine is often described simplistically as listening to the patient’s story, whereas it is much more common and requires special communication skills. Perhaps for these reasons, and despite its advantages, NM is not as widely practiced as it could be. Narrative skills are an integral part of practice and learning them takes time. As the author also states, “the healing power of storytelling is repeatedly attested to while evidence of effectiveness is scarce”. Lanphier [ 23 ] underlines the need to explain the term "narrative medicine" to avoid misunderstandings and to analyze the use of narrative as a tool.

Narrative Medicine: training

Liao et al. [ 24 ] presented a study aimed at helping students improve their relationships with patients by listening to them. These results, similar to those described by Charon [ 25 ], suggest that Narrative Medicine is worth recommending in academic training. The essay by O’Mahony [ 26 ] aims to provoke a debate on how and what the medical humanities should teach. Narratology and narrative medicine are linked to empathy.

Narrative Medicine: clinician-patient communication

Papers included within this category focus on the relationship between the clinician and patient, which is important in the healthcare context.

American healthcare institutions recognize the use of the Narrative Medicine approach to develop quality patient care. As a gastroenterologist at a health centre in Minnesota (US), Rian [ 27 ] concluded that the practice of Narrative Medicine should not be kept on the fringes of medicine as a hobby or ancillary treatment for the benefit of the patients but should be considered key to the healthcare process. Improving doctor‒patient communication merits more attention.

According to Rosti [ 19 ], NM can be seen as a tool to promote better communication. Although time constraints are often mentioned as an obstacle, the time needed to listen to patients is not excessive, and all healthcare professionals should consider giving patients more freedom from time constraints during consultations by encouraging them to talk about their experiences. The use of NM may also be associated with better diagnosis and treatment of pain.

Zaharias [ 28 ] underlines that communication skills are crucial. General practitioners can further develop the strong communication skills they already possess by practicing NM through neutrality, circular questions and hypotheses, and reflective skills.

Narrative Medicine: bioethics in qualitative research

The use of qualitative research in bioethics and narrative approaches to conducting and analysing qualitative interviews are becoming increasingly widespread. As Roest [ 29 ] states, this approach enables more “diagnostic thinking”. It is about promoting listening skills and the careful reading of people and healthcare practices, as well as quality criteria for the ethical evaluation of research and training.

• Clinical practice

In this classification, we included case studies performed in clinical care. We focused on methods used to guide the patients’ stories or narratives written by healthcare professionals. We analysed how Narrative Medicine has been implemented in clinical healthcare practice.

The studies included (38) were performed in the following countries: Italy (28), USA (4), Australia (1), Canada (1), China (1), Colombia (1), Norway (1), and several European countries (1) (Table  1 ). The main methods used were semi-structured interviews that guided the patient’s and physician’s narration [ 30 , 31 , 32 , 33 ], narrative diaries written by patients [ 34 ], and paper parallel charts (an instrument to integrate the patients’ stories in clinical practice) written by clinicians [ 34 , 35 , 36 ].

The studies underlined the usefulness of narrative medicine not only in qualitative research but also in integration with quantitative analysis. Gargiulo et al. [ 45 ] highlighted the importance of integrating narrative medicine and evidence-based approaches to improve therapeutic effectiveness and organizational pathways. Cappuccio et al. [ 36 ] affirmed that narrative medicine can be effective in supporting clinicians in their relationships with patients and caregivers.

Narrative Medicine is an important instrument for patients, caregivers and healthcare professionals [ 63 ]. Suter et al. [ 60 ] affirmed that patients’ stories can help other patients with similar experiences. The studies performed by Cercato [ 39 , 40 ] and Zocher [ 67 ] highlighted the role of digital diaries in the care process from the perspective of healthcare professionals and patients. Sansone et al. [ 55 ] highlighted that the use of diaries in the intensive care unit is helpful in facilitating communication between healthcare professionals and the family.

Education and training

This section includes studies on the role of Narrative Medicine in the education and training of medical students and healthcare professionals. The studies discuss the experiences, roles and programmes of the Narrative Medicine programme in education and training. Nineteen studies were carried out, 10 of which were in the USA (Table  2 ). Only two studies were carried out in Europe, 4 in Taiwan, 1 in Canada, 1 in Iran and 1 in Israel. Seven studies focused on the role of narrative medicine for healthcare professionals [ 68 , 69 , 70 , 71 , 72 , 73 , 74 ], and 11 were aimed at medical students from different disciplines. All studies underlined the positive role of Narrative Medicine in training. Chou et al. [ 75 ] affirmed that the new model of narrative medicine training, “community-based participatory narrative medicine”, which focuses on shared narrative work between healthcare trainees and patients, facilitates the formation of therapeutic patient-clinician relationships but also creates new opportunities to evaluate those relationships. Darayazadeh et al. [ 70 ] underlined the effectiveness of Narrative Medicine in improving students’ reflections and empathy with patients. Additionally, Lam et al. [ 76 ] highlighted that Narrative Medicine could be a useful tool for improving clinical empathy skills. The studies used different approaches to implement the Narrative Medicine method. Arntfield et al. [ 77 ] proposed three tools at different steps of the study (survey, focus group and open-ended questions). Chou et al. [ 75 ] asked participants to write a personal narrative. DasGupta and Charon [ 78 ] used a reflective writing exercise to analyse personal experiences of illness.

In this scoping review we identified 76 studies addressing dissemination and implementation of Narrative Medicine across three settings between 1998 and 2022. The studies performed by Hurwitz [ 3 ] and Greenhalgh [ 4 ] provide a path towards the Narrative Medicine affirm that sickness episodes are important milestones in patient life stories. Not only we live through storytelling, but often, with our doctor or nurse as a witness, we get sick, we improve, we get worse, we are stable and finally we also die through the story. affirms that the stories are often evocative and memorable. They are image rich, action packed and laden with emotions. Most people recall them better than they recall lists, graphs or numbers. Stories can convey important elements of nuance, including mood, tone and urgency. We learn through stories because the story form allows our existing schemas to be modified in the light of emerging experiential knowledge. The stories can capture tacit knowledge: in healthcare organizations they can bridge the gap between explicit, codified and formal knowledge (job descriptions, guidelines and protocols) and informal, not codified knowledge (knowing how to get things done in a particular organization or team, sometimes referred to as knowing the ropes). The “story” is the focal point in the studies related to the clinical practice as these discuss about the patient’s experience, illness story thought tools as questionnaires, narrative diary, chart parallels. The patient is an expert patient able to interact with the healthcare professionals, he/she had not a passive role; the patient is part of the process with the other involved stakeholders. Also, the Italian guidelines on Narrative Medicine [ 9 ] considers the storytelling as a fundamental instrument to acquire, understand and integrate several points of view related to persons involving in the disease and in the healthcare process. Storytelling represents the interaction between a healthcare professional and the patient’s world. According to this perspective, it is useful to educate in Narrative Medicine the healthcare professionals from the University to provide instruments to communicate and interact with their patients. Charon [ 11 ] emphasizes the role of training in narrative skills as an important tool permitting to physicians and medical students to improve their care. Charon [ 24 ] underlines that narrative training permits to explore the clinician’s attention to patients and to establish a relationship with patients, colleagues, and the self. The study of Liao [ 22 ] underlines that Narrative Medicine is worth recommending for healthcare education as resource for interdisciplinary collaboration among students from different discipline.

John Launer in The Art of Medicine. Narrative medicine , narrative practice , and the creation of meaning (2023) [ 87 ] affirm that Narrative Medicine could be complemented by the skills and pedagogy of narrative practice. In addition to the creation and study of words on the page, learners could bring their spoken accounts of their experiences at work and interview each other using narrative practice techniques. He also affirms that narrative practice and narrative medicine could both do more to build alliances with advocacy groups.

We have performed a picture of Narrative Medicine from its origin to today hoping that it will help to promote the power of Narrative Medicine in all three areas becoming increasingly integrated.

Strengths and limitations

The scoping review does not present the results of studies included but objectives, methodology and conclusions/suggestions as it aims to map the evidence related to the Narrative Medicine using a classification defined for the review. This classification had permit to make even clearer the “world” of Narrative Medicine and present a mapping.

English- and Italian-language articles were included because, as seen from the preceding pages, most of the studies were carried out in the United States and Italy.

This could be a limitation, as we may have excluded papers written in other languages. However, the United States and Italy are the countries where Narrative Medicine has developed the most.

The scoping review presents an overview of the literature considering three settings in which Narrative Medicine has emerged from its origins until today highlighting evidence in terms of theory, clinical practice, and education. Currently, a methodology to “measure” Narrative Medicine with indicators, a method assessing the effectiveness and promoting a greater diffusion of Narrative Medicine using objective and measurable indicators, is not available. Furthermore, the literature analysis doesn’t show an integration across three settings. We hope that the review will be a first step towards future projects in which it will be possible to measure Narrative Medicine according to an integrated approach between clinical practice and education/training.

Availability of data and materials

Availability of data and materials: All data generated or analysed during this study are included in this published article.

Abbreviations

• Narrative Medicine

Narrative-Based Medicine

Evidence-Based Medicine

Polvani S. Cura alle stelle. Manuale di salute narrativa. Bulgarini; 2022.

Google Scholar  

Polvani S, Sarti A. Medicina narrativa in terapia intensiva. Storie di Malattia e di cura. FrancoAngeli; 2013.

Greenhalgh T, Hurwitz B. Narrative based medicine Why study narrative? BMJ. 1999;318:48–50.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Greenhalgh T, Hurwitz B. Narrative-Based Medicine: Dialogue and Discourse in Clinical Practice. London: BMJ Books; 1998.

Hurwitz B, Charon R. A narrative future for health care. Lancet. 2013;381:1886–7.

Article   PubMed   PubMed Central   Google Scholar  

Ballo P, Milli M, Slater C, Bandini F, Trentanove F, Comper G, Zuppiroli A, Polvani S. Prospective Validation of the Decalogue, a Set of Doctor-Patient Communication Recommendations to Improve Patient Illness Experience and Mood States within a Hospital Cardiologic Ambulatory Setting. Biomed Res Int. 2017. https://doi.org/10.1155/2017/2792131 .

Kleinman A. The Illness Narratives: Suffering, Healing, and the Human Condition. New York: Basic Books; 1988.

Good BJ. Medicine, Rationality, and Experience: An anthropological perspective. Cambridge: Cambridge University Press; 1994.

Book   Google Scholar  

Istituto Superiore di Sanità, Linee di indirizzo per l’utilizzo della Medicina Narrativa in ambito clinico-assistenziale, per le malattie rare e cronico-degenerative. Sole24Ore Sanità. 2015.

Rui L. Wang L. Global Trends and Hotspots in Narrative Medicine Studies: A Bibliometric Analysis. 2023. https://doi.org/10.21203/rs.3.rs-2816041/v1 .

Article   Google Scholar  

Charon R. Narrative medicine in the international education of physicians. Presse Med. 2013;42(1):3–5.

Article   PubMed   Google Scholar  

Charon R. Narrative Medicine. A model for empathy, Reflection, profession and Trust. JAMA. 2001;286(15):1897–902.

Article   CAS   PubMed   Google Scholar  

Charon R. Narrative Medicine: Caring for the sick is a work of art. JAAPA. 2013;26(12):8.

Charon R. The membranes of care: stories in Narrative Medicine. Acad Med. 2012;87(3):342–7.

Abettan C. From method to hermeneutics: which epistemological framework for narrative medicine? Theor Med Bioeth. 2017;38:179–93.

Cenci C, Fatati G. Conversazioni online per comprendere la malattia e favorire il rapporto medico-paziente. Recenti Prog Med. 2020;111:682–4.

PubMed   Google Scholar  

Zaharias G. What is narrative-based medicine? Narrative-based medicine 1. Canadian Family Physician|Le Médecin de famille canadien. 2018;64:176–80.

PubMed   PubMed Central   Google Scholar  

Charon R. Form Function, and Ethics. Ann Intern Med. 2001;134:83–7.

Rosti G. Role of narrative-based medicine in proper patient assessment. Support Care Cancer. 2017;25(Suppl 1):3–6.

Greenhalgh T. Narrative based medicine in an evidence-based word. BMJ. 1999;318:323–5.

Lederman M. Social and gendered readings of illness narratives. J Med Humanit. 2016;37:275–88.

Kalitzkus V, Matthiessen PF. Narrative-Based Medicine: Potential, Pitfalls, and Practice. Permanente J. 2009;13(1):80–6.

Lanphier E. Narrative and Medicine premises, practices, pragmatism. Perspective in Biology and Medicine. 2021;64(2):211–34.

Liao HC, Wang YH. Storytelling in Medical Education: Narrative Medicine as a Resource for Interdisciplinary Collaboration. Int J Environ Res Public Health. 2020. https://doi.org/10.3390/ijerph17041135 .

Charon R. Close Reading and Creative Writing in Clinical Education: teaching attention, representation, and affiliation. Acad Med. 2016;91(3):345–50.

O’Mahony S. Against Narrative Medicine. Perspect Biol Med. 2013;56(4):611–9.

Rian J, Hammer R. The Practical Application of Narrative Medicine at Mayo Clinic: Imagining the Scaffold of a Worthy House. Cult Med Psychiatry. 2013;37:670–80.

Zaharias G. Narrative-based medicine and the general practice consultation. Narrative-based medicine 2. Canadian Family Physician|Le Médecin de famille canadien. 2018;64(4):286–90.

Roest B, Milota M, Carlo LC. Developing new ways to listen: the value of narrative approaches in empirical (bio)ethics. BMC Med Ethics. 2021. https://doi.org/10.1186/s12910-021-00691-7 .

Breccia M, Graffigna G, Galimberti S, Iurlo A, Pungolino E, Pizzuti M, Maggi A, et al. Personal history and quality of life in chronic myeloid leukemia patients: a cross-sectional study using narrative medicine and quantitative analysis. Support Care Cancer. 2016;24(11):4487–93.

Cappuccio A, Limonta T, Parodi A, Cristaudo A, Bugliaro F, Cannavò SP, Rossi O. Living with Chronic Spontaneous Urticaria in Italy: A Narrative Medicine Project to Improve the Pathway of Patient Care. Acta Derm Venereol. 2017;97(1):81–5.

Ceccarelli F, Covelli V, Olivieri G, Natalucci F, Conti F. Systemic Lupus Erythematosus before and after COVID-19 Lockdown: How the Perception of Disease Changes through the Lenses of Narrative Medicine. Healthcare (Basel). 2021. https://doi.org/10.3390/healthcare9060726 .

Cenci C, Mecarelli O. Digital narrative medicine for the personalization of epilepsy care pathways. Epilepsy Behav. 2020. https://doi.org/10.1016/j.yebeh.2020.107143 .

Banfi P, Cappuccio A, Latella M, Reale L, Muscianisi E, Marini MG. Narrative medicine to improve the management and quality of life of patients with COPD: the first experience applying parallel chart in Italy. Int J Chron Obstruct Pulmon Dis. 2018;13:287–97.

Cappuccio A, Sanduzzi Zamparelli A, Verga M, Nardini S, Policreti A, Porpiglia PA, Napolitano S, Marini MG. Narrative medicine educational project to improve the care of patients with chronic obstructive pulmonary disease. ERJ Open Res. 2018. https://doi.org/10.1183/23120541.00155-2017 .

Cappuccio A, Napolitano S, Menzella F, Pellegrini G, Policreti A, Pelaia G, Porpiglia PA, Marini MG. Use of narrative medicine to identify key factors for effective doctor-patient relationships in severe asthma. Multidiscip Respir Med. 2019. https://doi.org/10.1186/s40248-019-0190-7 .

Caputo A. Exploring quality of life in Italian patients with rare disease: a computer-aided content analysis of illness stories. Psychol Health Med. 2014;19(2):211–21.

Cepeda MS, Chapman CR, Miranda N, Sanchez R, Rodriguez CH, Restrepo AE, Ferrer LM, Linares RA, Carr DB. Emotional disclosure through patient narrative may improve pain and well-being: results of a randomized controlled trial in patients with cancer pain. J Pain Symptom Manage. 2008;35(6):623–31.

Cercato MC, Colella E, Fabi A, Bertazzi I, Giardina BG, Di Ridolfi P, Mondati M, et al. Narrative medicine: feasibility of a digital narrative diary application in oncology. J Int Med Res. 2022. https://doi.org/10.1177/03000605211045507 .

Cercato MC, Vari S, Maggi G, Faltyn W, Onesti CE, Baldi J, Scotto di Uccio A et al. Narrative Medicine: A Digital Diary in the Management of Bone and Soft Tissue Sarcoma Patients. Preliminary Results of a Multidisciplinary Pilot Study. J Clin Med. 2022. https://doi.org/10.3390/jcm11020406 .

De Vincentis G, Monari F, Baldari S, Salgarello M, Frantellizzi V, Salvi E, Reale L, Napolitano S. Narrative medicine in metastatic prostate cancer reveals ways to improve patient awareness & quality of care. Future Oncol. 2018;14(27):2821–32.

Di Gangi S, Naretto G, Cravero N, Livigni S. A narrative-based study on communication by family members in intensive care unit. J Crit Care. 2013;28(4):483–9.

Donzelli G, Paddeu EM, D’Alessandro F, Nanni CA. The role of narrative medicine in pregnancy after liver transplantation. J Matern Fetal Neonatal Med. 2015;28(2):158–61.

Fox DA, Hauser JM. Exploring perception and usage of narrative medicine by physician specialty: a qualitative analysis. Philos Ethics Humanit Med. 2021. https://doi.org/10.1186/s13010-021-00106-w .

Gargiulo G, Sansone V, Rea T, Artioli G, Botti S, Continisio GI, Ferri P, et al. Narrative Based Medicine as a tool for needs assessment of patients undergoing hematopoietic stem cell transplantation. Acta Biomed. 2017;88:18–24.

Graffigna G, Cecchini I, Breccia M, Capochiani E, Della Seta R, Galimberti S, Melosi A, et al. Recovering from chronic myeloid leukemia: the patients’ perspective seen through the lens of narrative medicine. Qual Life Res. 2017;26(10):2739–54.

Herrington ER, Parker LS. Narrative methods for assessing “quality of life” in hand transplantation: five case studies with bioethical commentary. Med Health Care Philos. 2019;22(3):407–25.

Kvåle K, Haugen DF, Synnes O. Patients’ illness narratives-From being healthy to living with incurable cancer: Encounters with doctors through the disease trajectory. Cancer Rep (Hoboken). 2020. https://doi.org/10.1002/cnr2.1227 .

Lamprell K, Braithwaite J. Reading Between the Lines: A Five-Point Narrative Approach to Online Accounts of Illness. J Med Humanit. 2019;40(4):569–90.

Marini MG, Chesi P, Bruscagnin M, Ceccatelli M, Ruzzon E. Digits and narratives of the experience of Italian families facing premature births. J Matern Fetal Neonatal Med. 2018;31(17):2258–64.

Marini MG, Chesi P, Mazzanti L, Guazzarotti L, Toni TD, Salerno MC, Officioso A, et al. Stories of experiences of care for growth hormone deficiency: the CRESCERE project. Future Sci OA. 2016. https://doi.org/10.4155/fso.15.82 .

Midena E, Varano M, Pilotto E, Staurenghi G, Camparini M, Pece A, Battaglia PM. Real-life patient journey in neovascular age-related macular degeneration: a narrative medicine analysis in the Italian setting. Eye (Lond). 2022;36(1):182–92.

Palandri F, Benevolo G, Iurlo A, Abruzzese E, Carella AM, Paoli C, Palumbo GA, et al. Life for patients with myelofibrosis: the physical, emotional and financial impact, collected using narrative medicine-Results from the Italian “Back to Life” project. Qual Life Res. 2018;27(6):1545–54.

Rushforth A, Ladds E, Wieringa S, Taylor S, Husain L, Greenhalgh T. Long Covid-The illness narratives. Soc Sci Med. 2021. https://doi.org/10.1016/j.socscimed.2021.114326 .

Sansone V, Cancani F, Gagliardi C, Satta T, Cecchetti C, de Ranieri C, Di Nardo M, Rossi A, et al. Narrative diaries in the paediatric intensive care unit: A thematic analysis. Nurs Crit Care. 2022;27(1):45–54.

Scaratti C, Zorzi G, Guastafierro E, Leonardi M, Covelli V, Toppo C, Nardocci N. Long term perceptions of illness and self after Deep Brain Stimulation in pediatric dystonia: A narrative research. Eur J Paediatr Neurol. 2020;26:61–7.

Simonelli F, Sodi A, Falsini B, Bacci G, Iarossi G, Di Iorio V, Giorgio D, et al. Care Pathway of RPE65-Related Inherited Retinal Disorders from Early Symptoms to Genetic Counseling: A Multicenter Narrative Medicine Project in Italy. Clin Ophthalmol. 2021;2(15):4591–605.

Slocum RB, Howard TA, Villano JL. Narrative Medicine perspectives on patient identity and integrative care in neuro-oncology. J Neuroncol. 2017;134(2):417–21.

Slocum RB, Hart AL, Guglin ME. Narrative medicine applications for patient identity and quality of life in ventricular assist device (VAD) patients. Heart Lung. 2019;48(1):18–21.

Suter N, Ardizzone G, Giarelli G, Cadorin L, Gruarin N, Cipolat Mis C, Michilin N, et al. The power of informal cancer caregivers’ writings: results from a thematic and narrative analysis. Support Care Cancer. 2021;29(8):4381–8.

Talarico R, Cannizzo S, Lorenzoni V, Marinello D, Palla I, Pirri S, Ticciati S, et al. RarERN Path: a methodology towards the optimisation of patients’ care pathways in rare and complex diseases developed within the European Reference Networks. Orphanet J Rare Dis. 2021. https://doi.org/10.1186/s13023-021-01778-5 .

Testa M, Cappuccio A, Latella M, Napolitano S, Milli M, Volpe M, Marini MG. The emotional and social burden of heart failure: integrating physicians’, patients’, and caregivers’ perspectives through narrative medicine. BMC Cardiovasc Disord. 2020. https://doi.org/10.1186/s12872-020-01809-2 .

Tonini MC, Fiorencis A, Iannacchero R, Zampolini M, Cappuccio A, Raddino R, Grillo E, et al. Narrative Medicine to integrate patients’, caregivers’ and clinicians ’migraine experiences: the DRONE multicentre project. Neurol Sci. 2021;42:5277–88.

Vanstone M, Toledo F, Clarke F, Boyle A, Giacomini M, Swinton M, Saunders L, et al. Narrative medicine and death in the ICU: word clouds as a visual legacy. BMJ Support Palliat Care. 2016. https://doi.org/10.1136/bmjspcare-2016-001179 .

Volpato E, Centanni S, Banfi P, D’Antonio S, Peterle E, Bugliaro F, Grattagliano I, et al. Narrative Analysis of the Impact of COVID-19 on Patients with Chronic Obstructive Pulmonary Disease, Their Caregivers, and Healthcare Professionals in Italy. Int J Chron Obstruct Pulmon Dis. 2021;16:2181–201.

Zhang Y, Pi B, Xu X, Li Y, Chen X, Yang N. Influence of Narrative Medicine-Based Health Education Combined With An Online Patient Mutual Assistance Group On The Health Of Patients With Inflammatory Bowel Disease and Arthritis. Psychol Res Behav Manag. 2020;7(13):1–10.

Zocher U, Bertazzi I, Colella E, Fabi A, Scarinci V, Franceschini A, Cenci C, et al. Application of narrative medicine in oncological clinical practice: impact on health care professional. Recenti Prog Med. 2020;111(3):154–9.

Chen PJ, Huang CD, Yeh SJ. Impact of a narrative medicine programme on healthcare providers’ empathy scores over time. BMC Med Educ. 2017. https://doi.org/10.1186/s12909-017-0952-x .

Chu SY, Wen CC, Lin CW. A qualitative study of clinical narrative competence of medical personnel. BMC Med Educ. 2020. https://doi.org/10.1186/s12909-020-02336-6 .

Daryazadeh S, Adibi P, Yamani N. The role of narrative medicine program in promoting professional ethics: perceptions of Iranian medical students. J Med Ethics Hist Med. 2021. https://doi.org/10.18502/jmehm.v14i21.8181 .

Karkabi K, Wald HS, Castel OC. The use of abstract paintings and narratives to foster reflective capacity in medical educators: a multinational faculty development workshop. Med Humanit. 2014;40(1):44–8.

Lijoi AF, Tovar AD. Narrative medicine: Re-engaging and re-energizing ourselves through story. Int J Psychiatry Med. 2020;55(5):321–30.

Wallace CL, Trees A, Ohs J, Hinyard L. Narrative Medicine for Healthcare Providers: Improving Practices of Advance Care Planning. Omega (Westport). 2023;87(1):87–102.

Winkel AF, Feldman N, Moss H, Jakalow H, Simon J, Blank S. Narrative Medicine Workshops for Obstetrics and Gynecology Residents and Association with Burnout Measures. Obstet Gynecol. 2016;128(Suppl 1):27–33.

Chou JC, Schepel IRM, Vo AT, Kapetanovic S, Schaff PB. Patient Co-Participation in Narrative Medicine Curricula as a Means of Engaging Patients as Partners in Healthcare: a pilot study involving medical students and patient living with HIV. J Med Humanit. 2021;42(4):641–57.

Lam JA, Feingold-Link M, Noguchi J, Quinn A, Chofay D, Cahill K, Rougas S. My Life, My Story: Integrating a Life Story Narrative Component into Medical Student Curricula. MedEdPORTAL. 2022. https://doi.org/10.15766/mep_2374-8265.11211 .

Arntfield SL, Slesar K, Dickson J, Charon R. Narrative medicine as a means of training medical students toward residency competencies. Patient Educ Couns. 2013;91(3):280–6.

DasGupta S, Charon R. Personal illness narratives: using reflective writing to teach empathy. Acad Med. 2004;79(4):351–6.

Gowda D, Curran T, Khedagi A, Mangold M, Jiwani F, Desai U, Charon R, Balmer D. Implementing an interprofessional narrative medicine program in academic clinics: Feasibility and program evaluation. Perspect Med Educ. 2019;8(1):52–9.

Lemogne C, Buffel du Vaure C, Hoertel N, Catu-Pinault A, Limosin F, Ghasarossian C, Le Jeunne C, Jaury P. Balint groups and narrative medicine compared to a control condition in promoting students’ empathy. BMC Med Educ. 2020. https://doi.org/10.1186/s12909-020-02316-w .

Liao KC, Peng CH, Snell L, Wang X, Huang CD, Saroyan A. Understanding the lived experiences of medical learners in a narrative medicine course: a phenomenological study. BMC Med Educ. 2021. https://doi.org/10.1186/s12909-021-02741-5 .

Lorenz JF, Darok MC, Ho L, Holstrom-Mercader MS, Freiberg AS, Dellasega CA. The Impact of an Unconventional Elective in Narrative Medicine and Pediatric Psycho-oncology on Humanism in Medical Students. J Cancer Educ. 2022;37(6):1798–805.

Miller E, Balmer D, Hermann N, Graham G, Charon R. Sounding narrative medicine: studying students’ professional identity development at Columbia University College of Physicians and Surgeons. Acad Med. 2014;89(2):335–42.

Shaw AC, McQuade JL, Reilley MJ, Nixon B, Baile WF, Epner DE. Integrating Storytelling into a Communication Skills Teaching Program for Medical Oncology Fellows. J Cancer Educ. 2019;34(6):1198–203.

Skelton JR, O’Riordan M, Berenguera Ossȯ A, Beavan J, Weetman K. Learning from patients: trainers’ use of narratives for learning and teaching. BJGP Open. 2017. https://doi.org/10.3399/bjgpopen17X100581 .

Launer J, Wohlmann A. Narrative medicine, narrative practice, and the creation of meaning. Lancet. 2023;401(10371):98–9.

Download references

The work has not been financed.

Author information

Authors and affiliations.

Institute of Management, Scuola Superiore Sant’Anna Pisa, Piazza Martiri della Libertà 33, Pisa, 56127, Italy

Ilaria Palla & Giuseppe Turchetti

SIMeN, Società Italiana Medicina Narrativa, Arezzo, Italy

Stefania Polvani

Azienda USL Toscana Sud Est, Arezzo, Italy

You can also search for this author in PubMed   Google Scholar

Contributions

I.P. and S.P. carried out the scoping review, conceived the study, data collection process and drafted the manuscript. G.T. participated in the coordination of the study. All authors read, reviewed and approved the final manuscript.

Corresponding author

Correspondence to Ilaria Palla .

Ethics declarations

Ethics approval and consent to participate.

Not applicable.

Consent for publication

Competing interests.

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Supplementary material 1., supplementary material 2., supplementary material 3., rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ .

Reprints and permissions

About this article

Cite this article.

Palla, I., Turchetti, G. & Polvani, S. Narrative Medicine: theory, clinical practice and education - a scoping review. BMC Health Serv Res 24 , 1116 (2024). https://doi.org/10.1186/s12913-024-11530-x

Download citation

Received : 01 February 2024

Accepted : 03 September 2024

Published : 27 September 2024

DOI : https://doi.org/10.1186/s12913-024-11530-x

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Healthcare professional
• Scoping review
• Personalized medicine

BMC Health Services Research

ISSN: 1472-6963

• General enquiries: [email protected]

An official website of the United States government

Here’s how you know

Official websites use .gov A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS A lock ( Lock A locked padlock ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

https://www.nist.gov/publications/consumer-perspectives-battery-electric-vehicles-literature-review

Consumer Perspectives on Battery Electric Vehicles: A Literature Review

Download paper, additional citation formats.

• Google Scholar

If you have any questions about this publication or are having problems accessing it, please contact [email protected] .

• IOP Publishing
• Follow us on Facebook
• Follow us on Twitter
• Follow us on LinkedIn
• Watch us on Youtube
• Enter e-mail address
• Show Enter password
• Remember me Forgot your password?
• Access more than 20 years of online content
• Manage which e-mail newsletters you want to receive
• Read about the big breakthroughs and innovations across 13 scientific topics
• Explore the key issues and trends within the global scientific community
• Choose which e-mail newsletters you want to receive

Reset your password

Please enter the e-mail address you used to register to reset your password

Note: The verification e-mail to change your password should arrive immediately. However, in some cases it takes longer. Don't forget to check your spam folder.

If you haven't received the e-mail in 24 hours, please contact [email protected]

Registration complete

Thank you for registering with Physics World If you'd like to change your details at any time, please visit My account

Knowledge grows step-by-step despite the exponential growth of papers, finds study

Scientific knowledge is growing at a linear rate despite an exponential increase in publications. That’s according to a study by physicists in China and the US, who say their finding points to a decline in overall scientific productivity. The study therefore contradicts the notion that productivity and knowledge grow hand in hand – but adds weight to the view that the rate of scientific discovery may be slowing or that “information fatigue” and the vast number of papers can drown out new discoveries .

Defining knowledge is complex, but it can be thought of as a network of interconnected beliefs and information. To measure it, the authors previously created a knowledge quantification index (KQI). This tool uses various scientific impact metrics to examine the network structures created by publications and their citations and quantifies how well publications reduce the uncertainty of the network, and thus knowledge.

The researchers claim the tool’s effectiveness has been validated through multiple approaches, including analysing the impact of work by Nobel laureates.

In the latest study, published on arXiv , the team analysed 213 million scientific papers, published between 1800 and 2020, as well as 7.6 million patents filed between 1976 and 2020. Using the data, they built annual snapshots of citation networks, which they then scrutinised with the KQI to observe changes in knowledge over time.

The researchers – based at Shanghai Jiao Tong University in Shanghai, the University of Minnesota in the US and the Institute of Geographic Sciences and Natural Resources Research in Beijing –found that while the number of publications has been increasing exponentially, knowledge has not.

Instead, their KQI suggests that knowledge has been growing in a linear fashion. Different scientific disciplines do display varying rates of knowledge growth, but they all have the same linear growth pattern. Patent growth was found to be much slower than publication growth but also shows the linear growth in the KQI.

‘Hidden’ citations conceal the true impact of scientific research

According to the authors, the analysis indicates “no significant change in the rate of human knowledge acquisition”, suggesting that our understanding of the world has been progressing at a steady pace.

If scientific productivity is defined as the number of papers required to grow knowledge, this signals a significant decline in productivity, the authors claim.

The analysis also revealed inflection points associated with new discoveries, major breakthroughs and other important developments, with knowledge growing at different linear rates before and after.

Such inflection points create the illusion of exponential knowledge growth due to the sudden alteration in growth rates, which may, according to the study authors, have led previous studies to conclude that knowledge is growing exponentially.

Research focus

“Research has shown that the disruptiveness of individual publications – a rough indicator of knowledge growth – has been declining over recent decades,” says Xiangyi Meng , a physicist at Northwestern University in the US, who works in network science but was not involved in the research. “This suggests that the rate of knowledge growth must be slower than the exponential rise in the number of publications.”

Meng adds, however, that the linear growth finding is “surprising” and “somewhat pessimistic” – and that further analysis is needed to confirm if knowledge growth is indeed linear or whether it “more likely, follows a near-linear polynomial pattern, considering that human civilization is accelerating on a much larger scale”.

Due to the significant variation in the quality of scientific publications, Meng says that article growth may “not be a reliable denominator for measuring scientific efficiency”. Instead, he suggests that analysing research funding and how it is allocated and evolves over time might be a better focus.

Want to read more?

Note: The verification e-mail to complete your account registration should arrive immediately. However, in some cases it takes longer. Don't forget to check your spam folder.

If you haven't received the e-mail in 24 hours, please contact [email protected] .

• E-mail Address

Michael Allen is a science writer based in the UK

Free Peer Review Excellence online course

Get peer review confident with our free, certified, course.

• Scientific enterprise

NASA suffering from ageing infrastructure and inefficient management practices, finds report

Discover more from physics world.

Science thrives on constructive and respectful peer review

The free-to-read Physics World Big Science Briefing 2024 is out now

Looking to the future of statistical physics, how intense storms can affect your cup of tea

Related jobs, biosafety and biogovernance administrative specialist, human and organizational performance program specialist, international faculty position, uestc, related events.

• Materials | Symposium Computational Design in Materials Science, Methodologies and Interplay Between Theory and Experiment 29 September — 3 October 2024 | Santos, Brazil
• Instrumentation and measurement | Conference 19th Joint Vacuum Conference 29 September — 4 October 2024 | Split, Croatia
• Scientific enterprise | Forum Big Science Business Forum 2024 10—4 October 2024 | Trieste, Italy

Fighting Recent Inflation: An Empirical Literature Review of Monetary and Governmental Policies

Managerial Economics, volume 25, issue 1, 2024 [ 10.7494/manage.2024.25.1.7 ]

Posted: 24 Sep 2024

Christof Haar

University of Graz - Department of Finance

Date Written: September 05, 2024

Inflation is a crucial issue for businesses and households, central banks and governments, in fact for all economic actors, as it has a strong impact on economic growth and welfare. This literature review captures how monetary policy and governmental policy can control inflation, how their measures work, and which are the key points to consider when conducting these policies, especially in times of crisis. It uses academic papers from the past eight decades, supplemented by publications from financial and economic institutions, but focuses on literature beginning with the 2000s to capture the latest methods and techniques to find out what drives inflation and how. Monetary policy and governmental policy should act together to effectively fight inflation. Monetary policy can have adverse effects on governments’ future tax revenues and debt-to-GDP ratios. Fiscal policy measures should be associated with altered government spending to avoid high inflation rates and/or high debt burdens in the future. Especially during and right after crises, measures have to be evaluated as too long support can fuel inflation in the future. Both parties should also take into account people’s inflation expectations, as these shape their economic behaviour.

Keywords: inflation, monetary policy, governmental policy, crisis, literature review

JEL Classification: E52, E62, E63

Suggested Citation: Suggested Citation

Christof Haar (Contact Author)

University of graz - department of finance ( email ).

Universitätstraße 15 Graz, 8010 Austria +43-316-380-3515 (Phone)

Do you have a job opening that you would like to promote on SSRN?

Paper statistics, related ejournals, macroeconomics: monetary & fiscal policies ejournal.

Subscribe to this fee journal for more curated articles on this topic

Monetary Economics: Central Banks - Policies & Impacts eJournal

International political economy: monetary relations ejournal, comparative political economy: monetary policy ejournal.