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Introduction.
A literature review is a written work that :
From these analyses, the writer then offers an overview of the current status of a particular area of knowledge from both a practical and theoretical perspective.
Literature reviews are important because they are usually a required step in a thesis proposal (Master's or PhD). The proposal will not be well-supported without a literature review. Also, literature reviews are important because they help you learn important authors and ideas in your field. This is useful for your coursework and your writing. Knowing key authors also helps you become acquainted with other researchers in your field.
Look at this diagram and imagine that your research is the "something new." This shows how your research should relate to major works and other sources.
Olivia Whitfield | Graduate Reference Assistant | 2012-2015
Literature Review is a comprehensive survey of the works published in a particular field of study or line of research, usually over a specific period of time, in the form of an in-depth, critical bibliographic essay or annotated list in which attention is drawn to the most significant works.
Also, we can define a literature review as the collected body of scholarly works related to a topic:
The objective of a Literature Review is to find previous published scholarly works relevant to an specific topic
A literature review is important because it:
All content in this section is from Literature Review Research from Old Dominion University
Keep in mind the following, a literature review is NOT:
Not an essay
Not an annotated bibliography in which you summarize each article that you have reviewed. A literature review goes beyond basic summarizing to focus on the critical analysis of the reviewed works and their relationship to your research question.
Not a research paper where you select resources to support one side of an issue versus another. A lit review should explain and consider all sides of an argument in order to avoid bias, and areas of agreement and disagreement should be highlighted.
A literature review serves several purposes. For example, it
As Kennedy (2007) notes*, 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 original studies. Third, there are the perceptions, conclusions, opinion, and interpretations that are shared informally that become part of the lore of 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 several approaches to how they can be done, depending upon the type of analysis underpinning your study. Listed below are definitions of types of literature reviews:
Argumentative Review This form examines literature selectively in order to support or refute an argument, deeply imbedded 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 to make summary claims of the sort found in systematic reviews.
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. A well-done integrative review meets the same standards as primary research in regard to clarity, rigor, and replication.
Historical Review Few things rest in isolation from historical precedent. Historical reviews are focused 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 [content], but how they said it [method of analysis]. This approach provides a framework of understanding at different levels (i.e. those of theory, substantive fields, research approaches and data collection and analysis techniques), enables researchers to draw on 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, and helps highlight many ethical issues which we should be aware of and consider as we go through our 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 analyse data from the studies that are included in the review. 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?"
Theoretical Review The purpose of this form is to concretely examine the corpus of theory that has accumulated in regard to an issue, concept, theory, phenomena. The theoretical literature review help 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.
* Kennedy, Mary M. "Defining a Literature." Educational Researcher 36 (April 2007): 139-147.
All content in this section is from The Literature Review created by Dr. Robert Larabee USC
Robinson, P. and Lowe, J. (2015), Literature reviews vs systematic reviews. Australian and New Zealand Journal of Public Health, 39: 103-103. doi: 10.1111/1753-6405.12393
What's in the name? The difference between a Systematic Review and a Literature Review, and why it matters . By Lynn Kysh from University of Southern California
Systematic review or meta-analysis?
A systematic review answers a defined research question by collecting and summarizing all empirical evidence that fits pre-specified eligibility criteria.
A meta-analysis is the use of statistical methods to summarize the results of these studies.
Systematic reviews, just like other research articles, can be of varying quality. They are a significant piece of work (the Centre for Reviews and Dissemination at York estimates that a team will take 9-24 months), and to be useful to other researchers and practitioners they should have:
Not all systematic reviews contain meta-analysis.
Meta-analysis is the use of 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 of health care than those derived from the individual studies included within a review. More information on meta-analyses can be found in Cochrane Handbook, Chapter 9 .
A meta-analysis goes beyond critique and integration and conducts secondary statistical analysis on the outcomes of similar studies. It is a systematic review that uses quantitative methods to synthesize and summarize the results.
An advantage of a meta-analysis is the ability to be completely objective in evaluating research findings. Not all topics, however, have sufficient research evidence to allow a meta-analysis to be conducted. In that case, an integrative review is an appropriate strategy.
Some of the content in this section is from Systematic reviews and meta-analyses: step by step guide created by Kate McAllister.
Writing literature reviews, what is a literature review.
"A literature review discusses published information in a particular subject area, and sometimes information in a particular subject area within a certain time period. A literature review can be just a simple summary of the sources, but it usually has an organizational pattern and combines both summary and synthesis. A summary is a recap of the important information of the source, but a synthesis is a re-organization, or a reshuffling, of that information. It might give a new interpretation of old material or combine new with old interpretations. Or it might trace the intellectual progression of the field, including major debates. And depending on the situation, the literature review may evaluate the sources and advise the reader on the most pertinent or relevant." Source: The Writing Center at UNC-Chapel Hill. (2013). Literature Reviews. Retrieved from https://writingcenter.unc.edu/handouts/literature-reviews/ This link opens in a new window
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Despite a surge in published scholarship in medical education 1 and rapid growth in journals that publish educational research, manuscript acceptance rates continue to fall. 2 Failure to conduct a thorough, accurate, and up-to-date literature review identifying an important problem and placing the study in context is consistently identified as one of the top reasons for rejection. 3 , 4 The purpose of this editorial is to provide a road map and practical recommendations for planning a literature review. By understanding the goals of a literature review and following a few basic processes, authors can enhance both the quality of their educational research and the likelihood of publication in the Journal of Graduate Medical Education ( JGME ) and in other journals.
In medical education, no organization has articulated a formal definition of a literature review for a research paper; thus, a literature review can take a number of forms. Depending on the type of article, target journal, and specific topic, these forms will vary in methodology, rigor, and depth. Several organizations have published guidelines for conducting an intensive literature search intended for formal systematic reviews, both broadly (eg, PRISMA) 5 and within medical education, 6 and there are excellent commentaries to guide authors of systematic reviews. 7 , 8
Such work is outside the scope of this article, which focuses on literature reviews to inform reports of original medical education research. We define such a literature review as a synthetic review and summary of what is known and unknown regarding the topic of a scholarly body of work, including the current work's place within the existing knowledge . While this type of literature review may not require the intensive search processes mandated by systematic reviews, it merits a thoughtful and rigorous approach.
An understanding of the current literature is critical for all phases of a research study. Lingard 9 recently invoked the “journal-as-conversation” metaphor as a way of understanding how one's research fits into the larger medical education conversation. As she described it: “Imagine yourself joining a conversation at a social event. After you hang about eavesdropping to get the drift of what's being said (the conversational equivalent of the literature review), you join the conversation with a contribution that signals your shared interest in the topic, your knowledge of what's already been said, and your intention.” 9
The literature review helps any researcher “join the conversation” by providing context, informing methodology, identifying innovation, minimizing duplicative research, and ensuring that professional standards are met. Understanding the current literature also promotes scholarship, as proposed by Boyer, 10 by contributing to 5 of the 6 standards by which scholarly work should be evaluated. 11 Specifically, the review helps the researcher (1) articulate clear goals, (2) show evidence of adequate preparation, (3) select appropriate methods, (4) communicate relevant results, and (5) engage in reflective critique.
Failure to conduct a high-quality literature review is associated with several problems identified in the medical education literature, including studies that are repetitive, not grounded in theory, methodologically weak, and fail to expand knowledge beyond a single setting. 12 Indeed, medical education scholars complain that many studies repeat work already published and contribute little new knowledge—a likely cause of which is failure to conduct a proper literature review. 3 , 4
Likewise, studies that lack theoretical grounding or a conceptual framework make study design and interpretation difficult. 13 When theory is used in medical education studies, it is often invoked at a superficial level. As Norman 14 noted, when theory is used appropriately, it helps articulate variables that might be linked together and why, and it allows the researcher to make hypotheses and define a study's context and scope. Ultimately, a proper literature review is a first critical step toward identifying relevant conceptual frameworks.
Another problem is that many medical education studies are methodologically weak. 12 Good research requires trained investigators who can articulate relevant research questions, operationally define variables of interest, and choose the best method for specific research questions. Conducting a proper literature review helps both novice and experienced researchers select rigorous research methodologies.
Finally, many studies in medical education are “one-offs,” that is, single studies undertaken because the opportunity presented itself locally. Such studies frequently are not oriented toward progressive knowledge building and generalization to other settings. A firm grasp of the literature can encourage a programmatic approach to research.
Considering these issues, journals have a responsibility to demand from authors a thoughtful synthesis of their study's position within the field, and it is the authors' responsibility to provide such a synthesis, based on a literature review. The aforementioned purposes of the literature review mandate that the review occurs throughout all phases of a study, from conception and design, to implementation and analysis, to manuscript preparation and submission.
Planning the literature review requires understanding of journal requirements, which vary greatly by journal ( table 1 ). Authors are advised to take note of common problems with reporting results of the literature review. Table 2 lists the most common problems that we have encountered as authors, reviewers, and editors.
Sample of Journals' Author Instructions for Literature Reviews Conducted as Part of Original Research Article a
Common Problem Areas for Reporting Literature Reviews in the Context of Scholarly Articles
Three resources may facilitate identifying relevant literature: human resources, search tools, and related literature. As the process requires time, it is important to begin searching for literature early in the process (ie, the study design phase). Identifying and understanding relevant studies will increase the likelihood of designing a relevant, adaptable, generalizable, and novel study that is based on educational or learning theory and can maximize impact.
A medical librarian can help translate research interests into an effective search strategy, familiarize researchers with available information resources, provide information on organizing information, and introduce strategies for keeping current with emerging research. Often, librarians are also aware of research across their institutions and may be able to connect researchers with similar interests. Reaching out to colleagues for suggestions may help researchers quickly locate resources that would not otherwise be on their radar.
During this process, researchers will likely identify other researchers writing on aspects of their topic. Researchers should consider searching for the publications of these relevant researchers (see table 3 for search strategies). Additionally, institutional websites may include curriculum vitae of such relevant faculty with access to their entire publication record, including difficult to locate publications, such as book chapters, dissertations, and technical reports.
Strategies for Finding Related Researcher Publications in Databases and Search Engines
Researchers will locate the majority of needed information using databases and search engines. Excellent resources are available to guide researchers in the mechanics of literature searches. 15 , 16
Because medical education research draws on a variety of disciplines, researchers should include search tools with coverage beyond medicine (eg, psychology, nursing, education, and anthropology) and that cover several publication types, such as reports, standards, conference abstracts, and book chapters (see the box for several information resources). Many search tools include options for viewing citations of selected articles. Examining cited references provides additional articles for review and a sense of the influence of the selected article on its field.
Once relevant articles are located, it is useful to mine those articles for additional citations. One strategy is to examine references of key articles, especially review articles, for relevant citations.
As the aforementioned resources will likely provide a tremendous amount of information, organization is crucial. Researchers should determine which details are most important to their study (eg, participants, setting, methods, and outcomes) and generate a strategy for keeping those details organized and accessible. Increasingly, researchers utilize digital tools, such as Evernote, to capture such information, which enables accessibility across digital workspaces and search capabilities. Use of citation managers can also be helpful as they store citations and, in some cases, can generate bibliographies ( table 4 ).
Citation Managers
Researchers often ask how to know when they have located enough citations. Unfortunately, there is no magic or ideal number of citations to collect. One strategy for checking coverage of the literature is to inspect references of relevant articles. As researchers review references they will start noticing a repetition of the same articles with few new articles appearing. This can indicate that the researcher has covered the literature base on a particular topic.
In preparing to write a research paper, it is important to consider which citations to include and how they will inform the introduction and discussion sections. The “Instructions to Authors” for the targeted journal will often provide guidance on structuring the literature review (or introduction) and the number of total citations permitted for each article category. Reviewing articles of similar type published in the targeted journal can also provide guidance regarding structure and average lengths of the introduction and discussion sections.
When selecting references for the introduction consider those that illustrate core background theoretical and methodological concepts, as well as recent relevant studies. The introduction should be brief and present references not as a laundry list or narrative of available literature, but rather as a synthesized summary to provide context for the current study and to identify the gap in the literature that the study intends to fill. For the discussion, citations should be thoughtfully selected to compare and contrast the present study's findings with the current literature and to indicate how the present study moves the field forward.
To facilitate writing a literature review, journals are increasingly providing helpful features to guide authors. For example, the resources available through JGME include several articles on writing. 17 The journal Perspectives on Medical Education recently launched “The Writer's Craft,” which is intended to help medical educators improve their writing. Additionally, many institutions have writing centers that provide web-based materials on writing a literature review, and some even have writing coaches.
The literature review is a vital part of medical education research and should occur throughout the research process to help researchers design a strong study and effectively communicate study results and importance. To achieve these goals, researchers are advised to plan and execute the literature review carefully. The guidance in this editorial provides considerations and recommendations that may improve the quality of literature reviews.
Are you writing a literature review as part of a final year project, dissertation, or thesis, or as a standalone piece of work? This page will work through a process of organising and synthesising your sources and then writing a clear and critical final review.
A literature review is an account of the current thinking in a specific area of study. Its purpose is to introduce the reader to what has gone before and often to provide you with a foundation that you can build on with your own research. This traditional form of review is sometimes also referred to as a narrative review.
A literature review will often form a section or chapter of a larger piece of research work, such as a dissertation, thesis, or final year project. It can also be a standalone piece of work.
A literature review will usually do some or all of the following:
Considering a body of scholarship as a whole (or in relation to each of your themes) will allow you to 'synthesise' multiple sources and produce an overall summary.
Developing a literature review will help you to develop a level of expertise in your chosen area. By consulting and including a unique combination of sources, you will be able to formulate an informed and original perspective. Where relevant, this can drive forward your ongoing research.
Writing a Literature Review workshop: book here
A systematic review is a research methodology, often following a standardised and replicable search method and reporting structure that is specific to your discipline. Visit our guidance on systematic reviews for more information.
As you encounter more and more relevant sources, you will face an ever-expanding amount of reading for yourself. It would take years to read through all of the literature in a specific field from start to finish.
Academic reading, and particularly the process of 'reading around' a topic, is about selective, or targeted reading. Visit our Reading and understanding information Hub to explore approaches to reading for different purposes.
Creating a Literature Matrix can help you to identify the key things that you want to take away from each source. A literature matrix is a simple spreadsheet where you select column titles to suit the aims of your literature review. Are you interested in the research methodology, the scale of the research, the main conclusions, or something else entirely?
Once you have scanned through a source and pulled out the points you are interested in, you can move onto the next source. Organising your reading in this way will also allow you to identify key themes that are emerging in your reading, which you will be able to use later on to plan your review.
You may want to use a reference management tool to help organise and produce your bibliography. Visit the University of Sheffield Library Reference Management pages here .
Make a copy of our Literature matrix template (Google Sheet) and add/delete columns based on the information you want to collect during your search. Using a spreadsheet means that you can filter and sort your sources, for example, into chronological order, or alphabetically by author.
This downloadable example literature matrix shows how you can lay out your columns.
Once you have a number of sources to work with, you will start to identify key themes emerging. At this point you can start to organise your sources systematically to develop and explore those themes. Can you organise your themes from the broadest to the narrowest and most specific?
A synthesis matrix will help you to identify a thematic structure for your literature review and to understand how the sources that you have found relate to one another. A synthesis matrix is a further spreadsheet that organises your sources by theme and includes a synthesis column, where you can begin to draw out comparisons between the sources.
Once you have identified a number of sources for each theme in your matrix, you should be able to identify the following:
Your synthesis column provides an opportunity for you to comment on multiple sources considered as a whole. It is a space for your critical voice and interpretation, which is a key part of writing a successful literature review.
Make a copy of our synthesis matrix (Google Sheet) to organise your themes and plan how the relevant sources can be synthesised.
Download a completed example synthesis matrix from NC State University (PDF, 34Kb)
Visit our Producing a literature review interactive tutorial - for further guidance.
Once you have done the background reading and organised your sources using a synthesis matrix, the job of writing your review is simply about adding flesh to the bones. You will need to write your review as a narrative account, but you can use your matrix as a framework to help you do so.
A literature review will usually follow a simple structure:
Your review may be broken down by section headings or be a continuous flow with themes clearly separated in a paragraph structure. Each section or paragraph will describe that theme and finish by summarising your overview of a theme (the synthesis part of the matrix above, which includes your critical analysis).
Our web page How to structure a paragrap h has further guidance to ensure your paragraphs are clear and contain your synthesis and critical analysis.
For advice and feedback on your own review, including referencing, synthesis and academic arguments, please book a writing advisory service appointment.
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This narrative review summarizes current literature on the relationship of mitochondrial biomarkers with obesity-related characteristics, including body mass index and body composition.
Mitochondria, as cellular powerhouses, play a pivotal role in energy production and the regulation of metabolic process. Altered mitochondrial functions contribute to obesity, yet evidence of the intricate relationship between mitochondrial dynamics and obesity-related outcomes in human population studies is scarce and warrants further attention.
We discuss emerging evidence linking obesity and related health outcomes to impaired oxidative phosphorylation pathways, oxidative stress and mtDNA variants, copy number and methylation, all hallmark of suboptimal mitochondrial function. We also explore the influence of dietary interventions and metabolic and bariatric surgery procedures on restoring mitochondrial attributes of individuals with obesity. Finally, we report on the potential knowledge gaps in the mitochondrial dynamics for human health for future study.
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Scientific Reports volume 14 , Article number: 21685 ( 2024 ) Cite this article
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One of the most common terms that is used to describe entities responsible for sharing genomic data for research purposes is ‘genomic research consortium’. However, there is a lack of clarity around the language used by consortia to describe their data sharing arrangements. Calls have been made for more uniform terminology. This article reports on a review of the genomic research consortium literature illustrating a wide diversity in the language that has been used over time to describe the access arrangements of these entities. The second component of this research involved an examination of publicly available information from a dataset of 98 consortia. This analysis further illustrates the wide diversity in the access arrangements adopted by genomic research consortia. A total of 12 different access arrangements were identified, including four simple forms (open, consortium, managed and registered access) and eight more complex tiered forms (for example, a combination of consortium, managed and open access). The majority of consortia utilised some form of tiered access, often following the policy requirements of funders like the US National Institutes of Health and the UK Wellcome Trust. It was not always easy to precisely identify the access arrangements of individual consortia. Greater consistency, clarity and transparency is likely to be of benefit to donors, depositors and accessors alike. More work needs to be done to achieve this end.
The genomic research landscape is teeming with hundreds of bodies, organisations, groups and teams which use a variety of terms to describe themselves. One of the most common terms that is used to describe many of these entities is ‘consortium’. This term emerged in the early 2000s to describe a particular type of research collaboration 1 . Core elements identified in the literature include: collaboration and/or partnership; pooling of resources; and efforts to reach a common/shared goal 2 , 3 , 4 , 5 , 6 , 7 . In this article, we rely on the following definition of genomic research consortia: ‘a group of scientists from multiple institutions who have agreed to cooperative research efforts involving, but not limited to, pooling of information from more than one study for the purpose of combined analyses and collaborative projects’ 5 .
The overarching purpose of genomic research consortia is to ensure that data generated by consortium members are shared for research purposes 8 . Establishing mechanisms to facilitate this, though vital to the genomics research effort, is not without scientific and technical challenges. The ongoing development of standards and policies for genomic data sharing by organisations like the Global Alliance for Genomics and Health (GA4GH) is addressing many of these challenges 9 . GA4GH also recognises these challenges are intimately interconnected with the regulatory and ethical concerns that arise whenever human genomic data is shared. The GA4GH Regulatory and Ethics Workstream (REWS) uses a global human rights framework to provide guidance on the regulatory, ethical, and social implications of genomic research and data sharing, and the development of standards and policies to address them 10 , 11 .
Funding agencies have also started to impose their own standards for the governance of genomic research funded by them. The US-based National Institutes of Health (NIH), for example formulated a policy on sharing genomic data in 2014 12 , which sits alongside its more recent broader data management and sharing policy 13 . One key feature is the expectation that researchers will ensure broad and responsible sharing. Investigators and their institutions are expected to provide the NIH with a Genomic Data Sharing Plan showing how the policy will be followed, and to submit large-scale human genomic and associated data to NIH-approved data repositories. Likewise, Wellcome in the UK has requirements regarding data access, including an explicit outputs management plan 14 .
GA4GH has been in existence for only eleven years and the NIH Genomic Data Sharing Policy came into effect shortly after that. Prior to their existence, the growth in genomic research consortia occurred largely organically. Although many of them are now bound by the policies of the NIH and other funding agencies, and/or are starting to adopt GA4GH standards, others continue to operate under their own governance arrangements. Assessing the adequacy and appropriateness of these governance arrangements is challenging, given the sheer number and diversity of entities that use this title. There are various reasons why it is important for genomic research consortia to be transparent in their governance arrangements, not least because they host data sourced from members of the public and generally obtained using public funding. In their examination of good governance of human genomic data, O’Doherty et al. 15 describe transparency as ‘a meta-function of good governance’.
The research reported in this article examined governance of data access by genomic research consortia. It included a review relevant general academic literature, and creation of a dataset of academic literature and other documentation relating to specific consortia.
Preliminary reviews of information relating to governance of data access in genomic research consortia were undertaken within the authors’ existing library of over 800 academic articles and other documents. A search was then undertaken on PubMed using the terms: health/genetic/genomic/consortia/consortium. Eighty-nine documents were ultimately selected and compiled for analysis in 2019. These documents were supplemented by more recent literature during 2020 to 2024. This library was not intended to be a comprehensive catalogue of every published document about governance of data access in genomic research consortia. Rather, it was intended to provide us with a sufficiently large sample to show common themes and points of difference.
An initial list of genomic research consortia was drawn from a catalogue of genomic data sharing initiatives compiled by GA4GH (it should be noted that this catalogue is no longer available on the GA4GH website). We also scoured other publicly available lists of consortia, including: a compilation of data sharing repositories available on the NIH website 16 ; repositories recommended by the journal Nature; and a list of GA4GH driver projects 17 . We acknowledge that by using GA4GH and NIH as our primary sources, there might be some skewing of our datasets towards North America. However, given the dominance of the US in genomic research and the NIH in funding this research, it is inevitable that the vast majority of consortia on our list would have their origins in this jurisdiction. We also note that it was not our intention that these searches would yield a definitive list of all genomic research consortia currently in existence. Instead, our aim was to create a reasonably comprehensive dataset of entities that self-identify as genomic research consortia. We acknowledge, however, that we may have missed some consortia which may have developed interesting and novel data access arrangements.
Based on our reliance on the definition of genomic research consortia proposed by Burgio et al., 5 we excluded entities that were otherwise included in our search results on several grounds:
entities that do not engage in research into genomics at the molecular level (for example the Human Cell Atlas), on the basis that their primary focus is not genomics;
repositories, archives and databases that store genomic data, such as the US-based database of Genotypes and Phenotypes (dbGaP). dbGaP is a repository established by the NIH and other government agencies to facilitate sharing of genotypic and phenotypic information 18 . Although dbGaP and other repositories do not participate in genomic research per se, consortia that deposit data in these repositories may be bound by their data access policies. In this respect their policies are relevant to our analysis;
entities whose only role is to analyse data, on the basis that they are not engaged in collaborative research; and
funding bodies (such as NIH and Wellcome), and bodies whose role is to develop policies and standards for data sharing (such as GA4GH), given that their role is to facilitate data sharing rather than being actively involved in genomic research. The model data sharing policies and other documents developed by these bodies are nevertheless relevant because they are often adopted by genomic research consortia.
In total, 102 consortia were identified by one of us (MA) together with another research assistant. This dataset was subsequently analysed separately by DN and JN and reduced to 98 genomic research consortia. Four entries were removed because they were shown not to be actively involved in genomic research and one was removed because the consortium had been dissolved some years prior to our project and no information was available about its governance. Although other consortia have also completed their work, information on their governance arrangements is still available (as are their datasets). One entry was split into two (ICGC and ICGC-ARGO) on the basis that the nature of the consortium (including the data access arrangements) changed on the creation of ICGC-ARGO.
A dataset of foundational articles and governance policies, guidelines, agreements and other documentation made publicly available by individual genomic research consortia was created to facilitate examination of specific governance arrangements. Where preliminary searches failed to reveal details on data access arrangements, consortium administrators were contacted and asked to provide relevant links, which resulted in some further additions to the dataset. The dataset was reviewed and analysed separately by DN and JN in 2023 for details on access arrangements. We then jointly sorted access arrangements into categories to generate a shared terminology. Further online searches of consortia websites were undertaken where the original dataset did not reveal sufficient information. A full list of consortia, together with their purported access arrangements and other demographic features, is provided in Supplementary Materials. Access to the full dataset is available on request from the authors (except for some documents where the consortium administrators requested confidentiality).
The European Expert Advisory Group on Data Access expressed concern about the lack of a shared terminology in the data sharing landscape in 2015. In response, the group attempted to impose some consistency in the terms employed to describe data sharing processes and the actors involved in them 19 . In a further attempt to address concerns about the lack of common terminology, GA4GH developed a ‘Data Sharing Lexicon’ in 2016 20 . Some of the more well-resourced genomic research consortia also imposed their own requirements for consistency in language and have been highly transparent in defining and explaining this. An exemplar is the ICGC 21 .
Despite these attempts to introduce a shared terminology, a host of terms continue to be employed to describe consortia activities. In particular, diverse terms have been used to describe the procedures involved in granting or facilitating data access. Some of these include: controlled access 22 , 23 , 24 , 25 , managed access 21 , 26 , 27 , tiered access 22 , 24 , unrestricted access, open access 24 , 27 , 28 , registered access 21 , 24 , authenticated, charged, exclusive and password access 29 , a passport model of access 30 and more. There is a lack of clarity about the extent to which these terms are truly distinct, related, or interchangeable with one another.
Recognising the broad range of language that used to describe data access arrangements in genomic research consortia, we designated five major categories from our analysis: consortium access, open access, registered access, managed access and tiered access. We describe each category in more detail below.
Given the rationale for the establishment of genomic research consortia is to share data, we expected that members of genomic research consortia share data with other members on a reciprocal basis 8 . However, it does not necessarily follow that members will be willing to share their data outside of the consortium 31 .
We use the term consortium access (CA) to describe the circumstances where consortium members only share data openly within the consortium. This puts consortium members in a privileged position relative to other researchers. There are at least three circumstances when this privileged position could fall away. The first arises when consortium-generated genomic data is made openly available to all. The second arises when all that is required to become a consortium member is registration, after which broad data access is granted without the need to participate in joint research endeavours. The third is somewhat different; rather than making data access more open, a consortium may require an application to be made by anyone accessing consortium-generated genomic data, vetted by the consortium, irrespective of whether the user is a consortium member or not.
In contrast to the closed approach of CA, open access (OA) makes data publicly available without restriction 22 , 27 . The theory behind OA is that unfettered access assists in the verification and replication of data, broadens opportunities to pool data and generates results without the need to collect further data, leading to better quality results and establishment of community resources 27 , 32 . As noted above, when a consortium opts for OA this means that consortium members do not have privileged access.
The Human Genome Project (HGP) laid the foundation for OA. The HGP commenced in 1990, and from the outset, it was a collaborative venture, both between institutions and countries. The goals of the HGP were to map the genes and sequence the genetic code for the entire human genome. In 1996 HGP participants agreed in the Bermuda Declaration that primary genomic sequences should remain in the public domain and that they should be rapidly released 33 , 34 . GenBank was created as a publicly accessible repository of the sequence information produced by the HGP 35 .
By 2003, there was some relaxation of the high standards for OA imposed by the Bermuda Declaration. In particular, it was felt necessary to recognise the valuable input provided by data generators. The Fort Lauderdale agreement reflected this desire 34 . Further inroads were made into these pure OA principles through the HapMap Project and the Genetic Association Information Network, led by the NIH 34 . Recognition of the input of data generators, protection of the privacy of data sources and avoidance of data capture for commercialisation purposes are all reasons why it is difficult to apply OA principles to all aspects of genomic data sharing 36
Registered access (RA) is used here to describe access that does not fully align with OA standards 23 , 24 . Researchers requesting access through this mechanism are not required to sign or abide by a formalised data transfer agreement. Rather, they may simply be required to accept the terms of use through a simple online agreement, by clicking ‘I agree’ in a checkbox or similar administratively ‘light’ procedure 24 . The GA4GH Data Sharing Lexicon describes RA as ‘a system of authentication and self-declaration prior to providing access to data.’ 20 The benefit of RA is that it creates a simple mechanism to verify the legitimacy of the user and to bind them to contractual terms regarding future uses of the data and other matters 23 , 24 . In this regard, it is less burdensome than managed access, considered below.
The HapMap Project marked the first step towards this RA approach. Its goal was to make sequence information available in a publicly accessible database 37 . The licence required users accessing the database to undertake that they would not restrict others from accessing or using the early-stage data they generated from the primary data. This obligation attracted some controversy, because it marked a significant change in philosophy from OA, although others saw it as an effective safeguard against capture 38 .
In some instances, consortia apply RA principles not just for data access but for consortium membership. This is another instance when there is no privileged access for consortium members who participate in joint research endeavours.
Another broad descriptor of access arrangements is managed access (MA). The MA approach requires that data access is managed or controlled by the consortium. In some instances, consortium members are required to comply with the same formal MA arrangements as external users, which is another example of circumstances where privileged access to data for consortium members is lost. This can arise when consortium data is not held centrally but rather is held by the organisation generating the data. Arrangements of this nature are described as federated access 39 , 40 , 41 . The GA4GH Beacon Project is an example of a system designed to facilitate federated access (amongst other things) 42 .
By imposing restrictions on data access 24 , MA arrangements involve more regulatory action by the consortium. Conditions might include who can access, what they can access, and how and under what terms access may be granted (i.e. on an internal server or downloaded) [ 22 ]. The term MA is not used ubiquitously; ‘controlled access’ and ‘restricted access’ are also common 22 , 24 , 25 . For instance, the ICGC uses controlled access to describe its access system for ‘composite genomic and clinical data that are associated to a unique, but not directly identifiable, person’, contrasting with its OA system for non-identifiable data 22 . Dyke et al. appear to use the terms MA and controlled access interchangeably 23 . The GA4GH Lexicon suggests that restricted access and controlled access are equivalent 20 . On this basis, we conclude that these terms are variations on the same theme—managing who can access data and on what terms, and for this reason, we refer to them collectively as MA.
Commonly, this degree of control is justified by the nature of the data, or to comply with conditions imposed by participant consent forms or by research ethics committees 20 , 27 . Dyke et al. suggest that genomic research consortia that employ MA should, by definition, have a Data Access Committee (DAC) 23 . The primary functions of a DAC are to: provide broad oversight of data access and to facilitate and expedite the data access process; review applications for access; make determinations as to access of researchers and others to data, including by reference to the qualifications and legitimacy of the researchers and the purposes specified for their research; and impose and ensure compliance with conditions upon data use 19 , 26 , 43 . Despite these relatively common features of DACs, there is a significant degree of variation in their purposes and activities 43 , 44 .
In summary, although DACs are widely recognised as the oversight bodies to whom researchers must apply to access data, there is a lack of clarity about: the extent to which their requirements are mandatory, or universal 26 , 32 , 43 , 45 ; the level of substantive or non-substantive oversight they wield in relation to researcher requests for access to data; and the criteria they apply to decisions about data-access 19 , 32 , 43 , 44 , 45 , 46 . This uncertainty calls into question the extent to which having a DAC ensures accountability, security and compliance with conditions and restrictions on data access.
It might be assumed that the four categories of data access listed above conveniently describe the gamut of access arrangements used by genomic research consortia. Our research and the work of others shows that the reality is much more complex. Tiered access (TA) is used to describe these more complex access arrangements of genomic research consortia.
The TA model provides genomic research consortia with the opportunity to segment their datasets and choose different access arrangements that best suit the consortium, its members and data sources. For example, it may be used to allow consortium members to retain privileged access up to the point of publication of their early findings. The NIH Genomic Data Sharing Policy explicitly allows delayed access for these purposes 12 .
This approach may also be used to provide external researchers with OA to certain data types, but require them to go through a more formalised process for other data types. This approach can balance competing concerns relating to scientific progress and protecting donor privacy 22 . The OA model might be applied to a range of data types including: data that cannot be linked in order to identify donors; aggregate level data; or data that does not contain information that could allow donors to be identified 19 , 22 . By contrast, access to more sensitive data, particularly data that may allow donors to be identified, is more likely to be subjected to the controlled procedures of MA. This means that users are likely to be required to make an application to the consortium’s DAC and enter into a Data Access Agreement before they are able to use the sensitive data 19 , 22 .
The ICGC was one of the early adopters of this TA approach 21 . dbGaP also requires depositors to use a TA approach to data access by external users, applying OA to studies, study documents, phenotypic variables and genotype access, and MA (which it refers to as controlled access) to de-identified data, phenotypes and genotypes for individual study subjects and pedigrees 47 .
Each of the consortia in our dataset has expressed an explicit commitment to genomic data access in one way or another. Some provide clear guidance on the ways in which they share their data, through their websites and through other documentation, making classification relatively straightforward. For a surprisingly large number of consortia, however, it took us some time to clearly and precisely identify data access arrangements, requiring extensive searching of our datasets, further online searches and email requests to consortium administrators. For 32 consortia (33% of the dataset), we had to infer data access arrangements based on: funding sources (for example, consortia funded by the NIH or Wellcome have to comply with their policies); repositories within which their data were stored (for example, dbGaP has certain requirements); nature of the consortium; types of data that the consortium generates; and other factors. Information on the main sources used to determine data access is provided for each consortium in the Supplementary Materials.
In sum, we are reasonably confident that the express and inferred data access arrangements we have recorded provide a reasonably comprehensive account of the variety of arrangements across our consortium dataset, and the prevalence of some of these arrangements within the sector as a whole. In Table 1 , we list the options for genomic data access in genomic research consortia ranging from CA to highly sophisticated models using up to three access categories. It should be noted that permutations of access arrangements which were not observed in the dataset are not included in Table 1 . In each case where there is at least one genomic research consortium using the stated access options, we have identified an exemplar and listed the total number of consortia in our dataset which appear to use this arrangement, noting again our caution that the actual arrangements are more difficult to accurately identify for some consortia than others.
This study is testament to the growth of genomic research since the HGP. It illustrates that genomic research consortia are providing the essential engine rooms for this research effort, by providing access to large and diverse sets of genomic data. The Supplementary Materials provide ample evidence of volume and diversity of genomic datasets, with some consortia being highly disease specific, others focusing more generally on classes of disease (for example, cancers) and large-scale population-wide initiatives.
This study also illustrates an increasing sophistication in data access arrangements. The simple dichotomy between OA and closed CA has been replaced with more tiered approaches, with TA (CA-OA-MA) being most prevalent (with 41 consortia manifesting this structure). Some data access arrangements are relatively uncommon. Complete OA, for example, is unusual, seen only rarely post-HGP. Far more commonplace is the open sharing of summary results, generally post-publication. Somewhat unanticipated also was the lack of reliance on RA as a mechanism for controlling the movement of data. Even within a TA structure, mechanisms that provided greater control (through MA) appear to be favoured over systems that simply require a process of registration.
One thing that is clear is that consortia continue to be committed to making their datasets as open as possible. The Variant Interpretation for Cancer Consortium 48 provides a good example. Although it describes itself as an ‘open consortium’, consortium documents set out a commitment to ‘share… at least a minimal set of data elements for cancer variant interpretations including: gene symbol, variant name, cancer subtype (tumor type and organ), clinical implication (drug sensitivity, drug resistance, adverse response, diagnostic, or prognostic), source (e.g., PubMed identifier) and curation group.’ Justifiable concerns around the privacy of data donors and primary researcher interests no doubt motivate the adoption of a limited definition of ‘open’ access in this and other consortia.
The dominance of the TA(CA-OA-MA) model is not unexpected, given the mandates from funders like the NIH and Wellcome. This arrangement appropriately recognises the value in making some information freely available, whilst acknowledging that there is a need to provide appropriate protections to donors and their families. It further acknowledges the valuable contributions made by consortium members, by allowing them to publish the results of their initial analyses before data is made more broadly available. As consortia move towards more federated access models, it seems likely that tiered access arrangements of this nature will become the norm.
Table 1 illustrates the intricacies of data access arrangements in genomic research consortia. Despite calls for standardisation, the level of diversity is striking. These differences are, unsurprisingly, more pronounced with consortia unaffiliated with major funders, such as the NIH or Wellcome. This demonstrates that, absent mandated adherence to policy arrangements, consortia tend to adopt access arrangements that best suit the interests of members. Beyond this, what is more striking is the effort it took us to precisely identify data access arrangements for many of the consortia in our dataset. As noted earlier, transparency is recognised as one of the fundamental requirements for good governance in genomics 15 .
Transparency and accuracy in articulation of data access arrangements is important for a number of reasons. For consortium members, clear parameters around access are essential to ensure that they are fully cognisant of arrangements in respect of the data they contribute to the consortium. Whether data is truly openly available, for example, may influence a decision as to whether or not to contribute to a particular consortium. For data users, certain access structures undoubtedly complicate the process of requesting data. Managed access provides a level of protection to consortium members and donors, but is more administratively burdensome 22 . A lack of transparency around the process for requesting access is likely to constitute a significant disincentive for users.
The adoption of consistent data access arrangements by consortia funded by the NIH and other bodies is an important step in simplifying the genomic data sharing landscape. We are aware that some of the other consortia in our dataset are also in the process of updating their access arrangements to more closely reflect developing norms within the sector. Nevertheless, the analysis presented in this article highlights the need for more work to achieve a shared, consistent terminology for access arrangements within genomics consortia and for greater transparency about these access arrangements. Not only is this important for consortium members and users of consortium data, but it is also critical in recognising the valuable contributions made by donors.
A list of all of the genomic research consortia in the dataset is provided in the supplementary materials. The full set of consortia-related documents is available from the authors on request.
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The authors acknowledge and thank other members of the Centre for Law and Genetics at the University of Tasmania for their encouragement and advice throughout this project and thank the Centre’s research assistants for their assistance with data collection, storage and preliminary analysis. We particularly acknowledge and thank Rachel Hay, who made a significant contribution to the early data collection and preliminary analysis, and Hugh Oxbrough, who assisted with finalising the consortium documentation.
This research was funded through the Australian Research Council’s Discovery Project scheme, DP180100269.
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Dianne Nicol & Jane Nielsen
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DN and JN conceptualised the project and contributed equally to data collection post-2021, data analysis and writing of the manuscript. MA undertook data collection and preliminary analysis. She wrote summary of the literature and reviewed the final manuscript.
Correspondence to Dianne Nicol .
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Dianne Nicol is co-lead of the Regulatory and Ethics Workstream of the Global Alliance for Genomics and Health. The authors have no other competing interests.
This project received ethics approval from the University of Tasmania Human Research Ethics Committee (Ethics Project ID: 18556). Ethics approval was not required for the aspect of the project reported in this article as all information was publicly available. Other aspects of this project (not reported in this article) involved interviews with consortium administrators for which ethics approval was granted.
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Nicol, D., Nielsen, J. & Archer, M. Data access arrangements in genomic research consortia. Sci Rep 14 , 21685 (2024). https://doi.org/10.1038/s41598-024-72653-z
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BMC Women's Health volume 24 , Article number: 513 ( 2024 ) Cite this article
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The purpose of this study was to develop an Infertility Perception Scale for Women (IPS-W).
Initial items were based on an extensive literature review and in-depth interviews with five infertile women and fifteen women not diagnosed with infertility. Forty-one items were derived from a pilot survey. Data were collected from 203 women who had experienced intrauterine insemination (IUI) and in-vitro fertilization (IVF) more than once. The data were analyzed to verify the reliability and validity of the scale.
Four factors containing 21 items were extracted from the exploratory factor analysis (EFA) to verify the construct validity. The four factors of infertility perception scale were perceived feelings, personal stigma, social stigma, and acceptance. These factors explained 59.3% of the total variance. The confirmatory factor analysis (CFA) confirmed a four-factor structure of the 21-item IPS-W. All fit indices were satisfactory (χ 2 /df ≤ 3, RMSEA < 0.08). These items were verified through convergent, discriminant, known group validity, concurrent validity testing. The internal consistency reliability was acceptable (Cronbach’s α = 0.90).
The scale reflects the perception of infertility within the cultural context of Korea. The findings can help nurses provide support that is appropriate for individual circumstances by examining how women experiencing infertility perceive infertility.
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Infertility, defined as the failure to achieve pregnancy within one year of regular unprotected sexual intercourse for couples of reproductive age, is perceived in the socio-cultural context of Korea as an impediment to an essential social rite of passage and a health problem that threatens family continuity [ 1 ]. In fact, Koreans struggling with infertility believe that giving birth to a child is a prerequisite for a stable marital relationship and that having children allows the development of family relationships [ 2 ].
Infertility is perceived negatively due to the uncertainty, loss of control, and stressful situations [ 3 ] it causes, having a negative impact on quality of life [ 4 ]. Accordingly, decisions about treatment procedures should be made by each individual and as a couple after exploring various options and with an appropriate social support system. However, individuals with infertility often decide on infertility treatment without an appropriate decision-making process, due to the fear of experiencing social prejudice and negative perceptions when revealing infertility problems [ 5 ].
The psychological difficulties faced by individuals with infertility are partly caused by negative social perceptions [ 5 , 6 ]. Approximately half of all infertile couples tend to hide their infertility problem out of fear of social stigma [ 7 ]. Such stigmatizing views of infertility create a vicious cycle leading infertile individuals to have a negative perception of their problem, avoid or prematurely discontinue infertility treatment, and feel isolated from society [ 5 ].
Harzif, Santawi, and Wijaya [ 8 ] compared differences in perceptions towards infertility treatment between urban and rural areas by examining the level of knowledge on the risk factors for infertility, attitudes towards infertility, the social impact of infertility, and other options for infertile couples. However, as yet, the perceptions of the individuals receiving infertility treatment have not been specifically measured [ 9 ]. Despite the limited tools available for measuring infertility perceptions, some studies have assessed the stigmatizing characteristics of infertility; however, in most cases, the term “infertility” does not appear in the generic tools for measuring stigma [ 7 , 10 , 11 ]. Taebe et al. [ 5 ] developed the female infertility stigma instrument (ISI-F) based on the premise that infertility represents one of the biggest challenges in female reproductive and sexual health in most societies. However, the development of ISI-F relied on females attempting natural pregnancy, including ovulation induction. Consequently, while it shares some of the stigmatizing characteristics of infertile individuals, this tool may not adequately reflect the psycho-emotional difficulties of individuals receiving infertility treatments. The Infertility Stigma Scale (ISS) developed by Fu et al. [ 12 ] is designed to measure the perceived self-stigmas that a female receiving infertility treatment places on herself. However, the scale only encompasses the stigmatizing characteristics of infertility, therefore it does not allow for the assessement of the general perception that females undergoing infertility treatment have about their infertility, especially in a Korean context. The concept of stigma refers to a psychological attitude linked to a series of negative outcomes [ 12 ]. In contrast, perception refers to the process of recognizing and interpreting the nature and meaning of all types of stimuli and may vary depending on how individuals interpret the situation and the society they live in. Perception can be used to explore problems and make decisions on what should be changed and the strategy ahead [ 13 ]. Self-perceived stigma acts as a stress factor that leads to negative social stigma about infertility and interferes with life adaptation [ 14 ]; hence, it is all the more important to improve the perceptions of infertility.
Therefore, this study aims to develop a sensitive tool to measure how women who experience infertility treatment perceive their infertility, positioning them as active agents in coping with infertility. The goal is to assess their personal perceptions of infertility and facilitate a positive shift in those perceptions.
The development and validation of the infertility perception scale was performed in accordance with the method proposed by Devellis [ 15 ]. This study adheres to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.
Development of preliminary items.
To identify the components of infertility perception, previous literature published in RISS, PUBMED, EMBASE, and NDSL were searched for relevant studies and existing tools in English or Korean. Only studies with full-text availability were included. The search keywords included “infertility,” “fertility,” “infertile women,” “infertility for women,” “infertility experiences,” “experiences of infertility,” “assisted reproductive technology,” “intrauterine insemination,” “in vitro fertilization,” “perception,” and “awareness.” Among articles from PubMed, EMBase, and RISS, a total 42 articles were selected and reviewed, excluding duplicate articles ( n = 102), studies with no women subjects ( n = 48), studies not relevant to perception ( n = 205), systematic reviews ( n = 6), and case studies ( n = 21).
In-depth interviews were then conducted to confirm the initially identified items of infertility perception. The interviews were conducted separately for women who had not experienced infertility and women who were receiving infertility treatment to avoid the possible influence of social perception on the perception of infertility among the women under treatment [ 16 ]. Moreover, the emotional ups and downs experienced by individuals receiving infertility treatment may vary significantly depending on the number of follicles, the number and quality of the collected eggs, the need to undergo repetitive infertility treatments, and added negative emotions from previous failed treatments.
The women without experience of infertility were married women aged 20–69 years (three women per age group) selected by purposive sampling with consideration of their education level, economic status, and type of residence. The interviews, which lasted between 20 and 40 min, were conducted between April 7 and April 20, 2020.
Subsequently, interviews were conducted on six women undergoing IVF procedures to identify the central concept of infertility perception. Announcements were posted inside treatment centers to recruit potential participants. The interviews, which lasted between 50 and 70 min, were conducted between May 10 and May 18, 2020.
The central question in the interviews was “How do you feel about infertility?” The supplementary question for women not diagnosed with infertility was “What did you think when you found out someone who was diagnosed with infertility or has experienced receiving infertility treatment?” The supplementary question for women receiving infertility treatment was “What feelings or thoughts did you have as you were diagnosed with infertility and undergoing infertility treatment?” The in-depth interviews were recorded with the consent of the participants, transcribed immediately, and analyzed according to the content analysis procedure proposed by Krippendorff [ 17 ].
After defining the conceptual framework through content analysis based on literature review and in-depth interviews, a total of 103 items were derived. Subsequently, duplicate items and those with unclear content were deleted, revised, or supplemented through a meeting with an expert with infertility nursing and research experience. As a result, a total of 66 preliminary items were derived, including 29, 18, and 19 items in personal, relational, and social dimensions, respectively.
To avoid central bias, during the instrument development process, the 4-point scale proposed by Lynn [ 18 ] was used to grade each item based on the level of agreement (1: “Strongly disagree” to 4: “Strongly agree”). The items in sections 1, 2, and 3 of the scale are reverse-scored, so that a higher total score indicates a more positive perception of infertility.
In this study, content validity was tested twice by calculating the content validity index (CVI) based on expert opinion. The appropriateness of each item was assessed using a 4-point Likert scale (4: “Highly relevant”; 3: “Quite relevant”; 2: “Somewhat relevant”; and 1: “Not relevant”). Items with an Item-Content Validity Index (I-CVI) ≥ 0.78 were selected. The first content validity testing was conducted in September 2020 by a 10-member expert panel [ 18 ].
The second content validity testing was conducted by five members from the first 10-member expert panel. Items with I-CVI ≥ 0.78 were selected, leading to a total of 41 preliminary items.
A pilot study was conducted with 20 women living in city B who had been diagnosed with infertility and received at least one round of assisted reproductive therapy to assess the level of understanding about the instrument and the time required to complete the questionnaire. The number of participants was based on the sample size of 20–40 participants for pilot studies proposed by Devellis [ 15 ]. Item appropriateness was assessed through questions such as “Are there any items that are difficult to understand?”, “Are there any items with ambiguous expressions?”, and “Are there any items that you believe lack relevance to the perception of infertility?”
The instrument was assessed through item analysis, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), convergent validity, discriminant validity, criterion validity, and reliability testing.
The subjects were married women aged 20 and above diagnosed with infertility; had received at least one round of IUI or IVF; currently receiving infertility treatment; understood the study’s purpose; and signed an informed consent. Those who had difficulties in understanding and responding to the self-reported questionnaire; had problems with cognitive comprehension; or had other physical disorders in addition to infertility were excluded. Based on the criteria that a sample size more than 200 or approximately five times the number of items would be appropriate for factor analysis [ 15 , 19 ] and considering a dropout rate of 10%, a total of 212 subjects were recruited.
Data was collected from eligible, voluntary participants from two hospitals between November 30, 2020 and February 5, 2021. In our study, a high valid response rate of 94.9% (203 valid responses out of 214 recruited participants) was achieved. This was primarily attributed to the structured approach in participant recruitment and data collection. Participants were initially approached by a researcher or trained assistant at the infertility treatment hospital, who provided a detailed explanation of the study’s purpose, the voluntary nature of participation, and assurances of confidentiality. Only those who expressed voluntary willingness to participate were provided with a QR code to access an online survey, which took approximately 15 to 20 min to complete. Conducting the survey during patients’ waiting times further facilitated participation. These factors collectively ensured that participants felt informed and comfortable in participating in the study.
For the concurrent validity, ISS developed by Fu et al. [ 12 ] was used. After obtaining permission, ISS was translated into Korean and was reviewed. Subsequently, the translated version was translated back into the original language and compared with the original items. The final version consisted of 27 items in Korean. The self-reported instrument consisted of four domains (self-devaluation, social withdrawal, public stigma, and family stigma) in 5-point scale (1: “Do not agree at all” to 5: “Strongly agree”). At the time of development, the reliability of the scale was indicated by Cronbach’s alpha = 0.94. Cronbach’s alpha was 0.86, 0.77, 0.92, and 0.84 for self-devaluation, social withdrawal, public stigma, and family stigma, respectively. In this study, the reliability of the scale was indicated by Cronbach’s alpha = 0.97. Cronbach’s alpha was 0.94, 0.86, 0.95, and 0.91 for self-devaluation, social withdrawal, public stigma, and family stigma, respectively.
Based on the evidence that a stigmatizing perception of infertility is associated with greater experience of negative emotions such as depression and anxiety [ 20 ], the Korean version of the Center for Epidemiologic Studies Depression Scale-Revised (K-CESD-R) [ 21 ] was used for known-group validity. The scale measures the severity of depressive symptoms according to four levels based on the frequency of the symptoms experienced during the past week. Each item receives 0 point for occurring never or rarely (during less than 1 day), 1 point for occurring some or a little of the time (1–2 days), 2 points for occurring occasionally or a moderate amount of time (3–4 days), and 3 points for occurring most or all of the time (5–7 days). The total scores for all the items of 0–15, 16–24, and 25–60 points were defined as normal, probable depression, and definite depression, respectively. According to Cho & Kim [ 22 ], the reliability of the scale was indicated by Cronbach’s alpha = 0.90 in the normal group ( N = 540), 0.93 in the clinical patient group ( N = 164), and 0.89 in the major depression group ( N = 46). In this study, the reliability of the scale was indicated by Cronbach’s alpha = 0.94.
Collected data were analyzed using SPSS version 25.0 program (IBM Corp., Armonk, NY, USA). The general and infertility-related characteristics of the subjects were analyzed by descriptive statistics using the frequency, percentage, mean, and standard deviation (SD). Item analysis was performed using mean, SD, skewness, and kurtosis. Moreover, item-total correlation analysis was performed, and items with an item-total correlation coefficient inferior to 0.30 were reviewed and deleted [ 23 ]. The construct validity was tested by EFA and the appropriateness of factor analysis was identified using the Kaiser-Meyer-Olkin (KMO) test and Bartlett’s test of sphericity. Furthermore, principal component analysis was used for factor extraction, while varimax rotation was used for factor rotation. For determination of the number of factors, eigenvalue ≥ 1, factor loading ≥ 0.50, commonality ≥ 0.40, and scree plot were considered [ 24 ].
The concurrent validity was tested using the Pearson’s correlation between the developed instrument and ISS [ 12 ], while the known-groups validity was tested using K-CESD-R [ 26 ]. Meanwhile, the differences in the infertility perception scores among the normal, probable depression, and definite depression groups were analyzed using one-way ANOVA, while a post-hoc test was performed using Scheffe’s test. Cronbach’s alpha was calculated to test the reliability.
The subjects’ age averaged 37.03 ± 4.56. The most response to cause of infertility was “unexplained” (57.1%). Most subjects (83.7%) had no children. The current treatments were IVF (57.1%) and IUI (42.9%). The 31.5% considered the treatment as moderately affordable, 15.8% viewed it as hardly affordable. The 11.3% had experience counseling for infertility. The 68.5% answered that their spousal support to infertility treatment was passive (Table 1 ).
Analysis of the items in the scale showed that the mean value was 1.27-3.00 and the SD was 0.48–0.97. The absolute skew and kurtosis values were 0.03–1.93 and 0.07–4.32, respectively, satisfying the criteria of an absolute skew value ≤ 2.0 and absolute kurtosis value ≤ 7.0 [ 25 ]. Accordingly, since multivariate normality was confirmed, all items were used in the analysis. The item-total correlation coefficient was 0.04–0.77, with 12 out of 41 items (items #6, 8, 9, 15, 22, 25, 28, 33, 36, 37, 38, and 39) showing an item-total correlation coefficient ≤ 0.30. Since these items were assessed as offering little contribution to the scale, they were deleted, leaving remaining items in the scale.
Construct validity testing.
A KMO value of 0.91 for 29 items and a Bartlett’s test of sphericity χ² value of 3137.40 ( p < .001) wear measured, confirming that the data were appropriate for factor analysis. After the first EFA, two items that were double-loaded on two factors (items #30 and 31) were deleted. After the second EFA on the remaining 27 items, three items that were double-loaded on two factors (items #12, 29, and 35) and three items with factor loading < 0.50 (items #1, 23, and 34) were deleted. Subsequently, the third EFA was performed using 21 items. The results showed a KMO value of 0.89 and a Bartlett’s test of sphericity χ² value of 1997.07 ( p < .001). Moreover, the commonality was 0.40–0.81, factor loading was 0.51–0.89, and there were four factors with eigenvalue > 1, which had a cumulative explanatory power of 59.3% (Table 2 ).
Four factors extracted according to infertility perception explained 59.3% of the total variance. In social science, an explanation of 40–60% of the variance for multifactor patterns is considered to be sufficient [ 26 ]. Factor-1 was named “perceived feelings,” consisting of six items explaining 34.6% of the total variance. Factor-2 was named “personal stigma,” consisting of eight items explaining 12.0% of the total variance. Factor-3 was named “social stigma,” consisting of three items explaining 6.6% of the total variance. Factor-4 was named “acceptance,” consisting of four items explaining 6.1% of the total variance.
According to the results of the exploratory factor analysis, the 21-item IPS-W with 4 factors underwent confirmatory factor analysis. The model fit was evaluated against predefined cutoff values for each fit index (χ 2 /df ≤ 3, AGFI ≥ 0.90, GFI ≥ 0.90, CFI ≥ 0.90, RMSEA < 0.08) [ 27 ]. The model achieved the following fit indices: χ 2 /df = 2.09, AGFI = 0.81, GFI = 0.85, CFI = 0.90, RMSEA = 0.07 (Fig. 1 ).
Measurement model of an infertility perception scale for women (IPS-W). χ 2 /df = 2.09, AGFI = 0.81, GFI = 0.85, CFI = 0.90, RMSEA = 0.07 χ 2 /df = Chi-square divided by degrees of freedom; AGFI = Adjustedm Goodness of Fit Index; GFI = Goodness of Fit Index; CFI = Comparative Fit Index; RMSEA = Root Mean Square Error of Approximation
Multi-trait/multi-item matrix analysis was performed. Convergent validity was validated since the correlation between each item and the total score in the corresponding subscale was 0.59–0.89, which satisfied the cut-off value of 0.40. Moreover, discriminant validity was also validated since the value derived by subtracting twice the standard error from the correlation coefficient between the item and the corresponding subscale was larger than the correlation coefficient of other sub-components.
Concurrent validity was first tested based on the correlation of the scale with ISS. The results showed a positive correlation with a correlation coefficient of 0.63 ( p < .001), while also showing positive correlations between sub-factors with correlation coefficients ranging between 0.30 and 0.54. Accordingly, the concurrent validity of the scale was validated (Table 3 ).
Secondly, the known-groups validity was tested by dividing the subjects into groups by the level of depression based on CES-D cut-off points and analyzing the differences in infertility perception among the groups. Since the results showed significant differences in infertility perception according to the level of depression, the known-groups validity was validated (F = 18.84, p < .001; Table 4 ).
The Cronbach’s alpha value of 21 infertility perception items was 0.90, and for each sub-domain, the Cronbach’s alpha value was 0.91, 0.85, 0.77, and 0.60 for Factor-1, 2, 3, and 4, respectively. According to the rationale by Hair et al. [ 19 ], a reliability ≥ 0.70 is considered acceptable for any new instrument (Table 2 ).
Factor-1 (perceived feelings) represents overall feelings about infertility, having a total explanatory power of 34.6%. Infertility not only causes psychological distress, such as sadness and loss of hope for the future [ 4 ], but it is also defined as an infertility crisis accompanied by physical, economic, and social stress [ 28 ]. The 50% of women considered this process as the most stressful experience in their life [ 29 ], while 84.2% of Korean women receiving infertility treatment experienced depression [ 4 ]. In Factor-1, the overall feeling about infertility was reflected by negative emotions including a sense of loss, anxiety, despair, depression, sadness, and guilt. Therefore, efforts are needed to understand the negative emotions of people who experience infertility and to alleviate these emotions.
Factor-2 refers to “personal stigma.” Infertility can be seen as the fault of the individual, while individuals who experience infertility perceive it as a void and a problem that is difficult to reveal. Personal stigma refers to the extent to which a person believes that negative stereotypes associated with a group they belong to will also be applied to them, while experiencing more personal stigma results in increased self-stigma, which is the feeling that the stigma is applicable to themselves [ 30 ]. Factor-2 reflects previous reports that women who marry but have not given birth are stigmatized as not fulfilling the role of a married woman and denying their own femininity [ 31 ]. Moreover, infertility diagnosis and treatment endlessly give women existential angst and hoping for a child is perceived as the attempt of women to understand their own existence even if they realize that such hope is in vain [ 32 ].
Infertility is understood differently depending on the socio-cultural context. A significant number of women diagnosed with infertility in Korea experience embarrassment and despair from their unexpected difficulties with fertility. Originally, they thought that having a child would be a natural process after getting married, and thus experience confusion about their sense of identity as a woman when this does not happen [ 33 , 34 ]. The perceptions of the Korean society surrounding infertility has been emphasized as an important factor influencing the sense of identity and emotions of individuals with infertility [ 35 ]. In other words, individuals currently undergoing infertility treatment have always perceived infertility negatively, rather than positively, as a member of the society before being diagnosed with infertility themselves. Such perception becomes palpable in their own lives once they are diagnosed with infertility, which adds stigmatizing characteristics to their perception of infertility.
Factor-3 (“social stigma”) includes stigmatizing characteristics about infertility at the societal level, believing that childbirth are an essential social rite of passage. But as they face difficulties with fertility, the views of other people become uncomfortable to women, leading them to withdraw from relationships. This could be interpreted as a factor similar to the “social withdrawal” mentioned by Fu et al. [ 12 ]. This process is well explained by studies reporting that, under the ideology of the Korean society, which regards motherhood as the main definer of a woman’s identity and has favorable views on having children, having friends or relatives that perpetrate a negative stigma around infertility and take a cruel view of women regardless of whether they are directly responsible for the infertility [ 36 ] can cause women with infertility to withdraw from relationships [ 33 ]. In fact, over 50% of Korean women who received infertility treatment experienced prejudice due to infertility and 43% reported serious withdrawal from relationships for this reason [ 34 ].
As previously described, infertility is not well understood and not adequately perceived within many societies [ 37 , 38 ]. Negative social perception about infertility can cause women who experience infertility to internalize social stigma, withdraw from interpersonal relationships, lose self-esteem, and experience decline in quality of life [ 14 ]. All items included in Factors 2 and 3 are close to problems stemming from perceived stigma, meaning one’s own belief that the society views them as a member of a stigmatized group [ 39 ].
Factor-4 reflects “acceptance,” whereby infertility is perceived as an acceptable problem that can be overcome together as a couple, rather than a problem that causes a negative outcome in life. Having no child due to infertility and the surrounding stigma can place a serious burden on the couple’s relationship and the difficulties may destroy the marriage or strengthen their bond [ 40 ]. The perception of infertility is deeply associated with the socio-cultural context due to the longstanding family norms in Korea. Recognizing infertility as a problem that can be overcome and understanding that individuals and the society must work together to resolve the problem.
Meanwhile, Factor-4 had a somewhat low reliability score of 0.60. This could be due to large differences in how positively individuals receiving infertility treatment think about infertility within their socio-cultural and familial context and how significant are their psycho-emotional changes, which may vary according to treatment stage, frequency of treatment, and outcomes during treatment (number of follicles, quality of eggs, number of embryos, quality of embryos, etc.). In the future, it may be necessary to repeat the measurements after unifying the infertility treatment on IUI or IVF or using the same measurement point like the start of treatment rather than during treatment to reduce the factors possibly acting as variables.
Concurrent validity test of the developed scale and ISS suggested that a more negative perception of infertility led to a higher perceived stigma about infertility [ 16 , 41 ]. Moreover, known-groups validity for comparing the level of infertility perception between groups showed that the depression groups perceived infertility more negatively than the normal group, consistent with a previous study [ 20 ], which validated the known-groups validity of the scale developed in the present study. The reliability of our scale corresponded to a Cronbach’s alpha of 0.90, indicating that the reliability was at an acceptable level and that all items were easily understood by women receiving infertility treatment.
Up to now, while there have been efforts to measure infertility perception, such efforts have focused mostly on the individual’s knowledge about infertility diagnosis and treatment or the stigmatizing characteristics of infertility within various socio-cultural contexts. In Korea, in particular, infertility has usually been perceived as having stigmatizing characteristics such as insufficiency and difficulty, but it is important to view infertility as a problem that can be overcome by working together, rather than a negative life event that can destroy the sense of identity of women and cause relationship problems among couples. The newly developed infertility perception scale also includes a significant number of the stigmatizing characteristics of infertility. Hence, efforts are needed to solve the issues surrounding inadequate beliefs through the understanding of the stigmatizing characteristics of infertility in order to transition towards a positive perception of infertility. Infertility not only causes individuals to experience various negative emotions but also impacts their life cycle, potentially increasing the risk of conditions such as coronary heart disease (CHD) [ 42 ] and elevating the likelihood of early menopause [ 43 ]. Therefore, a positive perception of infertility by those affected directly influences the individual, playing a crucial role in maintaining and promoting overall health.
This study convenience sampled women receiving infertility treatment at two Korean hospitals specializing in infertility treatment, limiting the generalization of the findings. In addition, although we selected two institutions with similar treatment processes and patient education, there is a limitation in that we were unable to systematically analyze the similarities and differences. Prior to data collection, the authors informed the study participants that the survey was anonymous and that their responses would be kept confidential. However, there is a limitation that, due to social stigma, participants might have underreported or misreported their true opinions. Moreover, Cultural differences across countries can significantly influence perceptions of infertility, where what may be considered stigma or acceptable responses in one culture may not be the same in another. Therefore, it is essential to assess the validity and reliability across diverse populations, including different ethnic groups, and adjust the questionnaire accordingly to ensure cultural relevance and applicability. In future research, it is recommended that the validity and reliability of the scale be tested only among women who are at the IVF stage, as IVF is in the final stage of assisted reproductive therapy. Additionally, follow-up studies should aim to identify the level of infertility perception and to develop and test the impact of nursing interventions.
The infertility perception scale developed in the present study could predict the psycho-emotional state of women receiving infertility treatment by examining their infertility perception. These findings could be used for the development of nursing interventions that could help women experiencing infertility to approach positively the problem of infertility.
We developed and tested an infertility perception scale consisting in a 4-point Likert scale with 21 items under four factors. The scale has a score range of 21–84 points, with higher scores indicating more negative perceptions of infertility. The scale can be used to measure infertility perception among women who are experiencing infertility and their spouse, family, and friends. The findings can help nurses provide support that is appropriate for individual circumstances by examining how women experiencing infertility perceive infertility.
Datasets used/or analyzed during the current study are available from the corresponding author on reasonable request.
Infertility Perception Scale for Women
Intrauterine insemination
in-Vitro Fertilization
Exploratory Factor Analysis
Confirmatory Factor Analysis
Female Infertility Stigma Instrument
Infertility Stigma Scale
Strengthening the Reporting of Observational Studies in Epidemiology
Content Validity Index
Item-Content Validity Index
Korean version of the Center for Epidemiologic Studies Depression Scale-Revised
Kaiser-Meyer-Olkin
Root-mean-square error of approximation
Adjusted Goodness-of-Fit Index
Goodness-of-Fit Index
Comparative Fit Index
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This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019R1F1A106291912).
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Department of Nursing, College of Nursing, Dankook University, Cheonan, South Korea
Department of Nursing, College of Nursing, Catholic University of Pusan, Busan, South Korea
Minkyung Ban
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Conceptualization or/and Methodology: Kim Miok. Data curation or/and Analysis: Ban Minkyung. Funding acquisition: Kim MiokInvestigation: Ban Minkyung. Project administration or/and Supervision: Kim Miok. Resources or/and Software: Kim Miok, Ban Minkyung. Validation: Kim Miok. Visualization: Kim Miok, Ban Minkyung. Writing: original draft or/and review & editing: Kim Miok, Ban Minkyung.
Correspondence to Minkyung Ban .
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This study obtained an approval from the Institutional Review Board (IRB) of Dankook University (IRB approval no.: DKU 2021-01-022). Consent was obtained from the subjects after informing them about the purpose and procedures of the study and the protection of personal information. Only those who voluntarily consented to participate in the study were included. The subjects were given a small token of appreciation for their participation.
Informed consent was obtained from all individual participants included in the study.
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Kim, M., Ban, M. Development of an infertility perception scale for women (IPS-W). BMC Women's Health 24 , 513 (2024). https://doi.org/10.1186/s12905-024-03336-0
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DOI : https://doi.org/10.1186/s12905-024-03336-0
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In this post, we'll review the purpose of literature reviews, why they are so significant, and the specific elements to include in one. Literature reviews can: 1. Provide a foundation for current research. 2. Define key concepts and theories. 3. Demonstrate critical evaluation. 4. Show how research and methodologies have evolved.
The purpose of a literature review is to: Provide a foundation of knowledge on a topic; Identify areas of prior scholarship to prevent duplication and give credit to other researchers; Identify inconstancies: gaps in research, conflicts in previous studies, open questions left from other research;
What is the purpose of a literature review? 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 ...
Writing a Literature Review. 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 ...
The introduction should clearly establish the focus and purpose of the literature review. Tip If you are writing the literature review as part of your dissertation or thesis, reiterate your central problem or research question and give a brief summary of the scholarly context. You can emphasize the timeliness of the topic ("many recent ...
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 purpose of a literature review. The four main objectives of a literature review are:. Studying the references of your research area; Summarizing the main arguments; Identifying current gaps, stances, and issues; Presenting all of the above in a text; Ultimately, the main goal of a literature review is to provide the researcher with sufficient knowledge about the topic in question so that ...
A literature or narrative review is a comprehensive review and analysis of the published literature on a specific topic or research question. The literature that is reviewed contains: books, articles, academic articles, conference proceedings, association papers, and dissertations. It contains the most pertinent studies and points to important ...
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. There are five key steps to writing a literature review: Search for relevant literature. Evaluate sources. Identify themes, debates and gaps.
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.
A literature review is a review and synthesis of existing research on a topic or research question. A literature review is meant to analyze the scholarly literature, make connections across writings and identify strengths, weaknesses, trends, and missing conversations. A literature review should address different aspects of a topic as it ...
Besides the obvious reason for students -- because it is assigned! -- a literature review helps you explore the research that has come before you, to see how your research question has (or has not) already been addressed. You identify: core research in the field. experts in the subject area. methodology you may want to use (or avoid)
The word "literature review" can refer to two related things that are part of the broader literature review process. The first is the task of reviewing the literature - i.e. sourcing and reading through the existing research relating to your research topic. The second is the actual chapter that you write up in your dissertation, thesis or ...
Narrative Reviews: The purpose of this type of review is to describe the current state of the research on a specific research topic and to offer a critical analysis of the literature reviewed. Studies are grouped by research/theoretical categories, and themes and trends, strengths and weaknesses, and gaps are identified.
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 ...
A literature review is a comprehensive summary of previous research on a topic. The literature review surveys scholarly articles, books, and other sources relevant to a particular area of research. ... "In writing the literature review, the purpose is to convey to the reader what knowledge and ideas have been established on a topic, and what ...
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 ...
A literature review and a theoretical framework are not the same thing and cannot be used interchangeably. While a theoretical framework describes the theoretical underpinnings of your work, a literature review critically evaluates existing research relating to your topic. You'll likely need both in your dissertation.
A literature review is a written work that: Compiles significant research published on a topic by accredited scholars and researchers; Surveys scholarly articles, books, dissertations, conference proceedings, and other sources; Examines contrasting perspectives, theoretical approaches, methodologies, findings, results, conclusions.
Literature Review is a comprehensive survey of the works published in a particular field of study or line of research, usually over a specific period of time, ... A literature review serves several purposes. For example, it. provides thorough knowledge of previous studies; introduces seminal works. ...
Therefore, this paper discusses the purposes of LRs in dissertations and theses. Second, the paper considers five steps for developing a review: defining the main topic, searching the literature, analyzing the results, writing the review and reflecting on the writing. Ultimately, this study proposes a twelve-item LR checklist.
PURPOSES OF A LITERATURE REVIEW 1. orient your reader by defining key concepts (theoretical) and/or providing relevant background (empirical) 2. ... The literature review is an opportunity to discover and craft your scholarly identity through the kinds of questions you engage, the discussions you enter, the critiques you launch, and the ...
A literature review can be just a simple summary of the sources, but it usually has an organizational pattern and combines both summary and synthesis. A summary is a recap of the important information of the source, but a synthesis is a re-organization, or a reshuffling, of that information. It might give a new interpretation of old material or ...
Purpose and Importance of the Literature Review. An understanding of the current literature is critical for all phases of a research study. Lingard 9 recently invoked the "journal-as-conversation" metaphor as a way of understanding how one's research fits into the larger medical education conversation. As she described it: "Imagine yourself joining a conversation at a social event.
What is a literature review? A literature review is an account of the current thinking in a specific area of study. Its purpose is to introduce the reader to what has gone before and often to provide you with a foundation that you can build on with your own research. This traditional form of review is sometimes also referred to as a narrative ...
Background: The purpose of this narrative review is to examine the literature investigating a causal relationship between stress and migraine and evaluate its implications for managing migraine. Methods: PubMed, PsycINFO and CINAHL were searched from 1988 to August 2021, identifying 2223 records evaluating the relationship between stress and migraine. Records were systematically screened. All ...
Purpose of Review This narrative review summarizes current literature on the relationship of mitochondrial biomarkers with obesity-related characteristics, including body mass index and body composition. Recent Findings Mitochondria, as cellular powerhouses, play a pivotal role in energy production and the regulation of metabolic process. Altered mitochondrial functions contribute to obesity ...
In line with Zheng et al. (Citation 2016) review, the literature search was conducted in online databases Embase, Medline Complete, CINAHL, and PsycInfo. Searches took place from July to August 2021. ... We fully appreciate that some software is intended for research purposes exclusively (as stated by 37.2% of survey respondents).
This article reports on a review of the genomic research consortium literature illustrating a wide diversity in the language that has been used over time to describe the access arrangements of ...
The purpose of this study was to develop an Infertility Perception Scale for Women (IPS-W). Initial items were based on an extensive literature review and in-depth interviews with five infertile women and fifteen women not diagnosed with infertility. Forty-one items were derived from a pilot survey. Data were collected from 203 women who had experienced intrauterine insemination (IUI) and in ...