a critical review of methodology

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Review Typologies

There are many types of evidence synthesis projects, including systematic reviews as well as others. The selection of review type is wholly dependent on the research question. Not all research questions are well-suited for systematic reviews.

• Review Typologies (from LITR-EX) This site explores different review methodologies such as, systematic, scoping, realist, narrative, state of the art, meta-ethnography, critical, and integrative reviews. The LITR-EX site has a health professions education focus, but the advice and information is widely applicable.

Review the table to peruse review types and associated methodologies. Librarians can also help your team determine which review type might be appropriate for your project. 

Reproduced from Grant, M. J. and Booth, A. (2009), A typology of reviews: an analysis of 14 review types and associated methodologies. Health Information & Libraries Journal, 26: 91-108.  doi:10.1111/j.1471-1842.2009.00848.x

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How to Write Critical Reviews

When you are asked to write a critical review of a book or article, you will need to identify, summarize, and evaluate the ideas and information the author has presented. In other words, you will be examining another person’s thoughts on a topic from your point of view.

Your stand must go beyond your “gut reaction” to the work and be based on your knowledge (readings, lecture, experience) of the topic as well as on factors such as criteria stated in your assignment or discussed by you and your instructor.

Make your stand clear at the beginning of your review, in your evaluations of specific parts, and in your concluding commentary.

Remember that your goal should be to make a few key points about the book or article, not to discuss everything the author writes.

Understanding the Assignment

To write a good critical review, you will have to engage in the mental processes of analyzing (taking apart) the work–deciding what its major components are and determining how these parts (i.e., paragraphs, sections, or chapters) contribute to the work as a whole.

Analyzing the work will help you focus on how and why the author makes certain points and prevent you from merely summarizing what the author says. Assuming the role of an analytical reader will also help you to determine whether or not the author fulfills the stated purpose of the book or article and enhances your understanding or knowledge of a particular topic.

Be sure to read your assignment thoroughly before you read the article or book. Your instructor may have included specific guidelines for you to follow. Keeping these guidelines in mind as you read the article or book can really help you write your paper!

Also, note where the work connects with what you’ve studied in the course. You can make the most efficient use of your reading and notetaking time if you are an active reader; that is, keep relevant questions in mind and jot down page numbers as well as your responses to ideas that appear to be significant as you read.

Please note: The length of your introduction and overview, the number of points you choose to review, and the length of your conclusion should be proportionate to the page limit stated in your assignment and should reflect the complexity of the material being reviewed as well as the expectations of your reader.

Write the introduction

Below are a few guidelines to help you write the introduction to your critical review.

Introduce your review appropriately

Begin your review with an introduction appropriate to your assignment.

If your assignment asks you to review only one book and not to use outside sources, your introduction will focus on identifying the author, the title, the main topic or issue presented in the book, and the author’s purpose in writing the book.

If your assignment asks you to review the book as it relates to issues or themes discussed in the course, or to review two or more books on the same topic, your introduction must also encompass those expectations.

Explain relationships

For example, before you can review two books on a topic, you must explain to your reader in your introduction how they are related to one another.

Within this shared context (or under this “umbrella”) you can then review comparable aspects of both books, pointing out where the authors agree and differ.

In other words, the more complicated your assignment is, the more your introduction must accomplish.

Finally, the introduction to a book review is always the place for you to establish your position as the reviewer (your thesis about the author’s thesis).

As you write, consider the following questions:

• Is the book a memoir, a treatise, a collection of facts, an extended argument, etc.? Is the article a documentary, a write-up of primary research, a position paper, etc.?
• Who is the author? What does the preface or foreword tell you about the author’s purpose, background, and credentials? What is the author’s approach to the topic (as a journalist? a historian? a researcher?)?
• What is the main topic or problem addressed? How does the work relate to a discipline, to a profession, to a particular audience, or to other works on the topic?
• What is your critical evaluation of the work (your thesis)? Why have you taken that position? What criteria are you basing your position on?

Provide an overview

In your introduction, you will also want to provide an overview. An overview supplies your reader with certain general information not appropriate for including in the introduction but necessary to understanding the body of the review.

Generally, an overview describes your book’s division into chapters, sections, or points of discussion. An overview may also include background information about the topic, about your stand, or about the criteria you will use for evaluation.

The overview and the introduction work together to provide a comprehensive beginning for (a “springboard” into) your review.

• What are the author’s basic premises? What issues are raised, or what themes emerge? What situation (i.e., racism on college campuses) provides a basis for the author’s assertions?
• How informed is my reader? What background information is relevant to the entire book and should be placed here rather than in a body paragraph?

Write the body

The body is the center of your paper, where you draw out your main arguments. Below are some guidelines to help you write it.

Organize using a logical plan

Organize the body of your review according to a logical plan. Here are two options:

• First, summarize, in a series of paragraphs, those major points from the book that you plan to discuss; incorporating each major point into a topic sentence for a paragraph is an effective organizational strategy. Second, discuss and evaluate these points in a following group of paragraphs. (There are two dangers lurking in this pattern–you may allot too many paragraphs to summary and too few to evaluation, or you may re-summarize too many points from the book in your evaluation section.)
• Alternatively, you can summarize and evaluate the major points you have chosen from the book in a point-by-point schema. That means you will discuss and evaluate point one within the same paragraph (or in several if the point is significant and warrants extended discussion) before you summarize and evaluate point two, point three, etc., moving in a logical sequence from point to point to point. Here again, it is effective to use the topic sentence of each paragraph to identify the point from the book that you plan to summarize or evaluate.

Questions to keep in mind as you write

With either organizational pattern, consider the following questions:

• What are the author’s most important points? How do these relate to one another? (Make relationships clear by using transitions: “In contrast,” an equally strong argument,” “moreover,” “a final conclusion,” etc.).
• What types of evidence or information does the author present to support his or her points? Is this evidence convincing, controversial, factual, one-sided, etc.? (Consider the use of primary historical material, case studies, narratives, recent scientific findings, statistics.)
• Where does the author do a good job of conveying factual material as well as personal perspective? Where does the author fail to do so? If solutions to a problem are offered, are they believable, misguided, or promising?
• Which parts of the work (particular arguments, descriptions, chapters, etc.) are most effective and which parts are least effective? Why?
• Where (if at all) does the author convey personal prejudice, support illogical relationships, or present evidence out of its appropriate context?

Keep your opinions distinct and cite your sources

Remember, as you discuss the author’s major points, be sure to distinguish consistently between the author’s opinions and your own.

Keep the summary portions of your discussion concise, remembering that your task as a reviewer is to re-see the author’s work, not to re-tell it.

And, importantly, if you refer to ideas from other books and articles or from lecture and course materials, always document your sources, or else you might wander into the realm of plagiarism.

Include only that material which has relevance for your review and use direct quotations sparingly. The Writing Center has other handouts to help you paraphrase text and introduce quotations.

Write the conclusion

You will want to use the conclusion to state your overall critical evaluation.

You have already discussed the major points the author makes, examined how the author supports arguments, and evaluated the quality or effectiveness of specific aspects of the book or article.

Now you must make an evaluation of the work as a whole, determining such things as whether or not the author achieves the stated or implied purpose and if the work makes a significant contribution to an existing body of knowledge.

Consider the following questions:

• Is the work appropriately subjective or objective according to the author’s purpose?
• How well does the work maintain its stated or implied focus? Does the author present extraneous material? Does the author exclude or ignore relevant information?
• How well has the author achieved the overall purpose of the book or article? What contribution does the work make to an existing body of knowledge or to a specific group of readers? Can you justify the use of this work in a particular course?
• What is the most important final comment you wish to make about the book or article? Do you have any suggestions for the direction of future research in the area? What has reading this work done for you or demonstrated to you?

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Trends and Motivations in Critical Quantitative Educational Research: A Multimethod Examination Across Higher Education Scholarship and Author Perspectives

• Open access
• Published: 04 June 2024

Cite this article

You have full access to this open access article

• Christa E. Winkler   ORCID: orcid.org/0000-0002-1700-5444 1 &
• Annie M. Wofford   ORCID: orcid.org/0000-0002-2246-1946 2  

To challenge “objective” conventions in quantitative methodology, higher education scholars have increasingly employed critical lenses (e.g., quantitative criticalism, QuantCrit). Yet, specific approaches remain opaque. We use a multimethod design to examine researchers’ use of critical approaches and explore how authors discussed embedding strategies to disrupt dominant quantitative thinking. We draw data from a systematic scoping review of critical quantitative higher education research between 2007 and 2021 ( N  = 34) and semi-structured interviews with 18 manuscript authors. Findings illuminate (in)consistencies across scholars’ incorporation of critical approaches, including within study motivations, theoretical framing, and methodological choices. Additionally, interview data reveal complex layers to authors’ decision-making processes, indicating that decisions about embracing critical quantitative approaches must be asset-based and intentional. Lastly, we discuss findings in the context of their guiding frameworks (e.g., quantitative criticalism, QuantCrit) and offer implications for employing and conducting research about critical quantitative research.

Avoid common mistakes on your manuscript.

Across the field of higher education and within many roles—including policymakers, researchers, and administrators—key leaders and educational partners have historically relied on quantitative methods to inform system-level and student-level changes to policy and practice. This reliance is rooted, in part, on the misconception that quantitative methods depict the objective state of affairs in higher education. This perception is not only inaccurate but also dangerous, as the numbers produced from quantitative methods are “neither objective nor color-blind” (Gillborn et al., 2018 , p. 159). In fact, like all research, quantitative data collection and analysis are informed by theories and beliefs that are susceptible to bias. Further, such bias may come in multiple forms such as researcher bias and bias within the statistical methods themselves (e.g., Bierema et al., 2021 ; Torgerson & Torgerson, 2003 ). Thus, if left unexamined from a critical perspective, quantitative research may inform policies and practices that fuel the engine of cultural and social reproduction in higher education (e.g., Bourdieu, 1977 ).

Largely, critical approaches to higher education research have been dominated by qualitative methods (McCoy & Rodricks, 2015 ). While qualitative approaches are vital, some have argued that a wider conceptualization of critical inquiry may propel our understanding of processes in higher education (Stage & Wells, 2014 ) and that critical research need not be explicitly qualitative (refer to Sablan, 2019 ; Stage, 2007 ). If scholars hope to embrace multiple ways of challenging persistent inequities and structures of oppression in higher education, such as racism, advancing critical quantitative work can help higher education researchers “expose and challenge hidden assumptions that frequently encode racist perspectives beneath the façade of supposed quantitative objectivity” (Gillborn et al., 2018 , p. 158).

Across professional networks in higher education, the perspectives of association leaders (e.g., Association for the Study of Higher Education [ASHE]) have often placed qualitative and quantitative research in opposition to each other, with qualitative research being a primary way to amplify the voices of systemically minoritized students, faculty, and staff (Kimball & Friedensen, 2019 ). Yet, given the vast growth of critical higher education research (e.g., Byrd, 2019 ; Espino, 2012 ; Martínez-Alemán et al., 2015 ), recent ASHE presidents have recognized how prior leaders planted transformative seeds of critical theory and praxis (Renn, 2020 ) and advocated for critical higher education scholarship as a disrupter (Stewart, 2022 ). With this shift in discourse, many members of the higher education research community have also grown their desire to expand upon the legacy of critical research—in both qualitative and quantitative forms.

Critical quantitative approaches hold promise as one avenue for meeting recent calls to embrace equity-mindedness and transform the future of higher education research, yet current structures of training and resources for quantitative methods lack guidance on engaging such approaches. For higher education scholars to advance critical inquiry via quantitative methods, we must first understand the extent to which such approaches have been adopted. Accordingly, this study sheds light on critical quantitative approaches used in higher education literature and provides storied insights from the experiences of scholars who have engaged critical perspectives with quantitative methods. We were guided by the following research questions:

To what extent do higher education scholars incorporate critical perspectives into quantitative research?

How do higher education scholars discuss specific strategies to leverage critical perspectives in quantitative research?

Contextualizing Existing Critical Approaches to Quantitative Research

To foreground our analysis of literature employing critical quantitative lenses to studies about higher education, we first must understand the roots of such framing. Broadly, the foundations of critical quantitative approaches align with many elements of equity-mindedness. Equity-mindedness prompts individuals to question divergent patterns in educational outcomes, recognize that racism is embedded in everyday practices, and invest in un/learning the effects of racial identity and racialized expectations (Bensimon, 2018 ). Yet, researchers’ commitments to critical quantitative approaches stand out as a unique thread in the larger fabric of opportunities to embrace equity-mindedness in higher education research. Below, we discuss three significant publications that have been widely applied as frameworks to engage critical quantitative approaches in higher education. While these publications are not the only ones associated with critical inquiry in quantitative research, their evolution, commonalities, and distinctions offer a robust background of epistemological development in this area of scholarship.

Quantitative Criticalism (Stage, 2007 )

Although some higher education scholars have applied critical perspectives in their research for many years, Stage’s ( 2007 ) introduction of quantitative criticalism was a salient contribution to creating greater discourse related to such perspectives. Quantitative criticalism, as a coined paradigmatic approach for engaging critical questions using quantitative data, was among the first of several crucial publications on this topic in a 2007 edition of New Directions for Institutional Research . Collectively, this special issue advanced perspectives on how higher education scholars may challenge traditional positivist and post-positivist paradigms in quantitative inquiry. Instead, researchers could apply (what Stage referred to as) quantitative criticalism to develop research questions centering on social inequities in educational processes and outcomes as well as challenge widely accepted models, measures, and analytic practices.

Notably, Stage ( 2007 ) grounded the motivation for this new paradigmatic approach in the core concepts of critical inquiry (e.g., Kincheloe & McLaren, 1994 ). Tracing critical inquiry back to the German Frankfurt school, Stage discussed how the principles of critical theory have evolved over time and highlighted Kincheloe and McLaren’s ( 1994 ) definition of critical theory as most relevant to the principles of quantitative criticalism. Kincheloe and McLaren’s definition of critical describes how researchers applying critical paradigms in their scholarship center concepts such as socially and historically created power structures, subjectivity, privilege and oppression, and the reproduction of oppression in traditional research approaches. Perhaps most importantly, Kincheloe and McLaren urge scholars to be self-conscious in their decision making—a tall ask of quantitative scholars operating from positivist and post-positivist vantage points.

In advancing quantitative criticalism, Stage ( 2007 ) first argued that all critical scholars must center their outcomes on equity. To enact this core focus on equity in quantitative criticalism, Stage outlined two tasks for researchers. First, critical quantitative researchers must “use data to represent educational processes and outcomes on a large scale to reveal inequities and to identify social or institutional perpetuation of systematic inequities in such processes and outcomes” (p. 10). Second, Stage advocated for critical quantitative researchers to “question the models, measures, and analytic practices of quantitative research in order to offer competing models, measures, and analytic practices that better describe experiences of those who have not been adequately represented” (p. 10). Stage’s arguments and invitations for criticalism spurred crucial conversations, many of which led to the development of a two-part series on critical quantitative approaches in New Directions for Institutional Research (Stage & Wells, 2014 ; Wells & Stage, 2015 ). With nearly a decade of new perspectives to offer, manuscripts within these subsequent special issues expanded the concepts of quantitative criticalism. Specifically, these new contributions advanced the notion that quantitative criticalism should include all parts of the research process—instead of maintaining a focus on paradigm and research questions alone—and made inroads when it came to challenging the (default, dominant) process of quantitative research. While many scholars offered noteworthy perspectives in these special issues (Stage & Wells, 2014 ; Wells & Stage, 2015 ), we now turn to one specific article within these special issues that offered a conceptual model for critical quantitative inquiry.

Critical Quantitative Inquiry (Rios-Aguilar, 2014 )

Building from and guided by the work of other criticalists (namely, Estela Bensimon, Sara Goldrick-Rab, Frances Stage, and Erin Leahey), Rios-Aguilar ( 2014 ) developed a complementary framework representing the process and application of critical quantitative inquiry in higher education scholarship. At the heart of Rios-Aguilar’s conceptualization lies the acknowledgment that quantitative research is a human activity that requires careful decisions. With this foundation comes the pressing need for quantitative scholars to engage in self-reflection and transparency about the processes and outcomes of their methodological choices—actions that could potentially disrupt traditional notions and deficit assumptions that maintain systems of oppression in higher education.

Rios-Aguilar ( 2014 ) offered greater specificity to build upon many principles from other criticalists. For one, methodologically, Rios-Aguilar challenged the notion of using “fancy” statistical methods just for the sake of applying advanced methods. Instead, she argued that critical quantitative scholars should engage “in a self-reflection of the actual research practices and statistical approaches (i.e., choice of centering approach, type of model estimated, number of control variables, etc.) they use and the various influences that affect those practices” (Rios-Aguilar, 2014 , p. 98). In this purview, scholars should ensure that all methodological choices advance their ability to reveal inequities; such choices may include those that challenge the use of reference groups in coding, the interpretation of statistics in ways that move beyond p -values for statistical significance, or the application and alignment of theoretical and conceptual frameworks that focus on the assets of systemically minoritized students. Rios-Aguilar also noted, in agreement with the foundations of equity-mindedness and critical theory, that quantitative criticalists have an obligation to translate findings into tangible changes in policy and practice that can redress inequities.

Ultimately, Rios-Aguilar’s ( 2014 ) framework focused on “the interplay between research questions, theory, method/research practices, and policy/advocacy” to identify how quantitative criticalists’ scholarship can be “relevant and meaningful” (p. 96). Specifically, Rios-Aguilar called upon quantitative criticalists to ask research questions that center on equity and power, engage in self-reflection about their data sources, analyses, and disaggregation techniques, attend to interpretation with practical/policy-related significance, and expand beyond field-level silos in theory and implications. Without challenging dominant approaches in quantitative higher education research, Rios-Aguilar noted that the field will continue to inaccurately capture the experiences of systemically minoritized students. In college access and success, for example, ignoring this need for evolving approaches and models would continue what Bensimon ( 2007 ) referred to as the Tintonian Dynasty, with scholars widely applying and citing Tinto’s work but failing to acknowledge the unique experiences of systemically minoritized students. These and other concrete recommendations have served as a springboard for quantitative criticalists, prompting scholars to incorporate critical approaches in more cohesive and congruent ways.

QuantCrit (Gillborn et al., 2018 )

As an epistemologically different but related form of critical quantitative scholarship, QuantCrit—quantitative critical race theory—has emerged as a vital stream of inquiry that applies critical race theory to methodological approaches. Given that statistical methods were developed in support of the eugenics movement (Zuberi, 2001 ), QuantCrit researchers must consider how the “norms” of quantitative research support white supremacy (Zuberi & Bonilla-Silva, 2008 ). Fortunately, as Garcia et al. ( 2018 ) noted, “[t]he problems concerning the ahistorical and decontextualized ‘default’ mode and misuse of quantitative research methods are not insurmountable” (p. 154). As such, the goal of QuantCrit is to conduct quantitative research in a way that can contextualize and challenge historical, social, political, and economic power structures that uphold racism (e.g., Garcia et al., 2018 ; Gillborn et al., 2018 ).

In coining the term QuantCrit, Gillborn et al. ( 2018 ) provided five QuantCrit tenets adapted from critical race theory. First, the centrality of racism offers a methodological and political statement about how racism is complex, fluid, and rooted in social dynamics of power. Second, numbers are not neutral demonstrates an imperative for QuantCrit researchers—one that prompts scholars to understand how quantitative data have been collected and analyzed to prioritize interests rooted in white, elite worldviews. As such, QuantCrit researchers must reject numbers as “true” and as presenting a unidimensional truth. Third, categories are neither “natural” nor given prompts researchers to consider how “even the most basic decisions in research design can have fundamental consequences for the re/presentation of race inequity” (Gillborn et al., 2018 , p. 171). Notably, even when race is a focus, scholars must operationalize and interpret findings related to race in the context of racism. Fourth, prioritizing voice and insight advances the notion that data cannot “speak for itself” and numerous interpretations are possible. In QuantCrit, this tenet leverages experiential knowledge among People of Color as an interpretive tool. Finally, the fifth tenet explicates how numbers can be used for social justice but statistical research cannot be placed in a position of greater legitimacy in equity efforts relative to qualitative research. Collectively, although Gillborn et al. ( 2018 ) stated that they expect—much like all epistemological foundations—the tenets of QuantCrit to be expanded, we must first understand how these stated principles arise in critical quantitative research.

Bridging Critical Quantitative Concepts as a Guiding Framework

Guided by these framings (i.e., quantitative criticalism, critical quantitative inquiry, QuantCrit) as a specific stream of inquiry within the larger realm of equity-minded educational research, we explore the extent to which the primary elements of these critical quantitative frameworks are applied in higher education. Across the framings discussed, the commitment to equity-mindedness contributes to a shared underlying essence of critical quantitative approaches. Not only do Stage, Rios-Aguilar, and Gillborn et al. aim for researchers to center on inequities and commit to disrupting “neutral” decisions about and interpretations of statistics, but they also advocate for critical quantitative research (by any name) to serve as a tool for advocacy and praxis—creating structural changes to discriminatory policies and practices, rather than ceasing equity-based commitments with publications alone. Thus, the conceptual framework for the present study brings together alignments and distinctions in scholars’ motivations and actualizations of quantitative research through a critical lens.

Specifically, looking to Stage ( 2007 ), quantitative criticalists must center on inequity in their questions and actions to disrupt traditional models, methods, and practices. Second, extending critical inquiry through all aspects of quantitative research (Rios-Aguilar, 2014 ), researchers must interrogate how critical perspectives can be embedded in every part of research. The embedded nature of critical approaches should consider how study questions, frameworks, analytic practices, and advocacy are developed with intentionality, reflexivity, and the goal of unmasking inequities. Third, centering on the five known tenets of QuantCrit (Gillborn et al., 2018 ), QuantCrit researchers should adapt critical race theory for quantitative research. Although QuantCrit tenets are likely to be expanded in the future, the foundations of such research should continue to acknowledge the centrality of racism, advance critiques of statistical neutrality and categories that serve white racial interests, prioritize the lived experiences of People of Color, and complicate how statistics can be one—but not the lone—part of social justice endeavors.

Over many years, higher education scholars have advanced more critical research, as illustrated through publication trends of critical quantitative manuscripts in higher education (Wofford & Winkler, 2022 ). However, the application of critical quantitative approaches remains laced with tensions among paradigms and analytic strategies. Despite recent systematic examinations of critical quantitative scholarship across educational research broadly (Tabron & Thomas, 2023 ), there has yet to be a comprehensive, systematic review of higher education studies that attempt to apply principles rooted in quantitative criticalism, critical quantitative inquiry, and QuantCrit. Thus, much remains to be learned regarding whether and how higher education researchers have been able to apply the principles previously articulated. In order for researchers to fully (re)imagine possibilities for future critical approaches to quantitative higher education research, we must first understand the landscape of current approaches.

Study Aims and Role of the Researchers

Study aims and scope.

For this study, we examined the extent to which authors adopted critical quantitative approaches in higher education research and the trends in tools and strategies they employed to do so. In other words, we sought to understand to what extent, and in what ways, authors—in their own perspectives—applied critical perspectives to quantitative research. We relied on the nomenclature used by the authors of each manuscript (e.g., whether they operated from the lens of quantitative criticalism, QuantCrit, or another approach determined by the authors). Importantly, our intent was not to evaluate the quality of authors’ applications of critical approaches to quantitative research in higher education.

Researcher Positionality

As with all research, our positions and motivations shape how we conceptualized and executed the present study. We come to this work as early career higher education faculty, drawn to the study of higher education as one way to rectify educational disparities, and thus are both deeply invested in understanding how critical quantitative approaches may advance such efforts. After engaging in initial discussions during an association-sponsored workshop on critical quantitative research in higher education, we were motivated to explore these perspectives, understand trends in our field, and inform our own empirical engagement. Throughout our collaboration, we were also reflexive about the social privileges we hold in the academy and society as white, cisgender women—particularly given how quantitative criticalism and QuantCrit create inroads for systemically minoritized scholars to combat the erasure of perspectives from their communities due to small sample sizes. As we work to understand prior critical quantitative endeavors, with the goal of creating opportunity for this work to flourish in the future, we continually reflect on how we can use our positions of privilege to be co-conspirators in the advancement of quantitative research for social justice in higher education.

This study employed a qualitatively driven multimethod sequential design (Hesse-Biber et al., 2015 ) to illuminate how critical quantitative perspectives and methods have been applied in higher education contexts over 15 years. Anguera et al. ( 2018 ) noted that the hallmark feature of multimethod studies is the coexistence of different methodologies. Unlike mixed-methods studies, which integrate both quantitative and qualitative methods, multimethod studies can be exclusively qualitative, exclusively quantitative, or a combination of qualitative and quantitative methods. A multimethod research design was also appropriate given the distinct research questions in this study—each answered using a different stream of data. Specifically, we conducted a systematic scoping review of existing literature and facilitated follow-up interviews with a subset of corresponding authors from included publications, as detailed below and in Fig.  1 . We employed a systematic scoping review to examine the extent to which higher education scholars incorporated critical perspectives into quantitative research (research question one), and we then conducted follow-up interviews to elucidate how those scholars discussed specific strategies for leveraging critical perspectives in their quantitative research (research question two).

Sequential multimethod approach to data collection and analysis

Given the scope of our work—which examined the extent to which, and in what ways, authors applied critical perspectives to quantitative higher education research—we employed an exploratory approach with a constructivist lens. Using a constructivist paradigm allowed us to explore the many realities of doing critical quantitative research, with the authors themselves constructing truths from their worldviews (Magoon, 1977 ). In what follows, we contextualize both our methodological choices and the limitations of those choices in executing this study.

Data Sources

Systematic scoping review.

First, we employed a systematic scoping review of published higher education literature. Consistent with the purpose of a scoping review, we sought to “examine the extent, range, and nature” of critical quantitative approaches in higher education that integrate quantitative methods and critical inquiry (Arskey & O’Malley, 2005 , p. 6). We used a multi-stage scoping framework (Arskey & O’Malley, 2005 ; Levac et al., 2010 ) to identify studies that were (a) empirical, (b) conducted within a higher education context, and (c) guided by critical quantitative perspectives. We restricted our review to literature published in 2007 or later (i.e., since Stage’s formal introduction of quantitative criticalism in higher education). All studies considered for review were written in the English language.

The literature search spanned multiple databases, including Academic Search Premier, Scopus, ERIC, PsychINFO, Web of Science, SocINDEX , Psychological and Behavioral Sciences Collection, Sociological Abstracts, and JSTOR. To locate relevant works, we used independent and combined keywords that reflected the inclusion criteria, with the initial search resulting in 285 unique records for eligibility screening. All screening was conducted separately by both authors using the CADIMA online platform (Kohl et al., 2018). In total, 285 title/abstract records were screened for eligibility, with 40 full-text records subsequently screened for eligibility. After separately screening all records, we discussed inconsistencies in title/abstract and full-text eligibility ratings to reach consensus. This strategy led us to a sample of 34 manuscripts that met all inclusion criteria (Fig.  2 ).

Identification of systematic scoping review sample via literature search and screening

Systematic scoping reviews are particularly well-suited for initial examinations of emerging approaches in the literature (Munn et al., 2018 ), aligning with our goal to establish an initial understanding of the landscape of critical quantitative research applications in higher education. It also relies heavily on researcher-led qualitative review of the literature, which we viewed as a vital component of our study, as we sought to identify not just what researchers did (e.g., what topics they explored or in what outlets they did so), but also how they articulated their decision-making process in the literature. Alternative methods to examining the literature, such as bibliometric analysis, supervised topic modeling, and network analysis, may reveal additional insights regarding the scope and structure of critical quantitative research in higher education not addressed in the current study. As noted by Munn et al. ( 2018 ), systematic scoping reviews can serve as a useful precursor to more advanced approaches of research synthesis.

Semi-structured Interviews

To understand how scholars navigated the opportunities and tensions of critical quantitative inquiry in their research, we then conducted semi-structured interviews with authors whose work was identified in the scoping review. For each article meeting the review criteria ( N  = 34), we compiled information about the corresponding author and their contact information as our sample universe (Robinson, 2014 ). Each corresponding author was contacted via email for participation in a semi-structured interview. There were 32 distinct corresponding authors for the 34 manuscripts, as two corresponding authors led two manuscripts each within our corpus of data. In the recruitment email, we provided corresponding authors with a link to a Qualtrics intake survey; this survey confirmed potential participants’ role as corresponding author on the identified manuscript, collected information about their professional roles and social identities, and provided information about informed consent in the study. Twenty-five authors responded to the Qualtrics survey, with 18 corresponding authors ultimately participating in an interview.

Individual semi-structured interviews were conducted via Zoom and lasted approximately 45–60 min. The interview protocol began with questions about corresponding authors’ backgrounds, then moving into questions regarding their motivations for engaging in critical approaches to quantitative methods, navigation of the epistemological and methodological tensions that may arise when doing quantitative research with a critical lens, approaches to research design, frameworks, and methods that challenged quantitative norms, and experiences with the publication process for their manuscript included in the scoping review. In other words, we asked that corresponding authors explicitly relay the thought processes underlying their methodological choices in the article(s) from our scoping review. Importantly, given the semi-structured nature of these interviews, conversations also reflected participants’ broader trajectory to and through critical quantitative thinking as well as their general reflections about how the field of higher education has grappled with critical approaches to quantitative scholarship. To increase consistency in our data collection and the nature of these conversations, the first author conducted all interviews. With participants’ consent, we recorded each interview, had interviews professionally transcribed, and then de-identified data for subsequent analysis. All interview participants were compensated for their time and contributions with a $50 Amazon gift card.

At the conclusion of each interview, participants were given the opportunity to select their own pseudonym. A profile of interview participants, along with their self-selected pseudonyms, is provided in Table  1 . Although we invited all corresponding authors to participate in interviews, our sample may reflect some self-selection bias, as authors had to opt in to be represented in the interview data. Further, interview insights do not represent all perspectives from participants’ co-authors, some of which may diverge based on lived experiences, history with quantitative research, or engagement with critical quantitative approaches.

Data Analysis

After identifying the sample of 34 publications, we began data analysis for the scoping review by uploading manuscripts to Dedoose. Both researchers then independently applied a priori codes (Saldaña, 2015 ) from Stage’s ( 2007 ) conceptualization of quantitative criticalism, Rios-Aguilar’s ( 2014 ) framework for quantitative critical inquiry, and Gillborn et al.’s ( 2018 ) QuantCrit tenets (Table  2 ). While we applied codes in accordance with Stage’s and Rios-Aguilar’s conceptualizations to each article, codes relevant to Gillborn et al.’s tenets of QuantCrit were only applied to manuscripts where authors self-identified as explicitly employing QuantCrit. Given the distinct epistemological origin of QuantCrit from broader forms of critical quantitative scholarship, codes representing the tenets of QuantCrit reflect its origins in critical race theory and may not be appropriate to apply to broader streams of critical quantitative scholarship that do not center on racism (e.g., scholarship related to (dis)ability, gender identity, sexual identity and orientation). After individually completing a priori coding, we met to reconcile discrepancies and engage in peer debriefing (Creswell & Miller, 2000 ). Data synthesis involved tabulating and reporting findings to explore how each manuscript component aligned with critical quantitative frameworks in higher education research to date.

We analyzed interview data through a multiphase process that engaged deductive and inductive coding strategies. After interviews were transcribed and redacted, we uploaded the transcripts to Dedoose for collaborative qualitative coding. The second author read each transcript in full to holistically understand participants’ insights about generating critical quantitative research. During this initial read, the second author noted quotes that were salient to our question regarding the strategies that scholars use to employ critical quantitative approaches.

Then, using the a priori codes drawn from Stage’s ( 2007 ), Rios-Aguilar’s ( 2014 ) and Gillborn et al.’s ( 2018 ) conceptualizations relevant to quantitative criticalism, critical quantitative inquiry, and QuantCrit, we collaboratively established a working codebook for deductive coding by defining the a priori codes in ways that could capture how participants discussed their work. Although these a priori codes had been previously applied to the manuscripts in the scoping review, definitions and applications of the same codes for interview analysis were noticeably broader (to align with the nature of conversations during interviews). For example, we originally applied the code “policy/advocacy”—established from Rios-Aguilar's work—to components from the implications section of scoping review manuscripts. When (re)developed for deductive coding of interview data, however, we expanded the definition of “policy/advocacy” to include participants’ policy- and advocacy-related actions (beyond writing) that advanced critical inquiry and equity for their educational communities.

In the final phase of analysis, each research team member engaged in inductive coding of the interview data. Specifically, we relied on open coding (Saldaña, 2015 ) to analyze excerpts pertaining to participants’ strategies for employing critical quantitative approaches that were not previously captured by deductive codes. Through open coding, we used successive analysis to work in sequence from a single case to multiple cases (Miles et al., 2014 ). Then, as suggested by Saldaña ( 2015 ), we collapsed our initial codes into broader categories that allowed us insight regarding how participants’ strategies in critical quantitative research expanded beyond those which have been previously articulated. Finally, to draw cohesive interpretations from these data, we independently drafted analytic memos for each interview participant’s transcript, later bridging examples from the scoping review that mapped onto qualitative codes as a form of establishing greater confidence and trustworthiness in our multimethod design.

In introducing study findings through a synthesized lens that heeds our multimethod design, we organize the sections below to draw from both scoping review and interview data. Specifically, we organize findings into two primary areas that address authors’ (1) articulated motivations to adopt critical approaches to quantitative higher education research, and (2) methodological choices that they perceive to align with critical approaches to quantitative higher education research. Within these sections, we discuss several coherent areas where authors collectively grappled with tensions in motivation (i.e., broad motivations, using coined names of critical approaches, conveying positionality, leveraging asset-based frameworks) and method (i.e., using data sources and choosing variables, challenging coding norms, interpreting statistical results), all of which signal authors’ efforts to embody criticality in quantitative research about higher education. Given our sequential research questions, which first examined the landscape of critical quantitative higher education research and then asked authors to elucidate their thought processes and strategies underlying their approaches to these manuscripts, our findings primarily focus on areas of convergence across data sources; we do, however, highlight challenges and tensions authors faced in conducting such work.

Articulated Motivations in Critical Approaches to Quantitative Research

To date, critical quantitative researchers in higher education have heeded Stage’s ( 2007 ) call to use data to reveal the large-scale perpetuation of inequities in educational processes and outcomes. This emerged as a defining aspect of higher education scholars’ critical quantitative work, as all manuscripts ( N  = 34) in the scoping review articulated underlying motivations to identify and/or address inequities.

Often, these motivations were reflected in the articulated research questions ( n  = 31; 91.2%). For example, one manuscript sought to “critically examine […] whether students were differentially impacted” by an educational policy based on intersecting race/ethnicity, gender, and income (Article 29, p. 39). Others sought to challenge notions of homogeneity across groups of systemically minoritized individuals by “explor[ing] within-group heterogeneity” of constructs such as sense of belonging among Asian American students (Article 32, p. iii) and “challenging the assumption that [economically and educationally challenged] students are a monolithic group with the same values and concerns” (Article 31, p. 5). These underlying motivations for conducting critical quantitative research emerged most clearly in the named approaches, positionality statements, and asset-based frameworks articulated in manuscripts.

Adopting the Coined Names of Quantitative Criticalism, QuantCrit, and Related Approaches

Based on the inclusion criteria applied in the scoping review, we anticipated that all manuscripts would employ approaches that were explicitly critical and quantitative in nature. Accordingly, all manuscripts ( N  = 34; 100%) adopted approaches that were coined as quantitative criticalism , QuantCrit , critical policy analysis (CPA), critical quantitative intersectionality (CQI) , or some combination of those terms. Twenty-one manuscripts (61.8%) identified their approach as quantitative criticalism, nine manuscripts (26.5%) identified their approach as QuantCrit, two manuscripts (5.9%) identified their approach as CPA, and two manuscripts (5.9%) identified their approach as CQI.

One of the manuscripts that applied quantitative criticalism broadly described it as an approach that “seeks to quantitatively understand the predictors contributing to completion for a specific population of minority students” (Article 34, p. 62), noting that researchers have historically “attempted to explain the experiences of [minority] students using theories, concepts, and approaches that were initially designed for white, middle and upper class students” (Article 34, p. 62). Although this example speaks only to the limited context and outcomes of one study, it highlights a broader theme found across articles; that is, quantitative criticalism was often leveraged to challenge dominant theories, concepts, and approaches that failed to represent systemically minoritized individuals’ experiences. In challenging dominant theories, QuantCrit applications were most explicitly associated with critical race theory and issues of racism. One manuscript noted that “QuantCrit recognizes the limitations of quantitative data as it cannot fully capture individual experiences and the impact of racism” (Article 29, p. 9). However, these authors subsequently noted that “quantitative methodology can support CRT work by measuring and highlighting inequities” (Article 29, p. 9). Several scholars who employed QuantCrit explicitly identified tenets of QuantCrit that they aimed to address, with several authors making clear how they aligned decisions with two tenets establishing that categories are not given and numbers are not neutral.

Despite broadly applying several of the coined names for critical realms of quantitative research, interview data revealed that several authors felt a palpable tension in labeling. Some participants, like Nathan, questioned the surface-level engagement that may come with coined names: “I don’t know, I think it’s the thinking and the thought processes and the intentionality that matters. How invested should we be in the label?” Nathan elaborated by noting how he has shied away from labeling some of his work as quantitative criticalist , given that he did not have a clear answer about “what would set it apart from the equity-minded, inequality-focused, structurally and systematically-oriented kind of work.” Similarly, Leo stated how labels could (un)intentionally stop short of the true mission for the research, recalling that he felt “more inclined to say that I’m employing critical quantitative leanings or influences from critical quant” because a true application of critical epistemology should be apparent in each part of the research process. Although most interview participants remained comfortable with labeling, we also note that—within both interview data and the articles themselves—authors sometimes presented varied source attributions for labels and conflated some of the coined names, representing the messiness of this emerging body of research.

Challenging Objectivity by Conveying Researcher Positionality

Positionality statements acknowledge the influence of scholars’ identities and social positions on research decisions. Quantitative research has historically been viewed as an objective, value-neutral endeavor, with some researchers deeming positionality statements as unnecessary and inconsistent with the positivist paradigm from which such work is often conducted. Several interviewed authors noted that positivist or post-positivist roots of quantitative research characterized their doctoral training, which often meant that their “original thinking around statistics and research was very post-positivist” (Carter) or that “there really wasn’t much of a discussion, as far as I can remember as a doc student, about epistemology or ontology” (Randall). Although positionality statements have been generally rare in quantitative research studies, half of the manuscripts in our sample ( n  = 17; 50.0%) included statements of researcher positionality. One interview participant, Gabrielle, discussed the importance of positionality statements as one way to challenge norms of quantitative research in saying:

It’s not objective, right? I think having more space to say, “This is why I chose the measures I chose. This is how I’m coming to this work. This is why it matters to me. This is my positioning, right?” I think that’s really important in quantitative work…that raises that level of consciousness to say these are not just passive, like every decision you make in your research is an active decision.

While Gabrielle, as well as Carter and Randall, all came to be advocates of positionality statements in quantitative scholarship through different pathways, it became clear through these and other interviews that positionality statements were one way to bring greater transparency to a traditionally value-neutral space.

As an additional source of contextual data, we reviewed submission guidelines for the peer-reviewed journals in which manuscripts were published. Not one of the 15 peer-reviewed outlets represented in our scoping review sample required that authors include positionality statements. One outlet, Journal of Diversity in Higher Education (where two scoping review articles were printed), offered “inclusive reporting standards” where they recommended that authors include reflexivity and positionality statements in their submitted manuscripts (American Psychological Association, 2024 ). Another outlet, Teachers College Record (where one scoping review article was printed), mentioned positionality statements in their author instructions. Yet, Teachers College Record did not require nor recommend the inclusion of author positionality statements; rather, they offered recommendations if authors chose to include them. Specifically, they suggested that if authors chose to include a positionality statement, it should be “more than demographic information or abstract statements” (Sage Journals, 2024 ). The remaining 13 peer-reviewed outlets from the scoping review data made no mention of author reflexivity or positionality in their author guidelines.

When present, the scoping review revealed that positionality statements varied in form and content. Some positionality statements were embedded in manuscript narratives, while others existed as separate tables with each author’s positionality represented as a separate row. In content, it was most common for authors to identify how their identities and experiences motivated their work. For example, one author noted their shared identity with their research participants as a low-income, first-generation Latina college student (Article 2, p. 25). Another author discussed the identity that they and their co-author shared as AAPI faculty, making the research “personally relevant for [them]” (Article 11, p. 344),

In interviews, participants recalled how the relationship between their identities, lived experiences, and motivations for critical approaches to quantitative research were all intertwined. Leo mentioned, “naming who we are in a study helps us be very forthright with the pieces that we’re more likely to attend to.” Yet, Leo went on to say that “one of the most cosmetic choices that people see in critically oriented quantitative research is our positionality statements,” which other participants noted about how information in positionality statements is presented. In several interviews, authors’ reflections on whether these statements should appear as lists of identities or deeper statements about reflexivity presented a clear tension. For some, positionality statements were places to “identify ourselves and our social locations” (David) or “brand yourself” as a critical quantitative scholar to meet “trendy” writing standards in this area (Michelle). Yet, others felt such statements fall short in revealing “how this study was shaped by their background identities and perspectives” (Junco) or appear to “be written in response to the context of the research or people participating” (Ginger). Ultimately, many participants felt that shaping honest positionality statements that better convey “the assumptions, and the biases and experiences we’ve all had” (Randall) was one area where quantitative higher education scholars could significantly improve their writing to reflect a critical lens.

Some manuscripts also clarified how authors’ identities and social positions reshaped the research process and product. For instance, authors of one manuscript reported being “guided by [their] cultural intuition” throughout the research (Article 17, p. 218). Alternatively, another author described the narrative style of their manuscript as intentionally “autobiographical and personally reflexive” in order “to represent the connections [they] made between [their] own experiences and findings that emerged” from their work (Article 28, p. 56). Taken together, among the manuscripts that explicitly included positionality statements, these remarks make clear that authors had widely varying approaches to their reflexivity and writing processes.

Actualizing Asset-Based Frameworks

Notably, conceptual and theoretical frameworks emerged as a common way for critical quantitative scholars to pursue equitable educational processes and outcomes in higher education research. Nearly all ( n  = 32; 94.1%) manuscripts explicitly challenged dominant conceptual and theoretical models. Some authors enacted this challenge by countering canonical constructs and theories in the framing of their study. For example, several manuscripts addressed critiques of theoretical concepts such as integration and sense of belonging in building the conceptual framework for their own studies. Other manuscripts were constructed with the underlying goal to problematize and redefine frameworks, such as engagement for Latina/e/o/x students or the “leaky pipeline” discourse related to broadening participation in the sciences.

Across interviews, participants challenged deficit framings or “traditional” theoretical and conceptual approaches in many ways. Some frameworks are taken as a “truism in higher ed” (Leo), such as sense of belonging and Astin’s ( 1984 ) I-E-O model, and these frameworks were sometimes purposefully used to disrupt their normative assumptions. Randall, for one, recalled using a more normative higher education framework but opted to think about this framework “as more culturalized” than had previously been done. Further, Carter noted that “thinking about the findings in an anti-deficit lens” comprised a large portion of critical quantitative approaches. Using frameworks for asset-based interpretation was further exemplified by Caroline stating, “We found that Black students don’t do as well, but it’s not the fault of Black students.” Instead, Caroline challenged deficit understandings through the selected framework and implications for institutional policy. Collectively, challenging normative theoretical underpinnings in higher education was widely favored among participants, and Jackie hoped that “the field continues to turn a critical lens onto itself, to grow and incorporate new knowledges and even older forms of knowledge that maybe it hasn’t yet.”

Alternatively, some participants discussed rejecting widely used frameworks in higher education research in favor of adapting frameworks from other disciplines. For example, QuantCrit researchers drew from critical race theory (and related frameworks, such as intersectionality) to quantitatively examine higher education topics in ways that value the knowledge of People of Color. In using these frameworks, which have origins in critical legal and Black feminist theorization, interview participants noted how important it was “to put yourself out there with talking about race and racism” (Isabel) and connect the statistics “back to systems related to power, privilege, and oppression [because] it’s about connecting [results] to these systemic factors that shape experience, opportunities, barriers, all of that kind of stuff” (Jackie). Further, several authors related pulling theoretical lenses from sociology, gender studies, feminist studies, and queer studies to explore asset-based theorization in higher education contexts and potentially (re)build culturally relevant concepts for quantitative measurement in higher education.

Embodying Criticality in Methodological Sources, Approaches, and Interpretations

Moving beyond underlying motivations of critical quantitative higher education research, scoping review authors also frequently actualized the task of questioning and reconstructing “models, measures, and analytic practices [to] better describe experiences of those who have not been adequately represented” (Stage, 2007 , p. 10). Common across all manuscripts ( N  = 34) was the discussion of specific ways in which authors’ critical quantitative approaches informed their analytic decisions. In fact, “analytic practices” was by far the most prevalent code applied to the manuscripts in our dataset, with 342 total references across the 34 manuscripts. This amounted to 20.8% of the excerpts in the scoping review dataset being coded as reflecting critical quantitative approaches to analytic practices, specifically.

Interestingly, many analytic approaches reflected what some would consider “standard” quantitative methodological tools. For example, manuscripts employed factor analysis to assess measures, t-tests to examine differences between groups, and hierarchical linear regression to examine relationships in specific contexts. Some more advanced, though less commonly applied, methods included measurement invariance testing and latent class analysis. Thus, applying a critical quantitative lens tended not to involve applying a separate set of analytic tools; rather, the critical lens was reflected in authors’ selection of data sources and variables, approaches to data coding and (dis)aggregation, and interpretation of statistical results.

Selecting Data Sources and Variables

Although scholars were explicit in their underlying motivations and approaches to critical quantitative research, this did not often translate into explicitly critical data collection endeavors. Most manuscripts ( n  = 29; 85.3%) leveraged existing measures and data sources for quantitative analysis. Existing data sources included many national, large-scale datasets including the Educational Longitudinal Study (NCES), National Survey of Recent College Graduates (NSF), and the Current Population Survey (U.S. Census Bureau). Other large-scale data sources reflecting specific higher education contexts and populations included the HEDS Diversity and Equity Campus Climate Survey, Learning About STEM Student Outcomes (LASSO) platform, and National Longitudinal Survey of Freshmen. Only five manuscripts (14.7%) conducted analysis using original data collected and/or with newly designed measures.

It was apparent, however, that many authors grappled with challenges related to using existing data and measures. Interview participants’ stories crystallized the strengths and limitations of secondary data. Over half of the interview participants in our study spoke about their choices regarding quantitative data sources. Some participants noted that surveys “weren’t really designed to ask critical questions” (Sarah) and discussed the issues with survey data collected around sex and gender (Jessica). Still, Sarah and Jessica drew from existing survey data to complicate the higher education experiences they aimed to understand and tried to leverage critical framing to question “traditional” definitions of social constructs. In another discussion about data sources and the design of such sources, Carter expanded by saying:

I came in without [being] able to think through the sampling or data collection portion, but rather “this is what I have, how do I use it in a way that is applying critical frameworks but also staying true to the data themselves.” That is something that looks different for each study.

In discussing quantitative data source design, more broadly, Tyler added: “In a lot of ways, all quantitative methods are mixed methods. All of our measures should be developed with a qualitative component to them.” In the scoping review articles, one example of this qualitative component is evident within the cognitive interviews that Sablan ( 2019 ) employed to validate survey items. Finally, several participants noted how crucial it is to “just be honest and acknowledge the [limitations of secondary data] in the paper” (Caroline) and “not try to hide [the limitations]” (Alexis), illustrating the value of increased transparency when it comes to the selection and use of existing quantitative data in manuscripts advancing critical perspectives.

Regardless of data source, attention to power, oppression, and systemic inequities was apparent in the selection of variables across manuscripts. Many variables, and thus the associated models, captured institutional contexts and conditions. The multilevel nature of variables, which extended beyond individual experiences, aligned with authors’ articulated motivations to disrupt inequitable educational processes and outcomes, which are often systemic and institutionalized in nature. For one, David explained key motivations behind his analytic process: “We could have controlled for various effects, but we really wanted to see how are [the outcomes] differing by these different life experiences?” David’s focus on moving past “controlling” for different effects shows a deep level of intentionality that was reflected among many participants. Carter expanded on this notion by recalling how variable selection required, “thinking through how I can account for systemic oppression in my model even though it’s not included in the survey…I’ve never seen it measured.” Further, Leo discussed how reflexivity shaped variable selection and shared: “Ultimately, it’s thinking about how do these environments not function in value-neutral ways, right? It’s not just selecting X, Y, and Z variable to include. It’s being able to interrogate [how] these variables represent environments that are not power neutral.” The process of selecting quantitative data sources and variables was perhaps best summed up by Nick, who concisely shared, “it’s been very iterative.” Indeed, most participants recalled how their methodological processes necessitated reflexivity—an iterative process of continually revisiting assumptions one brings to the quantitative research process (Jamieson et al., 2023 )—and a willingness to lean into innovative ways of operationalizing data for critical purposes.

Challenging the Norms of Coding

An especially common way of enacting critical principles in quantitative research was to challenge traditional norms of coding. This emerged in three primary ways: (1) disaggregation of categories to reflect heterogeneity in individuals’ experiences, (2) alternative approaches to identifying reference groups, and (3) efforts to capture individuals’ intersecting identities. Across manuscripts, authors often intentionally disaggregated identity subgroups (e.g., race/ethnicity, gender) and ran distinct analytical models for each subgroup separately. In interviews, Junco expressed that running separate models was one way that analyses could cultivate a different way of thinking about racial equity. Specifically, Junco challenged colleagues’ analytic processes by asking whether their research questions “really need to focus on racial comparison?” Junco then pushed her colleagues by asking, “can we make a different story when we look at just the Black groups? Or when we look at only Asian groups, can we make a different story that people have not really heard?” Isabel added that focusing on measurement for People of Color allowed for them (Isabel and her research collaborators) to “apply our knowledge and understanding about minoritized students to understand what the nuances were.” In nearly one third of the manuscripts ( n  = 11; 32.4%), focusing on single group analyses emerged as one way that QuantCrit scholars disrupted the perceived neutrality of numbers and how categories have previously been established to serve white, elite interests. Five of those manuscripts (14.7%) explicitly focused on understanding heterogeneity within systemically minoritized subpopulations, including Asian American, Latina/e/o/x, and Black students.

It was not the case, however, that authors avoided group comparisons altogether. For example, one team of authors used separate principal components analysis (PCA) models for Indigenous and non-Indigenous students with the explicit intent of comparing models between groups. The authors noted that “[t]ypically, monolithic comparisons between racial groups perpetuate deficit thinking and marginalization.” However, they sought to “highlight the nuance in belonging for Indigenous community college students as it differs from the White-centric or normative standards” by comparing groups from an asset-driven perspective (Article 5, p. 7). Thus, in cases where critical quantitative scholars included group comparisons, the intentionality underlying those choices as a mechanism to highlight inequities and/or contribute to asset-based narratives was apparent.

Four manuscripts (11.8%) were explicit in their efforts to identify alternative analytic methods to normative reference groups. Reference groups are often required when building quantitative models with categorical variables such as racial/ethnic and gender identity. Often, dominant identities (e.g., respondents who are white and/or men) comprise the largest portion of a research sample and are selected as the comparison group, typifying experiences of individuals with those dominant identities. To counter the traditional practice of reference groups, some manuscript authors stated using effect coding, often referencing the work of Mayhew and Simonoff ( 2015 ), and dynamic centering as two alternatives. Effect coding (used in three manuscripts) removes the need for a reference group; instead, all groups are compared to the overall sample mean. Dynamic centering (used in one manuscript), on the other hand, uses a reference group but one that is intentionally selected based on the construct in question, as opposed to relying on sample size or dominant identities.

Interview participants also discussed navigating alternative coding practices, with several authors raising key points about their exposure to and capacity building for effect coding. As Angela described, effect coding necessitates that “you don’t choose a specific group as your benchmark to do the comparison. And you instead compare to the group.” Angela then stated that this approach made more sense than choosing benchmarks, as she felt uncomfortable identifying one group as a comparison group. Junco, however, noted that “effect coding was much more complicated than what I thought,” as she reflected on unlearning positivist strategies in favor of equity-focused approaches that could elucidate greater nuance. Importantly, using alternative coding practices was not universal among manuscripts or interview participants. One manuscript utilized traditional dummy coding for race in regression models, with white students as the reference group to which all other groups were compared. The authors explicated that “using white students as the reference [was] not a result of ‘privileging’ them or maintaining the patterns of power related to racial categorizations” (Article 8, p. 1282). Instead, they argued that the comparison was a deliberate choice to “reveal patterns of racial or ethnic educational inequality compared to the privileged racial group” (Article 8, p. 1282). Another author maintained the use of reference groups purely for ease of interpretation. David shared, “it’s easier for the person to just look at it and compare magnitudes.” However, by prioritizing the benefit of easy interpretation with traditional reference groups, authors may incur other costs (such as sustaining unnecessary comparisons to white students). Additionally, several manuscripts ( n  = 13; 38.2%) employed analytic coding practices that aimed to account for intersectionality. While authors identified these practices by various names (e.g., interaction terms, mediating variables, conditional effects) they all afforded similar opportunities. The most common practice among authors in our sample ( n  = 8; 23.5%) was computing interaction terms to account for intersecting identities, such as race and gender. Specifically pertaining to intersectionality, Alexis summarized many researchers’ tensions well in sharing, “I know what Kimberlé Crenshaw says. But how do I operationalize that mathematically into something that’s relevant?” In offering one way that intersectionality could be realized with quantitative data, Tyler stated that “being able to keep in these variables that are interacting [via interaction terms] and showing differences” may align with the core ideas of intersectionality. Yet, participants also recognized that statistics would inherently always fall short of representing respondents’ lived experiences, as discussed by Nick: “We disaggregate as far as we can, but you could only go so far, and like, how do we deal with tension.” Several other participants reflected on bringing in open-text response data about individuals’ social identities, categorizing racial and ethnic groups according to continent (while also recognizing that this did not necessarily attend to the complexities of diasporas), or making decisions about groups that qualify as “minoritized” based on disciplinary and social movements. Collectively, the disparate approaches that authors used and discussed directly speak to critical higher education scholars’ movement away from normative comparisons that did not meaningfully answer questions related to (in)equity and/or intersectionality in higher education.

Interpreting Statistical Results

One notable, albeit less common, way higher education scholars enacted critical quantitative approaches through analytic methods was by challenging traditional ways of reporting and interpreting statistical results. The dominant approach to statistical methods aligns with a null hypothesis significance testing (NHST) approach, whereby p -values—used as indicators of statistically significant effects—serve to identify meaningful results. NHST practices were prevalent in nearly all scoping review manuscripts; yet, there were some exceptions. For example, three manuscripts (8.8%) cautioned against reliance on statistical significance due to its dependence on large sample size (i.e., statistical power), which is often at odds with centering research on systemically minoritized populations. One of those manuscripts (2.9%) even chose to interpret nonsignificant results from their quantitative analyses. In a similar vein, two manuscripts (5.9%) also questioned and adapted common statistical practices related to model selection (e.g., using corrected Akaike information criteria (AIC) instead of p -values) and variable selection (e.g., avoiding use of variance explained so as not to “[exclude] marginalized students from groups with small representations in the data” (Article 23, p. 7). Meanwhile, others attended to raw numeric data and uncertainty associated with quantitative results. The resources to enact these alternative methodological practices were briefly discussed by Tyler through his interview, in which he shared: “The use of p -values is so poorly done that the American Statistical Association has released a statement on p -values, an entire special collection [and people in my field] don’t know those things exist.” Tyler went on to share that this knowledge barrier was tied to the siloed nature of academia, and that such siloes may inhibit the generation of critical quantitative research that draws from different disciplinary origins.

Among interviewed authors, many also viewed interpretation as a stage of quantitative research that required a high level of responsibility and awareness of worldview. Nick related that using a QuantCrit approach changed how he was interpreting results, in “talking about educational debts instead of gaps, talking about racism instead of race.” As demonstrated by Nick, critical interpretations of statistics necessitate congruence with theoretical or conceptual framing, as well, given the explicit call to interrogate structures of inequity and power in research adopting a critical lens. Leo described this responsibility as a necessary challenge:

It’s very easy to look at results and interpret them—I don’t wanna say ‘as is’ because I don’t think that there is an ‘as is’—but interpret them in ways that they’re traditionally interpreted and to keep them there. But, if we’re truly trying to accomplish these critical quantitative themes, then we need to be able to reference these larger structures to make meaning of the results that are put in front of us.

Nick, Leo, and several other participants all emphasized how crucial interpretation is in critical quantitative research in ways that expanded beyond statistical practices; ultimately, the perspective that “behind every number is a human” served as a primary motivation for many authors in fulfilling the call toward ethical and intentional interpretation of statistics.

Leveraging a multimethod approach with 15 years of published manuscripts ( N  = 34) and 18 semi-structured interviews with corresponding authors, this study identifies the extent to which principles of quantitative criticalism, critical quantitative inquiry, and QuantCrit have been applied in higher education research. While scholars are continuing to develop strategies to enact a critical quantitative lens in their studies—a path we hope will continue, as continued questioning, creativity, and exploration of new possibilities underscore the foundations of critical theory (Bronner, 2017 )—our findings do suggest that higher education researchers may benefit from intentional conversations regarding specific analytic practices they use to advance critical quantitative research (e.g., confidence intervals versus p -values, finite mixture models versus homogeneous distribution models).

Our interviews with higher education scholars who produced such work also fills a need for guidance on strategies to enact critical perspectives in quantitative research, addressing an absence of such from most quantitative training and resources. By drawing on the work and insights of higher education researchers engaging critical quantitative approaches, we provide a foundation on which future scholars can imagine and implement a fuller range of possibilities for critical inquiry via quantitative methods in higher education. In what follows, we discuss the findings of this study alongside the frameworks from which they drew inspiration. Then, we offer implications for research and practice to catalyze continued exploration and application of critical quantitative approaches in higher education scholarship.

Synthesizing Key Takeaways

First, scoping review data revealed several commonalities across manuscripts regarding authors’ underlying motivations to identify and/or address inequities for systemically minoritized populations—speaking to how critical quantitative approaches can fall within the larger umbrella of equity-mindedness in higher education research. Such motivations were reflected in authors’ research questions and frameworks (consistent with Stage’s ( 2007 ) initial guidance). Most manuscripts identified their approach as quantitative criticalism broadly, although there were sometimes blurred boundaries between approaches termed quantitative criticalism, QuantCrit, critical policy analysis, and critical quantitative intersectionality. Notably, authors’ decisions about which framing their work invoked also determined how scholars enacted a specified critical quantitative approach. For example, the tenets of QuantCrit, offered by Gillborn et al. ( 2018 ), were specifically heeded by researchers seeking to take up a QuantCrit lens. Scholars who noted inspiration from Rios-Aguilar ( 2014 ) often drew specifically from the framework for critical quantitative inquiry. While the key ingredients of these critical quantitative approaches were offered in the foundational framings we introduced, the field has lacked understanding on how scholars take up these considerations. Thus, the present findings create inroads to a conversation about applying and extending the articulated components associated with critical quantitative higher education research.

Second, our multimethod approach illuminated general agreement (in manuscripts and interviews) that quantitative research in higher education—whether explicitly critical or not—is not neutral nor objective. However, despite positionality being a key part of Rios-Aguilar’s ( 2014 ) critical quantitative inquiry framework, only half of the manuscripts included researcher positionality. Thus, while educational researchers may agree that, without challenging objectivity, quantitative methods serve to uphold inequity (e.g., Arellano, 2022 ; Castillo & Babb, 2024 ), higher education scholars may not have yet established consensus on how these principles materialize. To be clear, consensus need not be the goal of critical quantitative approaches, given that critical theory demands constant questioning for new ways of thinking and being (Bronner, 2017 ); yet, greater solidarity among critical quantitative higher education researchers may be beneficial, so that community-based discussions can drive the actualization of equity-minded motivations. Interview data also revealed complications in how scholars choose if, and how, to define and label critical quantitative approaches. Some participants struggled with whether their work was “critical enough” to be labeled as such. Those conversations raise concerns that critical quantitative research in higher education could—or potentially has—become an exclusionary space where level of criticality is measured by an arbitrary barometer (refer to Garvey & Huynh, 2024 ). Meanwhile, other participants worried that attaching such a label to their work was irrelevant (i.e., that it was the motivations and intentionality underlying the work that mattered, not the label). Although the field remains in disagreement regarding if/how labeling should be implemented for critical quantitative approaches, “it is the naming of experience and ideologies of power that initiates the process [of transformation] in its critical form” (Hanley, 2004 , p. 55). As such, we argue that naming critical quantitative approaches can serve as a lever for transforming quantitative higher education research and create power in related dialogue.

Implications for Future Studies on Critical Quantitative Higher Education Research

As with any empirical approach, and especially those that are gaining traction (as critical quantitative approaches are in higher education; Wofford & Winkler, 2022 ), there is utility in conducting research about the research . First, in the context of higher education as a broad field of applied research, there is a need to illustrate what critical quantitative scholars focus on when they conceptualize higher education in the first place. For example, is higher education viewed as a possibility for social mobility? Or are critical quantitative scholars viewing postsecondary institutions as engines of inequity? Second, it was notable that—among the manuscripts including positionality statements—it was common for such statements to read as biographies (i.e., lists of social identities) rather than as reflexive accounts about the roles/commitments of the researcher(s). Future research would benefit from a deeper understanding of the enactment of positionality in critical quantitative higher education research. Third, given the productive tensions associated with naming and understanding the (dis)agreed upon ingredients between quantitative criticalism, critical quantitative inquiry, QuantCrit, as well as additional known and unknown conceptualizations, further research regarding how higher education scholars grapple with definitions, distinctions, and adaptations of these related approaches will clarify how scholars can advance their critical commitments with quantitative postsecondary data.

Implications for Employing Critical Quantitative Higher Education Research

Emerging analytical tools for critical quantitative research.

In terms of employing critical quantitative approaches in higher education research, there is significant room for scholars to explore emerging quantitative methodological tools. We agree with López et al.’s ( 2018 ) assessment that critical quantitative work tends to remain demographic and/or descriptive in its methodological nature, and there is great potential for more advanced inferential quantitative methods to serve critical aims. While there are some examples in the literature—for example, Sablan’s ( 2019 ) work in the realm of quantitative measurement and Malcom-Piqueux’s (2015) work related to latent class analysis and other person-centered modeling approaches—additional examples of advanced and innovative analytical tools were limited in our findings. Thus, integrating more advanced quantitative methodological tools into critical quantitative higher education research, such as finite mixture modeling (as noted by Malcom-Piqueux, 2015), measurement invariance testing, and multi-group structural equation modeling, may advance the ways in which scholars address questions related to heterogeneity in the experiences and outcomes of college students, faculty, and staff.

Traditional quantitative analytical tools have historically highlighted between-group differences that perpetuate deficit narratives for systemically minoritized students, faculty, and staff on college campuses; for example, comparing the educational outcomes of Black students to white students. Emerging approaches such as finite mixture modeling hold promise in unearthing more nuanced understandings. Of growing interest to many critical quantitative scholars is heterogeneity within minoritized populations; finite mixture modeling approaches such as growth mixture modeling, latent class analysis, and latent profile analysis are particularly well suited to reveal within-group differences that are otherwise obfuscated in most quantitative analyses. Although we found a few examples in our scoping review of authors who leveraged more traditional group comparisons for equity-minded aims, these emerging analytical approaches may be better suited for the questions asked by future critical quantitative scholars.

One Size Does Not Fit All

Many emerging analytical tools demonstrate promise in advancing conversations about inequity, particularly related to heterogeneity in subpopulations on college and university campuses. As noted previously, however, Rios-Aguilar ( 2014 ) noted that critical quantitative research need not rely solely on “fancy” or advanced analytical tools; in fact, our findings did not lead us to conclude that higher education scholars have established a set of analytical approaches that are explicitly critical in nature. Rather, our results revealed a common theme: that critical quantitative scholarship in higher education necessitates an elevated degree of intentionality in selection, application, and interpretation of whichever analytical approaches—advanced or not—scholars choose.

As noted, there were several instances in our data where commonly critiqued analytical approaches were still applied in the critical quantitative literature. For example, we found manuscripts that conducted a monolithic comparison of Indigenous and non-Indigenous students and the utilization of traditional dummy coding with white students as a normative reference group. What made these manuscripts distinct from more non-critical quantitative research was the thoughtfulness and intentionality with which those approaches were selected to serve equity-minded goals—an intentionality that was explicitly communicated to readers in the methods section of manuscripts. Just as the inclusion of positionality statements in half of the manuscripts suggests that researcher objectivity was generally not assumed by higher education scholars conducting critical quantitative scholarship, choices that often otherwise go unquestioned were interrogated and discussed in manuscripts.

Cokley and Awad ( 2013 ) share several recommendations for advancing social justice research via quantitative methods. One of their recommendations addresses the utilization of racial group comparisons in quantitative analyses. They do not suggest that researchers avoid comparisons between groups altogether, but rather they avoid “unnecessary” comparisons between groups (p. 35). They elaborate that, “[t]here should be a clear research questions that necessitates the use of the comparison” if utilized in quantitative research with critical aims (Cokley & Awad, 2013 , p. 35). Our findings suggested that—in the current state of critical quantitate scholarship in higher education—it is not so much about a specific set of approaches deeming scholarship as critical (or not), but rather about asking critical questions (as Stage initially called us to do in 2007) and then selecting methods that align with those goals.

Opportunities for Training and Collaboration

Notably, many of the emerging analytical approaches mentioned require a significant degree of methodological training. The limited use of such tools, which are otherwise well-suited for critical quantitative applications, points to a potential disconnect in training of higher education scholars. Some structured opportunities for partnership between disciplinary and methodological scholars have emerged via training programs such as the Quantitative Research Methods (QRM) for STEM Education Scholars Program (funded by the National Science Foundation Award 1937745) and the Institute on Mixture Modeling for Equity-Oriented Researchers, Scholars and Educators (IMMERSE) fellowship (funded by the Institute for Education Sciences Award R305B220021). These grant-funded training opportunities connect quantitative methodological experts with applied researchers across educational contexts.

We must consider additional ways, both formal and informal, to expand training opportunities for higher education scholars with interest in both advanced quantitative methods and equity-focused research; until then, expertise in quantitative methods and critical frameworks will likely inhabit two distinct communities of scholars. For higher education scholars to fully embrace the potential of critical quantitative research, we will be well served by intentional partnerships across methodological (e.g., quantitative and qualitative) and disciplinary (e.g., higher education scholars and methodologists) boundaries. In addition to expanding applied researchers’ analytical skillsets, training and collaboration opportunities also prepare potential critical quantitative scholars in higher education to select methodological approaches, whether introductory or advanced, that most closely align with their research aims.

Historically, critical inquiry has been viewed primarily as an endeavor for qualitative research. Recently, educational scholars have begun considering the possibilities for quantitative research to be leveraged in support of critical inquiry. However, there remains limited work evaluating whether and to what extent principles from quantitative criticalism, critical quantitative inquiry, and QuantCrit have been applied in higher education research. By drawing on the work and insights of scholars engaging in critical quantitative work, we provide a foundation on which future scholars can imagine and implement a vast range of possibilities for critical inquiry via quantitative methods in higher education. Ultimately, this work will allow scholars to realize the potential for research methodologies to directly support critical aims.

Data Availability

The list of manuscripts generated from the scoping review analysis is available via the Online Supplemental Materials Information link. Given the nature of our sample and topics discussed, interview data will not be shared publicly to protect participant anonymity.

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Acknowledgements

This research was supported by a grant from the American Educational Research Association, Division D. The authors gratefully thank Dr. Jason (Jay) Garvey for his support as an early thought partner with regard to this project, and Dr. Christopher Sewell for his helpful feedback on an earlier version of this manuscript, which was presented at the 2022 Association for the Study of Higher Education meeting.

This research was supported by a grant from the American Educational Research Association, Division D.

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Winkler, C.E., Wofford, A.M. Trends and Motivations in Critical Quantitative Educational Research: A Multimethod Examination Across Higher Education Scholarship and Author Perspectives. Res High Educ (2024). https://doi.org/10.1007/s11162-024-09802-w

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A model of contributors to a trusting patient-physician relationship: a critical review using a systematic search strategy

• Seraina Petra Lerch 1 , 2 , 3 ,
• Rahel Hänggi 4 ,
• Yara Bussmann 4 &
• Andrea Lörwald 4  

BMC Primary Care volume  25 , Article number:  194 ( 2024 ) Cite this article

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The lack of trust between patients and physicians has a variety of negative consequences. There are several theories concerning how interpersonal trust is built, and different studies have investigated trust between patients and physicians that have identified single factors as contributors to trust. However, all possible contributors to a trusting patient-physician relationship remain unclear. This review synthesizes current knowledge regarding patient-physician trust and integrates contributors to trust into a model.

A systematic search was conducted using the databases MEDLINE (Ovid), Embase (Ovid), PsycINFO (Ovid), and Eric (Ovid). We ran simultaneous searches for a combination of the phrases: patient-physician relationship (or synonyms) and trust or psychological safety. Six-hundred and twenty-five abstracts were identified and screened using pre-defined criteria and later underwent full-text article screening. We identified contributors to trust in the eligible articles and critically assessed whether they were modifiable.

Forty-five articles were included in the review. Patient-centered factors that contributed modifiable promoters of trust included psychological factors, levels of health education and literacy, and the social environment. Physician-centered factors that added to a trusting patient-physician relationship included competence, communication, interest in the patient, caring, the provisioning of health education, and professionalism. The patient-physician alliance, time spent together, and shared decision-making also contributed to trusting relationships between patients and physicians. External contributors included institutional factors, how payments are made, and additional healthcare services.

Our model summarized modifiable contributors to a trusting patient-physician relationship. We found that providing sufficient time during patient-physician encounters, ensuring continuity of care, and fostering health education are promising starting points for improving trust between patients and physicians. Future research should evaluate the effectiveness of interventions that address multiple modifiable contributors to a trusting patient-physician relationship.

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Introduction

Trust, as a cornerstone of human relationships, applies to the patient-physician relationship. Relationship building is a basic skill for the medical professional [ 1 , 2 ]. There is evidence that trust between patients and doctors has a positive effect and, if trust is missing, leads to potentially negative consequences. A meta-analysis confirmed that trust was positively associated with improved health outcomes [ 3 ] in, for example, diabetes [ 4 ], cancer [ 5 ], and human immunodeficiency virus infections (HIV infections) [ 6 ]. Trust also increases positive behavioral outcomes in patients [ 7 ], such as treatment adherence [ 8 , 9 ]. In contrast, low trust in physicians has been shown to negatively affect various patient health outcomes [ 4 , 6 , 10 , 11 , 12 , 13 , 14 ]. Economically, if trust in physicians is missing, it has adverse financial effects on healthcare systems [ 15 ]. Furthermore, a physician may be more likely to incur complaints when trusting relationships with patients are lacking [ 16 ].

In medicine, trust can be understood as being social or interpersonal [ 17 , 18 ]. Social trust refers to individuals’ trust in institutions or systems, such as the healthcare system or physicians in general, while interpersonal trust refers to the trust between two individuals [ 18 , 19 ]. Social trust is believed to affect interpersonal trust in medical settings [ 17 , 18 ]. There are various theories of trust from different disciplines [ 20 , 21 , 22 , 23 ]. However, the most prominent interpersonal trust theory in psychology (and applied in medical settings) is from Mayer et al., who defined trust as the willingness of an individual to be vulnerable to the actions of another based on the expectation that the other will perform a particular action important to the trustor, irrespective of the ability to monitor or control the other party [ 24 ]. Their theory of interpersonal trust suggests that benevolence, integrity, ability, propensity to trust, and perceived risk are components of a trust relationship [ 24 ]. When applied to the patient-physician relationship, the physician’s ability, integrity, and benevolence act as contributors. At the same time, a patient’s propensity to trust—their willingness to trust others—and the perceived risk they take when trusting a physician are also important factors. However, the reality is likely more complex, and there are probably more contributors to a trusting patient-physician relationship than the theory proposes. While different evidence-based studies have investigated the patient-physician trust relationship, to our knowledge, there has been no synthesis of all the evidence-based contributors to the relationship. In 2000, there was a call for an empirical conceptualization of trust. Rather than single theories used to explain interpersonal patient-physician trust or studies investigating isolated contributors of trust, the idea was to synthesize empirical evidence concerning how patient-physician trust can evolve into a model [ 19 ]. A recent review on trust in the medical field has renewed the need for such an empirical conceptualization of patient-physician trust [ 25 ]. Therefore, this study aimed to summarize the empirical evidence, identify the contributors to a trusting patient-physician relationship, and integrate them into a model. This model can then be used to identify potential approaches and leverage points to improve patient-physician trust. The two main research questions were:

Which factors contribute to a trusting patient-physician relationship?

Which of these factors can act as potential leverage points to improve the patient-physician relationship?

In addition, we critically assessed contributors based on how they are already implemented in healthcare systems and medical education.

As the research questions were too broad for a systematic or scoping review, a critical review with a systematic search approach was used to answer the first research question. Critical reviews focus on empirical research [ 26 ] to evaluate what is known about a specific topic and integrate it into a framework [ 26 , 27 ]. They may use a systematic search strategy to integrate the strengths of systematic and critical reviews [ 27 ], including all relevant literature, to avoid biases.

Search strategy

We searched the databases MEDLINE (Ovid), Embase (Ovid), PsycINFO (Ovid), and ERIC (Ovid) for a combination of terms (or synonyms) referring to the patient-physician relationship and trust or psychological safety. Database searches were run simultaneously as multifile searches in Ovid. For the results, Ovid’s de-duplicator was used. No study or clinical trial registries or online resources were searched. No experts were contacted, nor was a citation search conducted. A reproducible search for all of the databases is as follows:

Embase (1974 to January 13, 2022), ERIC (1965 to May 2021), Ovid MEDLINE(R) ALL (1946 to January 13, 2022), APA PsycInfo (1806 to January Week 1, 2022).

(patient* adj2 physician* adj2 (relation* or alliance or rapport)).ti, ab.

(trust* or psychological safety).ti, ab.

remove duplicates from 3.

We did not use any language, time period, study design, or other restrictions for the searches, and no search filters were used. The comprehensive literature search was run on January 13, 2022 and 630 articles were retrieved. An information specialist assisted in framing the research questions and provided information on the different types of reviews. Once a first draft of the search strategy was developed, multiple feedback rounds with the information specialist were conducted until the search strategy was finalized.

Screening process

Fifty-three records were retrieved from Ovid MEDLINER ALL, 509 from Embase, 1 from ERIC, and 67 from APA PsycInfo. In total, 630 records were found. As OVID’s de-duplication process did not identify all duplicates, any remaining duplicates were removed by SPL using EndNote’s duplicate identification strategy and a manual approach. After de-duplication, 613 articles remained, which were screened in two rounds. The first round was screened according to title and abstract. In the second round, 116 articles were evaluated for inclusion based on the full texts. SPL and RH did the screening, and AL decided when there were disagreements between SPL and RH. A study selection flowchart is shown in Fig.  1 .

PRISMA study flowchart

We included studies that reported factors contributing to a trusting relationship between patients and physicians and excluded those that only reported contributing factors between patients and health professionals other than physicians (or no contributors). We also included studies that explicitly measured trust between a patient and physician either quantitatively or qualitatively and excluded those with no measure of patient trust in physicians (e.g., only generalized patient trust or trust in other health professionals). We included quantitative, qualitative, and mixed methods papers and excluded dissertations and conference abstracts. Only articles in English and German were included.

Data synthesis and categorization

We first extracted the contributors mentioned in the studies as this review focused on integrating contributors to a trusting patient-physician relationship into an overall model. Extracted data included information on the setting, patients, physicians, how trust was operationalized, and which factors had a positive, negative, or no effect on the relationship. Contributors were then categorized into patient-related, physician-related, context-related, or patient- and physician-related factors. Study sizes and methods of measurement were highlighted. The factors were then synthesized, and the modifiable ones were extracted and displayed in a model.

Forty-five heterogeneous studies reported factors contributing to a trusting patient-physician relationship. An overview of these studies, including the contributors to trust for each study, can be found in Appendix 1 .

Patient-related factors

Several patient-related contributors to a trusting relationship were investigated, sometimes with contrasting results from different studies. These included demographic characteristics (gender, marital status, age, ethnicity, birthplace, and country of residence), health condition, health education and literacy, socioeconomic status, religious beliefs, social environment, psychological factors, and the patient’s health condition and status.

Studies found better mental and physical health status tended to positively affect the relationship—although this result was mixed. In several studies, a good general health condition and better self-reported health status were associated with increased trust towards the physician [ 28 , 29 , 30 , 31 , 32 ]. However, other studies found no correlation between self-reported health status and trust. For specific health conditions, low-risk adults without chronic illnesses had higher trust in their physicians than adults with risk factors such as diabetes or high lipid levels [ 33 , 34 , 35 , 36 , 37 ]. Disease progression, including relapses and lack of improvement of a medical condition, was negatively associated with trust [ 38 , 39 ], whereas a shorter duration of illness increased trust in the physician [ 29 ]. However, two studies found no connection between trust, disease duration [ 40 ], and healing [ 36 ].

Patient health education and literacy levels were found to promote a trusting patient-physician relationship, with higher health education [ 41 ] and literacy [ 42 ] levels contributing to trust and low health literacy [ 43 ] hindering it.

Patient socioeconomic status, including occupation, employment, educational and income levels, and the presence or type of health insurance, were all potential contributors, with high (household) income and educational levels, having health insurance, and being employed positively related to trust; although, these findings were ambiguous. Religious belief was also associated with trust in physicians [ 31 ].

The social environment, including social support and the care experiences of family members, further contributed to a trusting patient-physician relationship. In particular, poor social support negatively influenced trust [ 43 ], as did dissatisfaction with the care of family members [ 44 , 45 ].

The health locus of control was also associated with trust. This describes how a person views control of their health. An internal health locus of control suggests that the person sees oneself as controlling their health, whereas an external locus means that the person perceives external factors influencing their health.

Several patient psychological factors, including a propensity to trust, their coping mechanisms and attachment style, the health locus of control, and general trust in caregivers contributed to a trusting patient-physician relationship. Individuals who see powerful others as their health locus of control (i.e., believing other people, such as health professionals, can control their health) exhibited higher trust in physicians [ 46 ]. Poor coping styles hindered trust [ 43 ], while the willingness to reframe situations (a healthy coping style) added to a trusting patient-physician relationship [ 44 ]. For the most part, a general trust in doctors, caregivers, the healthcare system, or online health communities was associated with higher trust [ 47 , 48 , 49 ]. However, these findings were ambiguous regarding the propensity to trust. One study found that a patient’s propensity to trust predicted trust in their physician [ 50 ], although other studies did not find this connection [ 31 , 36 ]. Table  1 summarizes all of the evidence concerning patient-related factors.

Physician-related factors

Demographic characteristics, competence, communication, exploring, caring, provisioning health education, reputation, professionalism, and availability were investigated as potential contributors to a trusting patient-physician relationship.

Demographic characteristics of the physician, such as age and gender, did not contribute to a trusting relationship, although these findings were ambiguous.

Physician competency, including the perceived competence of the physician by the patient [ 41 , 44 , 51 , 55 , 68 , 69 , 70 ], the physician being up-to-date in their specialization [ 71 ], and having more years of experience [ 71 ] helped to build a trusting relationship with patients. Communication skills, including general communication skills [ 29 , 38 , 44 , 52 , 70 , 72 , 73 ], compassion, listening to the patient [ 41 , 44 , 52 ], as well as nonverbal behavior such as good eye contact, providing undivided attention, open body language, and smiling [ 41 , 44 , 52 , 73 ] also enhanced the trust relationship as did patient-centered [ 63 , 74 , 75 ], comprehensive care [ 30 ].

Physicians exploring a patient’s disease and problems [ 69 ], illness experiences [ 28 ], and the context of the patient [ 44 , 68 ] promoted a trusting relationship along with caring behavior [ 52 , 70 , 75 ] such as empathy [ 50 , 76 ] and compassion [ 41 , 44 , 69 ].

Provisioning health education to the patient contributed to a trusting relationship [ 38 , 41 , 64 , 68 , 69 ]; however, one study did not find any association between these factors [ 71 ].

We did identify physician reputation [ 71 ] and the reputation of their medical specialty [ 28 , 34 , 39 ] as contributing to a trusting relationship. Moreover, different aspects of professionalism [ 71 , 73 ], such as honesty [ 51 , 55 , 69 ] and availability [ 41 ], contributed to a trusting patient-physician relationship, while being disrespectful, arrogant, or cynical were negatively associated with trust [ 41 , 73 , 75 ]. These results are summarized in Table  2 .

Physician- and patient-related factors

Contributors related to the physician and patient were concordance, time spent together, the patient-physician alliance, and shared decision-making.

In relation to concordance, both gender and race were tested as promoters of trust; however, only gender concordance was identified as being a contributor [ 35 , 41 ].

Time spent together included time spent in a single session, the overall time spent together, and the continuity of care. Most results indicated that more time spent together in a single session [ 28 , 71 , 73 ] (with the physician giving the patient enough time to explain the reason for the visit [ 77 ]) promoted trust, whereas physicians appearing rushed was a barrier to a trusting relationship [ 44 ]. If the duration of the relationship with the doctor was long-term [ 28 , 36 , 77 ], the patient had higher rates of follow-up visits [ 51 ] and more physician visits in general [ 37 , 57 ]. Nevertheless, those findings were mixed, and not all studies found an association between the duration of a relationship with the doctor [ 40 , 45 ] and the number of team visits [ 47 , 48 ]. However, continuity of care [ 51 ] and continuity with one physician added to a trusting relationship [ 30 ].

Within the patient-physician alliance, alliances in shared decision-making [ 65 ] and having a good rapport [ 71 ] were found to enhance trust, while a patient’s perception of a physician’s distrust was a barrier [ 41 ]. Finding common ground [ 28 ] and shared identity [ 52 ] were tested but did not show any association with trust. In contrast, shared decision-making contributed to a trusting relationship that promoted trust in most studies [ 41 , 42 , 44 ]. These findings are summarized in Table  3 .

Context-related factors

Context-related factors such as practice/institution, physician payments, and additional healthcare services were investigated as potential contributors to trusting relationships.

Most aspects of the practice or the institution were found to contribute to a trusting relationship, with easy accessibility [ 30 ] to the practice and a good reputation [ 71 ] promoting trust, while institutional betrayal [ 65 ] hindered it. The atmosphere of the practice also mattered. A good practice or organizational climate added to a trusting relationship [ 35 ], whereas perceived chaos hampered it [ 29 ]. Patients having enough physician choice also added to a trusting relationship [ 48 ], while managed care settings contributed to mistrust [ 41 ]. Inpatient settings enhanced trust compared to outpatient settings [ 59 ]. Regarding payments, situations where patients do not know how the physician is paid or the physician is paid by the number of office visits rather than a fixed salary [ 30 ] contributed to a trusting relationship. In contrast, public disclosure of payments was negatively associated with trust [ 78 ]. Additional health services such as addiction consultations [ 79 ], preventive services [ 77 ], and the coordination of specialty care [ 30 ] also contributed to patient-physician trust. These findings are summarized in Table  4 .

Potential leverage points to improve a trusting relationship

We integrated the modifiable contributors to a trusting patient-physician relationship from each conceptual group into a model and identified potential leverage points for improving the relationship (Fig.  2 ).

Model of contributors to a trusting patient-physician relationship

Patient-centered leverage points

Within patient-centered factors, health education and literacy, the social environment, and psychological factors were modifiable. A patient who is better educated about health and can understand and use this education for themselves (health literacy) may form better trusting relationships with their physicians; thus, interventions should focus on improving health education and literacy. Patient psychological factors such as coping styles and health locus of control are other potential leverage points to increase trust within the relationship. The social environment, specifically receiving sufficient social support, was a further modifiable contributor to trust, indicating that targeted interventions should aim to improve patients’ social support systems.

Physician-centered leverage points

We identified physicians’ competence, communication skills, exploring, caring, the provisioning of health education, and professionalism as modifiable contributors to a trusting patient-physician relationship. For competence, being up-to-date in the specialization and perceived as competent are leverage points that could increase trust. Communication skills, including verbal and nonverbal behavior, exploring patient health, and professionalism, can also be learned and are, hence, modifiable. Caring, including empathy and compassion, is a skill that can be increased through interventions and also used to increase trust. In addition, physicians can be taught how to provide health education, and specific material can be provided to them for health education, which is another potential leverage point.

Patient and physician-centered leverage points

We identified shared decision-making, the patient-physician alliance, and time spent together as contributors that can be modified. Although time spent together and the continuity of care is context-dependent, awareness can be raised among physicians, and specific training can help the physician allow patients to explain the reason for their visit. Alliances and shared decision-making are skills taught during medical school: therefore, potential interventions already exist. Shared decision-making also includes healthcare professionals other than physicians. Therefore, one possible intervention strategy would be to foster interprofessional education and teamwork to support shared decision-making between patients and healthcare professionals.

Context-dependent leverage points

The healthcare system, provisioning of additional healthcare services, transparency regarding physician payment, and characteristics of the practice or institution (e.g., keeping a good institutional climate and having mechanisms to prevent institutional betrayal) are modifiable contributors; however, these strongly depend on the specific country. Furthermore, only a few studies have investigated contributors to a trusting relationship within this conceptual group. Therefore, the list of context-dependent contributors may be limited.

We conducted a critical review with a systematic search strategy to identify evidence-based contributors to a trusting patient-physician relationship and integrated the modifiable contributors into a model. Our results confirm the existing theory of interpersonal trust [ 24 ], and, in line with this theory, we found that the physician’s caring (benevolence), competence and communication (ability), and professionalism (integrity) were contributors to a trusting patient-physician relationship. In addition, the physician’s exploring and provisioning of health education also contributed to a trusting relationship. We confirmed the importance of a patient’s propensity to trust as a psychological contributor and were able to add more psychological factors, including coping style and health locus of control. We further added the patient’s level of health education and literacy, and social environment as contributing factors and confirmed that, as the risk a patient must take concerning their health decreases, the easier it is for them to trust the physician. Our model further adds physician- and patient-related factors and the institutional context. The latter indicates the importance of including social trust in understanding interpersonal trust in medicine, as suggested by Mechanic [ 18 ]. One highly prominent factor was health education, which can be addressed by the physician, patient, and the context, which suggests that fostering health education is a promising intervention to increase trust.

Patient psychological factors such as coping styles and health locus of control are modifiable contributors to a trusting relationship. Previous studies have shown that coping styles can be improved for chronically ill patients [ 80 ], while other interventions can address a patient’s health locus of control and improve their social support systems. Furthermore, social support interventions have been shown to be effective in patients with different diseases [ 81 , 82 , 83 ]. Health education could be addressed through e-learning and by provisioning self-help groups that exchange ideas about diseases [ 84 ] with educational tools and teaching materials [ 85 ]. However, these interventions are system-related as the healthcare system must offer those interventions.

Medical education

Most physician-centered modifiable contributors to a trusting relationship fall under the scope of medical education. Competence is acquired and addressed through university education, graduate school, and continuing education. Communication skills are taught in medical education courses, and professionalism is addressed as a CanMED role [ 86 ]. Exploration is an important skill that is already part of communication curriculums [ 87 ] and is based on the common-sense model of illness [ 88 ]. Physicians can be taught to provide health education [ 89 ]; however, it is a skill that medical students find difficult to achieve [ 90 ]. Further intervention possibilities could address a physician’s ability to express compassion and empathy. A recent review summarized educational methods used to address medical student empathy [ 91 ], with simulation training shown to be an effective tool [ 92 ].

A practical example that implements the described practices can be found in the Presence 5 project, which teaches physicians to better listen to patients, explore their story and emotions, and connect with them. These teachings have had positive effects on the physicians’ attitude, compassion, communication, and exploring behavior [ 93 , 94 ].

Patient- and physician-related factors

As with physician-related contributors to trust, patient- and physician-related promoters of trust could be addressed through medical education. Building an alliance with patients and learning about shared decision-making are skills taught in medical school [ 95 ]. The physician can also be made aware that spending sufficient time with a patient is relevant to building trust; however, the ability to modify this contributor is dependent on the healthcare and billing system.

Context-dependent contributors

We found that a transparent billing system and institution-related contributors such as reputation, medical practice atmosphere, accessibility, and additional healthcare services contributed to a trusting patient-physician relationship. A recent discussion on making health care more accessible can be found in Gupta et al. [ 96 ].

One healthcare system that addresses many of these factors is Canada’s patient-centered model: ‘the patient’s medical home.’ Under this model, patients can choose a physician they feel comfortable with and who will continuously manage their health care over their lifespan. Each physician is surrounded by a team that considers the patient’s situation and may provide additional healthcare services when needed. This model ensures that each patient receives comprehensive and accessible care that provides sufficient time with the physician and guarantees continuity of care [ https://patientsmedicalhome.ca/ , 97 ]. Over the long term, patient medical homes have led to better care, decreased costs, and more satisfaction for providers and patients [ https://patientsmedicalhome.ca/ , 97 ]. Other positive aspects of the patient’s medical home, aside from increased continuity of care and the availability of additional health care services, may lie within the aspect of time spent together [ 98 ] or improved disease progression [ 99 ], which is also addressed within the patient’s medical homes.

Strengths and limitations

The strength of this critical review lies in the systematic search approach, which only included papers that operationalized or specifically described trust. Despite this approach, we cannot ensure that we have included all empirical contributors to patient-physician trust that have been researched. While the systematic search did limit bias in the identified contributors within the critical assessment of what could be modifiable or not, the critical assessment could be biased through the author’s background. However, we discussed the process in depth as a team.

Our search strategy included psychological safety as a synonym for trust, as well as the terms rapport, alliance, and relationship. We checked indexed search terms to ensure the inclusion of relevant synonyms. In the past, trust was more conceptualized as rapport or alliance, whereas today, it is associated with a newer term: “psychological safety.” While we tried to include relevant search terms, we might have missed some, limiting the results.

While our search was not limited to patients trusting their physicians, most papers focused on this and excluded physicians’ trust in their patients. Dyadic analyses of patient-physician trust are scarce. However, Petrocchi et al. (2019) have begun investigating patient-physician trust as a dyad [ 100 ]. Some papers only reported correlations of trust with unmodifiable, less relevant, but easy-to-gather factors, such as sex or age. Thus, more contributors to trust may have yet to be investigated.

Implications for future research

Interestingly, many non-modifiable or insignificant contributors, such as physician or patient demographics, were investigated in almost every study we reviewed. However, the most promising contributors, such as health education, were barely explored. Future research should investigate modifiable and promising contributors to a trusting relationship that have, as yet, been barely researched, including patient psychological factors and additional healthcare services. Additionally, factors that have not been investigated should be addressed, including digitized healthcare settings and how telemedicine, chatbots, and video consultations affect patients’ trust in physicians. Further research should also focus on measuring how successful physician interventions are, as previous research and interventions have not increased patient trust [ 101 , 102 ]. Future interventions should also consider multiple contributors to trust, as they are all related. For such interventions, the outcomes for each contributor should be evaluated first, with trust as a secondary outcome.

As the present review aimed to create a model of patient-physician trust, only studies that included trust between patients and physicians were included, with other healthcare professionals excluded. However, research has already acknowledged the importance of trusting relationships for all healthcare professionals [ 103 ], which should be further expanded. Thus, shared contributors to trust between healthcare professionals, their differences, and potential leverage points should also be identified.

Implications for practice

Our critical review has demonstrated that there are more contributors to a trusting patient-physician relationship than the theory of interpersonal trust proposes, and the context in which the patient-physician relationship takes place is highly relevant. One way to increase trust within the patient-physician relationship is to implement healthcare systems that are organized similarly to the Canadian ‘patient’s medical homes’ model. Changing the healthcare system is also an effective tool to simultaneously address multiple contributors to trust.

At the level of the institution, enhancing trust should focus on health education, which can be addressed through the implementation of self-help and support groups, providing high-quality health educational material, and training healthcare professionals.

At the physician level, we recommend taking as much time as possible for each patient to explore their perspective and current situation, organize (as much as possible) continuity of care, and ensure patient health education.

Using a systematic search, our model summarizes identified modifiable contributors to a trusting patient-physician relationship. Providing sufficient time during patient-physician encounters, ensuring continuity of care, and fostering health education are promising leverage points for improving trust between patients and physicians. Future research should evaluate the effectiveness of interventions that address multiple modifiable contributors to a trusting patient-physician relationship.

Data availability

The data (review search) of the current review are available from the corresponding author on reasonable request.

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Acknowledgements

We would like to thank the information specialist team at the library of the medical faculty of Bern. They supported the development of our research question and a search strategy. We also thank Adrian Michel (mediamatician) for the model illustration. The preliminary results of this review were presented at the European Health Psychology Conference on August 27, 2022 in Bratislava.

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Lerch, S.P., Hänggi, R., Bussmann, Y. et al. A model of contributors to a trusting patient-physician relationship: a critical review using a systematic search strategy. BMC Prim. Care 25 , 194 (2024). https://doi.org/10.1186/s12875-024-02435-z

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Barriers and facilitators to mental health treatment access and engagement for LGBTQA+ people with psychosis: a scoping review protocol

• Cláudia C. Gonçalves   ORCID: orcid.org/0000-0001-6767-0920 1 ,
• Zoe Waters 2 ,
• Shae E. Quirk 1 ,
• Peter M. Haddad 1 , 3 ,
• Ashleigh Lin 4 ,
• Lana J. Williams 1 &
• Alison R. Yung 1 , 5  

Systematic Reviews volume  13 , Article number:  143 ( 2024 ) Cite this article

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The prevalence of psychosis has been shown to be disproportionately high amongst sexual and gender minority individuals. However, there is currently little consideration of the unique needs of this population in mental health treatment, with LGBTQA+ individuals facing barriers in accessing timely and non-stigmatising support for psychotic experiences. This issue deserves attention as delays to help-seeking and poor engagement with treatment predict worsened clinical and functional outcomes for people with psychosis. The present protocol describes the methodology for a scoping review which will aim to identify barriers and facilitators faced by LGBTQA+ individuals across the psychosis spectrum in help-seeking and accessing mental health support.

A comprehensive search strategy will be used to search Medline, PsycINFO, Embase, Scopus, LGBTQ+ Source, and grey literature. Original studies of any design, setting, and publication date will be included if they discuss barriers and facilitators to mental health treatment access and engagement for LGBTQA+ people with experiences of psychosis. Two reviewers will independently screen titles/abstracts and full-text articles for inclusion in the review. Both reviewers will then extract the relevant data according to pre-determined criteria, and study quality will be assessed using the Joanna Briggs Institute (JBI) critical appraisal checklists. Key data from included studies will be synthesised in narrative form according to the Guidance on the Conduct of Narrative Synthesis in Systematic Reviews.

The results of this review will provide a comprehensive account of the current and historical barriers and facilitators to mental healthcare faced by LGBTQA+ people with psychotic symptoms and experiences. It is anticipated that the findings from this review will be relevant to clinical and community services and inform future research. Findings will be disseminated through publication in a peer-reviewed journal and presented at conferences.

Scoping review registration

This protocol is registered in Open Science Framework Registries ( https://doi.org/10.17605/OSF.IO/AT6FC ).

Peer Review reports

The prevalence of psychotic disorders in the general population has been estimated to be around 0.27–0.75% [ 1 , 2 ], with the lifetime prevalence of ever having a psychotic experience being estimated at 5.8% [ 3 ]. However, rates of psychotic symptoms and experiences are disproportionately high amongst LGBTQA+ populations, with non-heterosexual individuals estimated to be 1.99–3.75 times more likely to experience psychosis than their heterosexual peers [ 4 , 5 , 6 , 7 ]. Additionally, it has been estimated that transgender or gender non-conforming (henceforth trans) individuals are 2.46–49.7 times more likely than their cisgender peers (i.e. individuals whose gender identity is the same as their birth registered sex) to receive a psychotic disorder diagnosis [ 8 , 9 ]. The increased rates of psychotic experiences noted amongst gender and sexual minorities may be explained by evidence indicating that LGBTQA+ people are also exposed to risk factors for psychosis at a far greater rate than members of the general population, such as childhood adversity [ 10 , 11 , 12 ], minority stress [ 13 ], discrimination [ 14 ], and stigma [ 15 , 16 ]. Furthermore, there is added potential for diagnostic biases leading to over-diagnosing psychosis in gender diverse individuals, whose gender expression and dysphoria may be pathologized by mental health service providers [ 8 ].

Despite these concerning statistics, there is very little research examining the experiences of LGBTQA+ people with psychosis, and limited consideration of the unique needs these individuals may have in accessing and engaging with mental health services. While timely access to treatment has consistently been associated with better symptomatic and functional outcomes for people with psychosis [ 17 , 18 ], there are often delays to treatment initiation which are worsened for LGBTQA+ individuals [ 19 , 20 ]. These individuals face additional barriers to accessing adequate mental health support compared to cisgender/heterosexual people [ 19 ] and may need to experiment with several mental health services before finding culturally competent care [ 20 ]. This in turn may lead to longer duration of untreated psychosis. Additionally, there seems to be a lack of targeted support for this population from healthcare providers, with LGBTQA+ individuals with serious mental health concerns reporting higher rates of dissatisfaction with psychiatric services than their cisgender and heterosexual counterparts [ 7 , 14 , 21 ]. However, the extent of these differences varies across contexts [ 22 ], potentially due to improved education around stigma and LGBTQA+ issues within a subset of mental health services.

Nonetheless, stigma remains one of the highest cited barriers to help-seeking for mental health problems, particularly with regard to concerns around disclosure [ 23 ], which can be particularly challenging for people experiencing psychosis [ 24 , 25 ]. Stigma stress in young people at risk for psychosis is associated with less positive attitudes towards help-seeking regarding both psychiatric medication and psychotherapy [ 26 ], potentially partly due to fears of judgement and being treated differently by service providers [ 27 ]. This issue may be compounded for people who also belong to minoritized groups [ 23 , 28 ], particularly as LGBTQA+ individuals have reported experiencing frequent stigma and encountering uninformed staff when accessing mental healthcare [ 7 , 29 ]. Furthermore, stigma-fuelled hesitance to access services may be heightened for trans people [ 30 ] whose identities have historically been pathologized and conflated with experiences of psychosis [ 31 ].

Even when individuals manage to overcome barriers to access support, there are added challenges to maintaining adequate treatment engagement. In a large online study, half of trans and nearly one third of LGB participants reported having stopped using mental health services in the past because of negative experiences related to their gender identity or sexuality [ 20 ]. This can be particularly problematic as experiences of stigma predict poorer medication adherence in psychosis [ 32 ] which subsequently multiplies the risk for relapse and suicide [ 33 ]. While no research to date has explored non-adherence rates in people with psychosis who are LGBTQA+, concerns around suicidality are heightened for individuals who are gender and sexuality diverse [ 34 , 35 , 36 ].

Generally, there is rising demand for mental healthcare that specifically addresses the needs of gender and sexual minority individuals and promotes respect for diversity, equity, and inclusion [ 29 , 37 ]. This is particularly salient as positive relationships with staff are associated with better medication adherence for people with psychosis [ 38 ] and healthcare providers with LGBTQA+-specific mandates have demonstrated higher satisfaction rates for LGBTQA+ individuals [ 20 ]. Mental health services need to adapt treatment options to acknowledge minority stress factors for those with stigmatised identities and, perhaps more importantly, how these intersect and interact to increase inequalities in people from minoritized groups accessing and benefiting from treatment [ 37 , 39 ].

Additionally, gender affirming care needs to be recognised as an important facet of mental health treatment for many trans individuals, as it is associated with positive outcomes such as improvements in quality of life and psychological functioning [ 40 , 41 , 42 ] and reductions in psychiatric symptom severity and need for subsequent mental health treatment [ 8 , 43 ]. While there are additional barriers in access to gender affirming care for individuals with psychosis, this treatment has shown success in parallel with treatment to address psychosis symptom stabilisation [ 19 , 44 ]. The importance of affirmation is echoed by the finding that many negative experiences of LGBTQA+ participants with mental health services could be avoided simply by respecting people’s pronouns and using gender-neutral language [ 20 ].

To ensure timely access to appropriate treatment for LGBTQA+ people with psychosis, there is a need for improved understanding of the factors which challenge and facilitate help-seeking and engagement with mental health support. A preliminary search of Google Scholar, Medline, the Cochrane Database of Systematic Reviews, and PROSPERO was conducted and revealed no existing or planned reviews exploring benefits and/or obstacles to mental health treatment specific to this population. Therefore, the proposed review seeks to comprehensively search and appraise the existing literature to identify and summarise a range of barriers and facilitators to adequate mental health support faced by LGBTQA+ people with experiences of psychosis. This will allow for the mapping of the types of evidence available and identification of any knowledge gaps. Moreover, we hope to guide future decision-making in mental healthcare to improve service accessibility for LGBTQA+ individuals with psychosis and to set the foundations for future research that centres this marginalised population. Based on published guidance [ 45 , 46 , 47 ], a scoping review methodology was identified as the most appropriate approach to address these aims.

Selection criteria

This scoping review protocol has been developed in compliance with the JBI Manual for Evidence Synthesis [ 48 ] and, where relevant, the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) checklist [ 49 ] (see Additional file 1). In the event of protocol amendments, the date, justification, and description for each amendment will be provided.

Due to the limited literature around the topic of this review, any primary original study design, setting, and publication date will be considered for inclusion. Publications written in English will be included, and articles in other languages may be considered pending time and cost constraints around translation. Publications will be excluded if the full text is not available upon request from authors.

The PCC (Population, Concept, Context) framework was used to develop the inclusion criteria for this scoping review:

This review will include individuals of any age who are LGBTQA+ and have had experiences of psychosis. For the purposes of this review, ‘LGBTQA+ individuals’ will be broadly defined as any individual that is not heterosexual and/or cisgender or anyone who engages in same-gender sexual behaviour. Studies may include participants who are cisgender and heterosexual if they separately report outcomes for LGBTQA+ individuals. Within this review, the term ‘psychosis’ includes (i) any diagnosis of a psychotic disorder, such as schizophrenia spectrum disorders, mood disorders with psychotic features, delusional disorders, and drug-induced psychotic disorders, (ii) sub-threshold psychotic symptoms, such as those present in ultra-high risk (UHR), clinical high risk (CHR), or at risk mental state (ARMS) individuals, and (iii) any psychotic-like symptoms or experiences. Studies may include participants with multiple diagnoses if they separately report outcomes for individuals on the psychosis spectrum.

This review will include publications which discuss potential barriers and/or facilitators to mental health help-seeking and/or engagement with mental health treatment. ‘Barriers’ will be operationalised as any factors which may delay or prevent individuals from accessing and engaging with appropriate mental health support. These may include lack of mental health education, experienced or internalised stigma, experiences of discrimination from health services, and lack of inclusivity in health services. ‘Facilitators’ will be operationalised as any factors which may promote timely help-seeking and engagement with sources of support. These may include improved access to mental health education, positive sources of social support, and welcoming and inclusive services. Mental health help-seeking will be broadly defined as any attempt to seek and access formal or informal support to address a mental health concern related to experiences of psychosis (e.g. making an initial appointment with a service provider, seeking help from a friend). Mental health treatment engagement will be broadly defined as adherence and active participation in the treatment that is offered by a source of support (e.g. attending scheduled appointments, taking medication as prescribed, openly communicating with service providers).

This review may include research encompassing any setting in which mental healthcare is provided. This is likely to include formal healthcare settings such as community mental health teams or inpatient clinics as well as informal settings such as LGBTQA+ spaces or informal peer support. Studies will be excluded if they focus exclusively on physical health treatment.

Search strategy

Database searches will be conducted in Medline, PsycINFO, Embase, Scopus, and LGBTQ+ Source. The full search strategy for this protocol is available (see Additional file 2). This strategy has been collaboratively developed and evaluated by a scholarly services health librarian. Searches will include subject headings relevant to each database and title/abstract keywords relating to three main concepts: (i) LGBTQA+ identity, (ii) experiences of psychosis, and (iii) mental health treatment. Keywords for each concept will be combined using the Boolean operator ‘OR’, and the three concepts will be combined using ‘AND’. This search strategy was appropriately translated for each of the selected databases. There will be no limitations on language or publication date at this stage to maximise the breadth of the literature captured. Publications returned from these searches will be exported to EndNote. Searches will be re-run prior to the final analysis to capture any newly published studies.

The database searches will be supplemented by searching the grey literature as per the eligibility criteria detailed above. These may include theses and dissertations, conference proceedings, reports from mental health services, and policy documents from LGBTQA+ groups. Google and Google Scholar will be searched using a combination of clauses for psychosis (Psychosis OR psychotic OR schizophrenia OR schizoaffective), treatment (treatment or “help-seeking”), and queer identity. The latter concept will have three clauses for three separate searches, with one including broad queer identity (LGBT), one specific to non-heterosexual individuals (gay OR lesbian OR homosexual OR bisexual OR queer OR asexual), and one specific to trans individuals (transgender OR transsexual OR transexual OR “non-binary” OR “gender minority”). Additionally, reference lists and citing literature will be manually searched for each paper included in the review to capture any articles and policy documents not previously identified.

Data selection

Search results will be imported into Covidence using EndNote, and duplicates will be eliminated. Titles and abstracts will be screened by the first and second authors according to pre-defined screening criteria, which will be discussed by the authors and piloted prior to screening. These criteria will consider whether the articles included LGBTQA+ participants with experiences of psychosis (as operationalised above) in relation to mental health help-seeking and/or treatment. Full texts of relevant articles will then be obtained and screened by the first and second reviewer in accordance with the full inclusion and exclusion criteria after initial piloting to maximise inter-rater reliability. Decisions on inclusion and exclusion will be blinded and recorded on Covidence. Potential discrepancies will be resolved through discussion, and when consensus cannot be reached, these will be resolved by the supervising author. The process of study selection will be documented using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram [ 50 ].

Data extraction

Data extraction will be performed independently by two reviewers using Covidence. Prior to beginning final extraction, both reviewers will independently pilot the extraction tool using a sample of five included studies and discuss any necessary changes. Information extracted is planned to include the following: title, author name(s), year of publication, country in which the study was conducted, study design, sample size, population of focus (i.e. sexual minorities, gender minorities, or both), sample demographics (i.e. age, gender identity, and sexual orientation), setting (e.g. early intervention service, community mental health team, etc.), psychosis characteristics (e.g. diagnoses included, severity of symptoms, etc.), type of treatment (e.g. cognitive behavioural therapy, antipsychotic medication, etc.), and any barriers and/or facilitators identified according to the aforementioned operationalised definitions. Disagreements will be resolved through discussion between the two reviewers and, when necessary, final decisions will be made by a senior supervisor. Once extracted, information will be recorded in Excel. Lead authors of papers will be contacted by the primary review author in cases where there is missing or insufficient data.

Quality assessment

Due to the expected heterogeneity in the types of studies that may be included in this review (e.g. qualitative studies, randomised controlled trials, case control studies, case reports), the relevant revised Joanna Briggs Institute (JBI) critical appraisal checklists [ 51 ] will be used to assess risk of bias and study quality for each study design. Two reviewers will independently use these checklists to assess each paper that is included following the full-text screening. If there are discrepancies in article ratings, these will be resolved through discussion between the two authors. If no consensus is reached, discrepancies will be resolved by a senior supervisor. In line with the scoping nature of this review, low-quality studies will not be excluded from the synthesis.

Evidence synthesis

Data from included studies will be synthesised using a narrative synthesis approach in accordance with the Guidance on the Conduct of Narrative Synthesis in Systematic Reviews [ 52 ]. A preliminary descriptive synthesis will be conducted by tabulating the extracted data elements from each study alongside quality assessment results and developing an initial description of the barriers and facilitators to (1) accessing and (2) engaging with mental health support that are identified in the literature. This initial synthesis will then be interrogated and refined to contextualise these barriers and facilitators in the setting, population, and methodology of each study to form the basis for an interpretative synthesis.

This review will not use a pre-existing thematic framework to categorise barriers and facilitators as it is expected that the factors identified will not neatly fit into existing criteria. Instead, these will be conceptualised according to overarching themes as interrelated factors, so that potentially complex interactions between barriers and facilitators within and across relevant studies may be explored through concept mapping. If most of the studies included are qualitative, there may also be scope for a partial meta-synthesis. To avoid oversimplifying the concept of ‘barriers and facilitators’ (see criticism by Bach-Mortensen & Verboom [ 53 ]), this data synthesis will be followed by a critical reflection of the findings through the lens of the socio-political contexts which may give rise to the barriers and facilitators identified, exploring the complexities necessary for any changes to be implemented in mental health services.

If the extracted data indicate that gender minority and sexual minority individuals experience unique or different barriers and/or facilitators to each other, these population groups will be analysed separately as opposed to findings being generalised across the LGBTQA+ spectrum. Furthermore, if there is scope to do so, analyses may be conducted to investigate how perceived barriers and facilitators for this population may have changed over time (i.e. according to publication date) as definitions of psychosis evolve and LGBTQA+ individuals gain visibility in clinical services.

The proposed review will add to the literature around mental health treatment for LGBTQA+ people with psychosis. It will provide a thorough account of the barriers and facilitators to accessing and engaging with support faced by this population and may inform future research and clinical practice.

In terms of limitations, this review will be constrained by the existing literature and may therefore not be sufficiently comprehensive in reflecting the barriers and facilitators experienced by subgroups within the broader LGBTQA+ community. Additionally, although broad inclusion criteria are necessary to capture the full breadth of research conducted in this topic, included studies are likely to be heterogeneous and varied in terms of their methodology and population which may complicate data synthesis.

Nonetheless, it is anticipated that the findings from this review will provide the most comprehensive synthesis to date of the issues driving low help-seeking and treatment engagement in people across the psychosis spectrum who are LGBTQA+. This review will likely also identify gaps in the literature which may inform avenues for future research, and the factors identified in this review will be considered in subsequent research by the authors.

Additionally, findings will be relevant to healthcare providers that offer support to people with psychosis who may have intersecting LGBTQA+ identities as well as LGBTQA+ organisations which offer support to LGBTQA+ people who may be experiencing distressing psychotic experiences. These services are likely to benefit from an increased awareness of the factors which may improve or hinder accessibility for these subsets of their target populations. Therefore, results from this review may inform decision-making around the implementation of service-wide policy changes.

The findings of this review will be disseminated through the publication of an article in a peer-reviewed journal and presented at relevant conferences in Australia and/or internationally. Additionally, the completed review will form part of the lead author’s doctoral thesis.

Availability of data and materials

Not applicable for this protocol.

Abbreviations

• At risk mental state

Clinical high risk for psychosis

Joanna Briggs Institute

Lesbian, gay, and bisexual

Lesbian, gay, bisexual, transgender, queer or questioning, asexual or aromantic, and more

Population, Concept, Context

Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols

Ultra-high risk for psychosis

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Acknowledgements

The authors would like to acknowledge the support of Ms Olivia Larobina, Scholarly Services Librarian (STEMM) at Deakin University, in the development of the search strategy.

CCG is funded by a Deakin University Postgraduate Research (DUPR) Scholarship. ZW is funded by a University of Western Australia Research Training Program (RTP) Scholarship. AL is supported by a National Health and Medical Research Council (NHMRC) Emerging Leaders Fellowship (2010063). LJW is supported by a NHMRC Emerging Leaders Fellowship (1174060). ARY is supported by a NHMRC Principal Research Fellowship (1136829). The funding providers had no role in the design and conduct of the study, or in the preparation, review, or approval of this manuscript.

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Additional file 1. prisma-p 2015 checklist. completed prisma-p checklist for this systematic review protocol., 13643_2024_2566_moesm2_esm.docx.

Additional file 2. Search Strategy. Detailed search strategy for this systematic review, including search terms and relevant controlled vocabulary terms for each included database.

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Gonçalves, C.C., Waters, Z., Quirk, S.E. et al. Barriers and facilitators to mental health treatment access and engagement for LGBTQA+ people with psychosis: a scoping review protocol. Syst Rev 13 , 143 (2024). https://doi.org/10.1186/s13643-024-02566-5

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Reviews in Gastroenterology 2023

Electrogastrography Measurement Systems and Analysis Methods Used in Clinical Practice and Research: Comprehensive Review Provisionally Accepted

• 1 VSB-Technical University of Ostrava, Czechia

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Electrogastrography (EGG) is a non-invasive method with high diagnostic potential for the prevention of gastroenterological pathologies in clinical practice. In this paper, a review of the measurement systems, procedures, and methods of analysis used in electrogastrography is presented. A critical review of historical and current literature is conducted, focusing on electrode placement, measurement apparatus, measurement procedures, and time-frequency domain methods of filtration and analysis of the non-invasively measured electrical activity of the stomach.As a result a total of 129 relevant articles with primary aim on experimental diet were reviewed in this study. Scopus, PubMed and Web of Science databases were used to search for articles in English language, according to the specific query and using PRISMA method. The research topic of electrogastrography has been continuously growing in popularity since the first measurement by professor Alvarez 100 years ago and there are many researchers and companies interested in EGG nowadays. Measurement apparatus and procedures are still being developed in both commercial and research settings. There are plenty variable electrode layouts, ranging from minimal numbers of electrodes for ambulatory measurements to very high numbers of electrodes for spatial measurements. Most authors used in their research anatomically approximated layout with 2 active electrodes in bipolar connection and commercial electrogastrograph with sampling rate of 2 or 4 Hz. Test subjects were usually healthy adults and diet was controlled. However, evaluation methods are being developed at a slower pace and usually the signals are classified only based on dominant frequency. The main review contributions include the overview of spectrum of measurement systems and procedures for electrogastrography developed by many authors, but a firm medical standard has not yet been defined. Therefore, it is not possible to use this method in clinical practice for objective diagnosis.

Keywords: electrogastrography, non-invasive method, Measurement systems, Electrode placement, Measurement apparatus, Signal processing

Received: 19 Jan 2024; Accepted: 03 Jun 2024.

Copyright: © 2024 Oczka, Augustynek, Penhaker and Kubicek. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Jan Kubicek, VSB-Technical University of Ostrava, Ostrava, 708 33, Moravian-Silesian Region, Czechia

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Lau F, Kuziemsky C, editors. Handbook of eHealth Evaluation: An Evidence-based Approach [Internet]. Victoria (BC): University of Victoria; 2017 Feb 27.

Handbook of eHealth Evaluation: An Evidence-based Approach [Internet].

Chapter 9 methods for literature reviews.

Guy Paré and Spyros Kitsiou .

9.1. Introduction

Literature reviews play a critical role in scholarship because science remains, first and foremost, a cumulative endeavour ( vom Brocke et al., 2009 ). As in any academic discipline, rigorous knowledge syntheses are becoming indispensable in keeping up with an exponentially growing eHealth literature, assisting practitioners, academics, and graduate students in finding, evaluating, and synthesizing the contents of many empirical and conceptual papers. Among other methods, literature reviews are essential for: (a) identifying what has been written on a subject or topic; (b) determining the extent to which a specific research area reveals any interpretable trends or patterns; (c) aggregating empirical findings related to a narrow research question to support evidence-based practice; (d) generating new frameworks and theories; and (e) identifying topics or questions requiring more investigation ( Paré, Trudel, Jaana, & Kitsiou, 2015 ).

Literature reviews can take two major forms. The most prevalent one is the “literature review” or “background” section within a journal paper or a chapter in a graduate thesis. This section synthesizes the extant literature and usually identifies the gaps in knowledge that the empirical study addresses ( Sylvester, Tate, & Johnstone, 2013 ). It may also provide a theoretical foundation for the proposed study, substantiate the presence of the research problem, justify the research as one that contributes something new to the cumulated knowledge, or validate the methods and approaches for the proposed study ( Hart, 1998 ; Levy & Ellis, 2006 ).

The second form of literature review, which is the focus of this chapter, constitutes an original and valuable work of research in and of itself ( Paré et al., 2015 ). Rather than providing a base for a researcher’s own work, it creates a solid starting point for all members of the community interested in a particular area or topic ( Mulrow, 1987 ). The so-called “review article” is a journal-length paper which has an overarching purpose to synthesize the literature in a field, without collecting or analyzing any primary data ( Green, Johnson, & Adams, 2006 ).

When appropriately conducted, review articles represent powerful information sources for practitioners looking for state-of-the art evidence to guide their decision-making and work practices ( Paré et al., 2015 ). Further, high-quality reviews become frequently cited pieces of work which researchers seek out as a first clear outline of the literature when undertaking empirical studies ( Cooper, 1988 ; Rowe, 2014 ). Scholars who track and gauge the impact of articles have found that review papers are cited and downloaded more often than any other type of published article ( Cronin, Ryan, & Coughlan, 2008 ; Montori, Wilczynski, Morgan, Haynes, & Hedges, 2003 ; Patsopoulos, Analatos, & Ioannidis, 2005 ). The reason for their popularity may be the fact that reading the review enables one to have an overview, if not a detailed knowledge of the area in question, as well as references to the most useful primary sources ( Cronin et al., 2008 ). Although they are not easy to conduct, the commitment to complete a review article provides a tremendous service to one’s academic community ( Paré et al., 2015 ; Petticrew & Roberts, 2006 ). Most, if not all, peer-reviewed journals in the fields of medical informatics publish review articles of some type.

The main objectives of this chapter are fourfold: (a) to provide an overview of the major steps and activities involved in conducting a stand-alone literature review; (b) to describe and contrast the different types of review articles that can contribute to the eHealth knowledge base; (c) to illustrate each review type with one or two examples from the eHealth literature; and (d) to provide a series of recommendations for prospective authors of review articles in this domain.

9.2. Overview of the Literature Review Process and Steps

As explained in Templier and Paré (2015) , there are six generic steps involved in conducting a review article:

• formulating the research question(s) and objective(s),
• searching the extant literature,
• screening for inclusion,
• assessing the quality of primary studies,
• extracting data, and
• analyzing data.

Although these steps are presented here in sequential order, one must keep in mind that the review process can be iterative and that many activities can be initiated during the planning stage and later refined during subsequent phases ( Finfgeld-Connett & Johnson, 2013 ; Kitchenham & Charters, 2007 ).

Formulating the research question(s) and objective(s): As a first step, members of the review team must appropriately justify the need for the review itself ( Petticrew & Roberts, 2006 ), identify the review’s main objective(s) ( Okoli & Schabram, 2010 ), and define the concepts or variables at the heart of their synthesis ( Cooper & Hedges, 2009 ; Webster & Watson, 2002 ). Importantly, they also need to articulate the research question(s) they propose to investigate ( Kitchenham & Charters, 2007 ). In this regard, we concur with Jesson, Matheson, and Lacey (2011) that clearly articulated research questions are key ingredients that guide the entire review methodology; they underscore the type of information that is needed, inform the search for and selection of relevant literature, and guide or orient the subsequent analysis. Searching the extant literature: The next step consists of searching the literature and making decisions about the suitability of material to be considered in the review ( Cooper, 1988 ). There exist three main coverage strategies. First, exhaustive coverage means an effort is made to be as comprehensive as possible in order to ensure that all relevant studies, published and unpublished, are included in the review and, thus, conclusions are based on this all-inclusive knowledge base. The second type of coverage consists of presenting materials that are representative of most other works in a given field or area. Often authors who adopt this strategy will search for relevant articles in a small number of top-tier journals in a field ( Paré et al., 2015 ). In the third strategy, the review team concentrates on prior works that have been central or pivotal to a particular topic. This may include empirical studies or conceptual papers that initiated a line of investigation, changed how problems or questions were framed, introduced new methods or concepts, or engendered important debate ( Cooper, 1988 ). Screening for inclusion: The following step consists of evaluating the applicability of the material identified in the preceding step ( Levy & Ellis, 2006 ; vom Brocke et al., 2009 ). Once a group of potential studies has been identified, members of the review team must screen them to determine their relevance ( Petticrew & Roberts, 2006 ). A set of predetermined rules provides a basis for including or excluding certain studies. This exercise requires a significant investment on the part of researchers, who must ensure enhanced objectivity and avoid biases or mistakes. As discussed later in this chapter, for certain types of reviews there must be at least two independent reviewers involved in the screening process and a procedure to resolve disagreements must also be in place ( Liberati et al., 2009 ; Shea et al., 2009 ). Assessing the quality of primary studies: In addition to screening material for inclusion, members of the review team may need to assess the scientific quality of the selected studies, that is, appraise the rigour of the research design and methods. Such formal assessment, which is usually conducted independently by at least two coders, helps members of the review team refine which studies to include in the final sample, determine whether or not the differences in quality may affect their conclusions, or guide how they analyze the data and interpret the findings ( Petticrew & Roberts, 2006 ). Ascribing quality scores to each primary study or considering through domain-based evaluations which study components have or have not been designed and executed appropriately makes it possible to reflect on the extent to which the selected study addresses possible biases and maximizes validity ( Shea et al., 2009 ). Extracting data: The following step involves gathering or extracting applicable information from each primary study included in the sample and deciding what is relevant to the problem of interest ( Cooper & Hedges, 2009 ). Indeed, the type of data that should be recorded mainly depends on the initial research questions ( Okoli & Schabram, 2010 ). However, important information may also be gathered about how, when, where and by whom the primary study was conducted, the research design and methods, or qualitative/quantitative results ( Cooper & Hedges, 2009 ). Analyzing and synthesizing data : As a final step, members of the review team must collate, summarize, aggregate, organize, and compare the evidence extracted from the included studies. The extracted data must be presented in a meaningful way that suggests a new contribution to the extant literature ( Jesson et al., 2011 ). Webster and Watson (2002) warn researchers that literature reviews should be much more than lists of papers and should provide a coherent lens to make sense of extant knowledge on a given topic. There exist several methods and techniques for synthesizing quantitative (e.g., frequency analysis, meta-analysis) and qualitative (e.g., grounded theory, narrative analysis, meta-ethnography) evidence ( Dixon-Woods, Agarwal, Jones, Young, & Sutton, 2005 ; Thomas & Harden, 2008 ).

9.3. Types of Review Articles and Brief Illustrations

EHealth researchers have at their disposal a number of approaches and methods for making sense out of existing literature, all with the purpose of casting current research findings into historical contexts or explaining contradictions that might exist among a set of primary research studies conducted on a particular topic. Our classification scheme is largely inspired from Paré and colleagues’ (2015) typology. Below we present and illustrate those review types that we feel are central to the growth and development of the eHealth domain.

9.3.1. Narrative Reviews

The narrative review is the “traditional” way of reviewing the extant literature and is skewed towards a qualitative interpretation of prior knowledge ( Sylvester et al., 2013 ). Put simply, a narrative review attempts to summarize or synthesize what has been written on a particular topic but does not seek generalization or cumulative knowledge from what is reviewed ( Davies, 2000 ; Green et al., 2006 ). Instead, the review team often undertakes the task of accumulating and synthesizing the literature to demonstrate the value of a particular point of view ( Baumeister & Leary, 1997 ). As such, reviewers may selectively ignore or limit the attention paid to certain studies in order to make a point. In this rather unsystematic approach, the selection of information from primary articles is subjective, lacks explicit criteria for inclusion and can lead to biased interpretations or inferences ( Green et al., 2006 ). There are several narrative reviews in the particular eHealth domain, as in all fields, which follow such an unstructured approach ( Silva et al., 2015 ; Paul et al., 2015 ).

Despite these criticisms, this type of review can be very useful in gathering together a volume of literature in a specific subject area and synthesizing it. As mentioned above, its primary purpose is to provide the reader with a comprehensive background for understanding current knowledge and highlighting the significance of new research ( Cronin et al., 2008 ). Faculty like to use narrative reviews in the classroom because they are often more up to date than textbooks, provide a single source for students to reference, and expose students to peer-reviewed literature ( Green et al., 2006 ). For researchers, narrative reviews can inspire research ideas by identifying gaps or inconsistencies in a body of knowledge, thus helping researchers to determine research questions or formulate hypotheses. Importantly, narrative reviews can also be used as educational articles to bring practitioners up to date with certain topics of issues ( Green et al., 2006 ).

Recently, there have been several efforts to introduce more rigour in narrative reviews that will elucidate common pitfalls and bring changes into their publication standards. Information systems researchers, among others, have contributed to advancing knowledge on how to structure a “traditional” review. For instance, Levy and Ellis (2006) proposed a generic framework for conducting such reviews. Their model follows the systematic data processing approach comprised of three steps, namely: (a) literature search and screening; (b) data extraction and analysis; and (c) writing the literature review. They provide detailed and very helpful instructions on how to conduct each step of the review process. As another methodological contribution, vom Brocke et al. (2009) offered a series of guidelines for conducting literature reviews, with a particular focus on how to search and extract the relevant body of knowledge. Last, Bandara, Miskon, and Fielt (2011) proposed a structured, predefined and tool-supported method to identify primary studies within a feasible scope, extract relevant content from identified articles, synthesize and analyze the findings, and effectively write and present the results of the literature review. We highly recommend that prospective authors of narrative reviews consult these useful sources before embarking on their work.

Darlow and Wen (2015) provide a good example of a highly structured narrative review in the eHealth field. These authors synthesized published articles that describe the development process of mobile health ( m-health ) interventions for patients’ cancer care self-management. As in most narrative reviews, the scope of the research questions being investigated is broad: (a) how development of these systems are carried out; (b) which methods are used to investigate these systems; and (c) what conclusions can be drawn as a result of the development of these systems. To provide clear answers to these questions, a literature search was conducted on six electronic databases and Google Scholar . The search was performed using several terms and free text words, combining them in an appropriate manner. Four inclusion and three exclusion criteria were utilized during the screening process. Both authors independently reviewed each of the identified articles to determine eligibility and extract study information. A flow diagram shows the number of studies identified, screened, and included or excluded at each stage of study selection. In terms of contributions, this review provides a series of practical recommendations for m-health intervention development.

9.3.2. Descriptive or Mapping Reviews

The primary goal of a descriptive review is to determine the extent to which a body of knowledge in a particular research topic reveals any interpretable pattern or trend with respect to pre-existing propositions, theories, methodologies or findings ( King & He, 2005 ; Paré et al., 2015 ). In contrast with narrative reviews, descriptive reviews follow a systematic and transparent procedure, including searching, screening and classifying studies ( Petersen, Vakkalanka, & Kuzniarz, 2015 ). Indeed, structured search methods are used to form a representative sample of a larger group of published works ( Paré et al., 2015 ). Further, authors of descriptive reviews extract from each study certain characteristics of interest, such as publication year, research methods, data collection techniques, and direction or strength of research outcomes (e.g., positive, negative, or non-significant) in the form of frequency analysis to produce quantitative results ( Sylvester et al., 2013 ). In essence, each study included in a descriptive review is treated as the unit of analysis and the published literature as a whole provides a database from which the authors attempt to identify any interpretable trends or draw overall conclusions about the merits of existing conceptualizations, propositions, methods or findings ( Paré et al., 2015 ). In doing so, a descriptive review may claim that its findings represent the state of the art in a particular domain ( King & He, 2005 ).

In the fields of health sciences and medical informatics, reviews that focus on examining the range, nature and evolution of a topic area are described by Anderson, Allen, Peckham, and Goodwin (2008) as mapping reviews . Like descriptive reviews, the research questions are generic and usually relate to publication patterns and trends. There is no preconceived plan to systematically review all of the literature although this can be done. Instead, researchers often present studies that are representative of most works published in a particular area and they consider a specific time frame to be mapped.

An example of this approach in the eHealth domain is offered by DeShazo, Lavallie, and Wolf (2009). The purpose of this descriptive or mapping review was to characterize publication trends in the medical informatics literature over a 20-year period (1987 to 2006). To achieve this ambitious objective, the authors performed a bibliometric analysis of medical informatics citations indexed in medline using publication trends, journal frequencies, impact factors, Medical Subject Headings (MeSH) term frequencies, and characteristics of citations. Findings revealed that there were over 77,000 medical informatics articles published during the covered period in numerous journals and that the average annual growth rate was 12%. The MeSH term analysis also suggested a strong interdisciplinary trend. Finally, average impact scores increased over time with two notable growth periods. Overall, patterns in research outputs that seem to characterize the historic trends and current components of the field of medical informatics suggest it may be a maturing discipline (DeShazo et al., 2009).

9.3.3. Scoping Reviews

Scoping reviews attempt to provide an initial indication of the potential size and nature of the extant literature on an emergent topic (Arksey & O’Malley, 2005; Daudt, van Mossel, & Scott, 2013 ; Levac, Colquhoun, & O’Brien, 2010). A scoping review may be conducted to examine the extent, range and nature of research activities in a particular area, determine the value of undertaking a full systematic review (discussed next), or identify research gaps in the extant literature ( Paré et al., 2015 ). In line with their main objective, scoping reviews usually conclude with the presentation of a detailed research agenda for future works along with potential implications for both practice and research.

Unlike narrative and descriptive reviews, the whole point of scoping the field is to be as comprehensive as possible, including grey literature (Arksey & O’Malley, 2005). Inclusion and exclusion criteria must be established to help researchers eliminate studies that are not aligned with the research questions. It is also recommended that at least two independent coders review abstracts yielded from the search strategy and then the full articles for study selection ( Daudt et al., 2013 ). The synthesized evidence from content or thematic analysis is relatively easy to present in tabular form (Arksey & O’Malley, 2005; Thomas & Harden, 2008 ).

One of the most highly cited scoping reviews in the eHealth domain was published by Archer, Fevrier-Thomas, Lokker, McKibbon, and Straus (2011) . These authors reviewed the existing literature on personal health record ( phr ) systems including design, functionality, implementation, applications, outcomes, and benefits. Seven databases were searched from 1985 to March 2010. Several search terms relating to phr s were used during this process. Two authors independently screened titles and abstracts to determine inclusion status. A second screen of full-text articles, again by two independent members of the research team, ensured that the studies described phr s. All in all, 130 articles met the criteria and their data were extracted manually into a database. The authors concluded that although there is a large amount of survey, observational, cohort/panel, and anecdotal evidence of phr benefits and satisfaction for patients, more research is needed to evaluate the results of phr implementations. Their in-depth analysis of the literature signalled that there is little solid evidence from randomized controlled trials or other studies through the use of phr s. Hence, they suggested that more research is needed that addresses the current lack of understanding of optimal functionality and usability of these systems, and how they can play a beneficial role in supporting patient self-management ( Archer et al., 2011 ).

9.3.4. Forms of Aggregative Reviews

Healthcare providers, practitioners, and policy-makers are nowadays overwhelmed with large volumes of information, including research-based evidence from numerous clinical trials and evaluation studies, assessing the effectiveness of health information technologies and interventions ( Ammenwerth & de Keizer, 2004 ; Deshazo et al., 2009 ). It is unrealistic to expect that all these disparate actors will have the time, skills, and necessary resources to identify the available evidence in the area of their expertise and consider it when making decisions. Systematic reviews that involve the rigorous application of scientific strategies aimed at limiting subjectivity and bias (i.e., systematic and random errors) can respond to this challenge.

Systematic reviews attempt to aggregate, appraise, and synthesize in a single source all empirical evidence that meet a set of previously specified eligibility criteria in order to answer a clearly formulated and often narrow research question on a particular topic of interest to support evidence-based practice ( Liberati et al., 2009 ). They adhere closely to explicit scientific principles ( Liberati et al., 2009 ) and rigorous methodological guidelines (Higgins & Green, 2008) aimed at reducing random and systematic errors that can lead to deviations from the truth in results or inferences. The use of explicit methods allows systematic reviews to aggregate a large body of research evidence, assess whether effects or relationships are in the same direction and of the same general magnitude, explain possible inconsistencies between study results, and determine the strength of the overall evidence for every outcome of interest based on the quality of included studies and the general consistency among them ( Cook, Mulrow, & Haynes, 1997 ). The main procedures of a systematic review involve:

• Formulating a review question and developing a search strategy based on explicit inclusion criteria for the identification of eligible studies (usually described in the context of a detailed review protocol).
• Searching for eligible studies using multiple databases and information sources, including grey literature sources, without any language restrictions.
• Selecting studies, extracting data, and assessing risk of bias in a duplicate manner using two independent reviewers to avoid random or systematic errors in the process.
• Analyzing data using quantitative or qualitative methods.
• Presenting results in summary of findings tables.
• Interpreting results and drawing conclusions.

Many systematic reviews, but not all, use statistical methods to combine the results of independent studies into a single quantitative estimate or summary effect size. Known as meta-analyses , these reviews use specific data extraction and statistical techniques (e.g., network, frequentist, or Bayesian meta-analyses) to calculate from each study by outcome of interest an effect size along with a confidence interval that reflects the degree of uncertainty behind the point estimate of effect ( Borenstein, Hedges, Higgins, & Rothstein, 2009 ; Deeks, Higgins, & Altman, 2008 ). Subsequently, they use fixed or random-effects analysis models to combine the results of the included studies, assess statistical heterogeneity, and calculate a weighted average of the effect estimates from the different studies, taking into account their sample sizes. The summary effect size is a value that reflects the average magnitude of the intervention effect for a particular outcome of interest or, more generally, the strength of a relationship between two variables across all studies included in the systematic review. By statistically combining data from multiple studies, meta-analyses can create more precise and reliable estimates of intervention effects than those derived from individual studies alone, when these are examined independently as discrete sources of information.

The review by Gurol-Urganci, de Jongh, Vodopivec-Jamsek, Atun, and Car (2013) on the effects of mobile phone messaging reminders for attendance at healthcare appointments is an illustrative example of a high-quality systematic review with meta-analysis. Missed appointments are a major cause of inefficiency in healthcare delivery with substantial monetary costs to health systems. These authors sought to assess whether mobile phone-based appointment reminders delivered through Short Message Service ( sms ) or Multimedia Messaging Service ( mms ) are effective in improving rates of patient attendance and reducing overall costs. To this end, they conducted a comprehensive search on multiple databases using highly sensitive search strategies without language or publication-type restrictions to identify all rct s that are eligible for inclusion. In order to minimize the risk of omitting eligible studies not captured by the original search, they supplemented all electronic searches with manual screening of trial registers and references contained in the included studies. Study selection, data extraction, and risk of bias assessments were performed inde­­pen­dently by two coders using standardized methods to ensure consistency and to eliminate potential errors. Findings from eight rct s involving 6,615 participants were pooled into meta-analyses to calculate the magnitude of effects that mobile text message reminders have on the rate of attendance at healthcare appointments compared to no reminders and phone call reminders.

Meta-analyses are regarded as powerful tools for deriving meaningful conclusions. However, there are situations in which it is neither reasonable nor appropriate to pool studies together using meta-analytic methods simply because there is extensive clinical heterogeneity between the included studies or variation in measurement tools, comparisons, or outcomes of interest. In these cases, systematic reviews can use qualitative synthesis methods such as vote counting, content analysis, classification schemes and tabulations, as an alternative approach to narratively synthesize the results of the independent studies included in the review. This form of review is known as qualitative systematic review.

A rigorous example of one such review in the eHealth domain is presented by Mickan, Atherton, Roberts, Heneghan, and Tilson (2014) on the use of handheld computers by healthcare professionals and their impact on access to information and clinical decision-making. In line with the methodological guide­lines for systematic reviews, these authors: (a) developed and registered with prospero ( www.crd.york.ac.uk/ prospero / ) an a priori review protocol; (b) conducted comprehensive searches for eligible studies using multiple databases and other supplementary strategies (e.g., forward searches); and (c) subsequently carried out study selection, data extraction, and risk of bias assessments in a duplicate manner to eliminate potential errors in the review process. Heterogeneity between the included studies in terms of reported outcomes and measures precluded the use of meta-analytic methods. To this end, the authors resorted to using narrative analysis and synthesis to describe the effectiveness of handheld computers on accessing information for clinical knowledge, adherence to safety and clinical quality guidelines, and diagnostic decision-making.

In recent years, the number of systematic reviews in the field of health informatics has increased considerably. Systematic reviews with discordant findings can cause great confusion and make it difficult for decision-makers to interpret the review-level evidence ( Moher, 2013 ). Therefore, there is a growing need for appraisal and synthesis of prior systematic reviews to ensure that decision-making is constantly informed by the best available accumulated evidence. Umbrella reviews , also known as overviews of systematic reviews, are tertiary types of evidence synthesis that aim to accomplish this; that is, they aim to compare and contrast findings from multiple systematic reviews and meta-analyses ( Becker & Oxman, 2008 ). Umbrella reviews generally adhere to the same principles and rigorous methodological guidelines used in systematic reviews. However, the unit of analysis in umbrella reviews is the systematic review rather than the primary study ( Becker & Oxman, 2008 ). Unlike systematic reviews that have a narrow focus of inquiry, umbrella reviews focus on broader research topics for which there are several potential interventions ( Smith, Devane, Begley, & Clarke, 2011 ). A recent umbrella review on the effects of home telemonitoring interventions for patients with heart failure critically appraised, compared, and synthesized evidence from 15 systematic reviews to investigate which types of home telemonitoring technologies and forms of interventions are more effective in reducing mortality and hospital admissions ( Kitsiou, Paré, & Jaana, 2015 ).

9.3.5. Realist Reviews

Realist reviews are theory-driven interpretative reviews developed to inform, enhance, or supplement conventional systematic reviews by making sense of heterogeneous evidence about complex interventions applied in diverse contexts in a way that informs policy decision-making ( Greenhalgh, Wong, Westhorp, & Pawson, 2011 ). They originated from criticisms of positivist systematic reviews which centre on their “simplistic” underlying assumptions ( Oates, 2011 ). As explained above, systematic reviews seek to identify causation. Such logic is appropriate for fields like medicine and education where findings of randomized controlled trials can be aggregated to see whether a new treatment or intervention does improve outcomes. However, many argue that it is not possible to establish such direct causal links between interventions and outcomes in fields such as social policy, management, and information systems where for any intervention there is unlikely to be a regular or consistent outcome ( Oates, 2011 ; Pawson, 2006 ; Rousseau, Manning, & Denyer, 2008 ).

To circumvent these limitations, Pawson, Greenhalgh, Harvey, and Walshe (2005) have proposed a new approach for synthesizing knowledge that seeks to unpack the mechanism of how “complex interventions” work in particular contexts. The basic research question — what works? — which is usually associated with systematic reviews changes to: what is it about this intervention that works, for whom, in what circumstances, in what respects and why? Realist reviews have no particular preference for either quantitative or qualitative evidence. As a theory-building approach, a realist review usually starts by articulating likely underlying mechanisms and then scrutinizes available evidence to find out whether and where these mechanisms are applicable ( Shepperd et al., 2009 ). Primary studies found in the extant literature are viewed as case studies which can test and modify the initial theories ( Rousseau et al., 2008 ).

The main objective pursued in the realist review conducted by Otte-Trojel, de Bont, Rundall, and van de Klundert (2014) was to examine how patient portals contribute to health service delivery and patient outcomes. The specific goals were to investigate how outcomes are produced and, most importantly, how variations in outcomes can be explained. The research team started with an exploratory review of background documents and research studies to identify ways in which patient portals may contribute to health service delivery and patient outcomes. The authors identified six main ways which represent “educated guesses” to be tested against the data in the evaluation studies. These studies were identified through a formal and systematic search in four databases between 2003 and 2013. Two members of the research team selected the articles using a pre-established list of inclusion and exclusion criteria and following a two-step procedure. The authors then extracted data from the selected articles and created several tables, one for each outcome category. They organized information to bring forward those mechanisms where patient portals contribute to outcomes and the variation in outcomes across different contexts.

9.3.6. Critical Reviews

Lastly, critical reviews aim to provide a critical evaluation and interpretive analysis of existing literature on a particular topic of interest to reveal strengths, weaknesses, contradictions, controversies, inconsistencies, and/or other important issues with respect to theories, hypotheses, research methods or results ( Baumeister & Leary, 1997 ; Kirkevold, 1997 ). Unlike other review types, critical reviews attempt to take a reflective account of the research that has been done in a particular area of interest, and assess its credibility by using appraisal instruments or critical interpretive methods. In this way, critical reviews attempt to constructively inform other scholars about the weaknesses of prior research and strengthen knowledge development by giving focus and direction to studies for further improvement ( Kirkevold, 1997 ).

Kitsiou, Paré, and Jaana (2013) provide an example of a critical review that assessed the methodological quality of prior systematic reviews of home telemonitoring studies for chronic patients. The authors conducted a comprehensive search on multiple databases to identify eligible reviews and subsequently used a validated instrument to conduct an in-depth quality appraisal. Results indicate that the majority of systematic reviews in this particular area suffer from important methodological flaws and biases that impair their internal validity and limit their usefulness for clinical and decision-making purposes. To this end, they provide a number of recommendations to strengthen knowledge development towards improving the design and execution of future reviews on home telemonitoring.

9.4. Summary

Table 9.1 outlines the main types of literature reviews that were described in the previous sub-sections and summarizes the main characteristics that distinguish one review type from another. It also includes key references to methodological guidelines and useful sources that can be used by eHealth scholars and researchers for planning and developing reviews.

Typology of Literature Reviews (adapted from Paré et al., 2015).

As shown in Table 9.1 , each review type addresses different kinds of research questions or objectives, which subsequently define and dictate the methods and approaches that need to be used to achieve the overarching goal(s) of the review. For example, in the case of narrative reviews, there is greater flexibility in searching and synthesizing articles ( Green et al., 2006 ). Researchers are often relatively free to use a diversity of approaches to search, identify, and select relevant scientific articles, describe their operational characteristics, present how the individual studies fit together, and formulate conclusions. On the other hand, systematic reviews are characterized by their high level of systematicity, rigour, and use of explicit methods, based on an “a priori” review plan that aims to minimize bias in the analysis and synthesis process (Higgins & Green, 2008). Some reviews are exploratory in nature (e.g., scoping/mapping reviews), whereas others may be conducted to discover patterns (e.g., descriptive reviews) or involve a synthesis approach that may include the critical analysis of prior research ( Paré et al., 2015 ). Hence, in order to select the most appropriate type of review, it is critical to know before embarking on a review project, why the research synthesis is conducted and what type of methods are best aligned with the pursued goals.

9.5. Concluding Remarks

In light of the increased use of evidence-based practice and research generating stronger evidence ( Grady et al., 2011 ; Lyden et al., 2013 ), review articles have become essential tools for summarizing, synthesizing, integrating or critically appraising prior knowledge in the eHealth field. As mentioned earlier, when rigorously conducted review articles represent powerful information sources for eHealth scholars and practitioners looking for state-of-the-art evidence. The typology of literature reviews we used herein will allow eHealth researchers, graduate students and practitioners to gain a better understanding of the similarities and differences between review types.

We must stress that this classification scheme does not privilege any specific type of review as being of higher quality than another ( Paré et al., 2015 ). As explained above, each type of review has its own strengths and limitations. Having said that, we realize that the methodological rigour of any review — be it qualitative, quantitative or mixed — is a critical aspect that should be considered seriously by prospective authors. In the present context, the notion of rigour refers to the reliability and validity of the review process described in section 9.2. For one thing, reliability is related to the reproducibility of the review process and steps, which is facilitated by a comprehensive documentation of the literature search process, extraction, coding and analysis performed in the review. Whether the search is comprehensive or not, whether it involves a methodical approach for data extraction and synthesis or not, it is important that the review documents in an explicit and transparent manner the steps and approach that were used in the process of its development. Next, validity characterizes the degree to which the review process was conducted appropriately. It goes beyond documentation and reflects decisions related to the selection of the sources, the search terms used, the period of time covered, the articles selected in the search, and the application of backward and forward searches ( vom Brocke et al., 2009 ). In short, the rigour of any review article is reflected by the explicitness of its methods (i.e., transparency) and the soundness of the approach used. We refer those interested in the concepts of rigour and quality to the work of Templier and Paré (2015) which offers a detailed set of methodological guidelines for conducting and evaluating various types of review articles.

To conclude, our main objective in this chapter was to demystify the various types of literature reviews that are central to the continuous development of the eHealth field. It is our hope that our descriptive account will serve as a valuable source for those conducting, evaluating or using reviews in this important and growing domain.

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• Cite this Page Paré G, Kitsiou S. Chapter 9 Methods for Literature Reviews. In: Lau F, Kuziemsky C, editors. Handbook of eHealth Evaluation: An Evidence-based Approach [Internet]. Victoria (BC): University of Victoria; 2017 Feb 27.
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• Published: 04 June 2024

Methodological identification of anomalies episodes in ECG streams: a systematic mapping study

• Uzair Iqbal 1 ,
• Riyad Almakki 2 ,
• Muhammad Usman 3 ,
• Abdullah Altameem 2 ,
• Mubarak Albathan 2 &
• Abdul Khader Jilani 4  

BMC Medical Research Methodology volume  24 , Article number:  127 ( 2024 ) Cite this article

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An electrocardiogram is a medical examination tool for measuring different patterns of heart blood flow circle either in the form of usual or non-invasive patterns. These patterns are useful for the identification of morbidity condition of the heart especially in certain conditions of heart abnormality and arrhythmia . Myocardial infarction (MI) is one of them that happened due to sudden blockage of blood by the cause of malfunction of heart. In electrocardiography (ECG) intensity of MI is highlighted on the basis of unusual patterns of T wave changes. Various studies have contributed for MI through T wave’s classification, but more to the point of T wave has always attracted the ECG researchers. Methodology. This Study is primarily designed for proposing the combination of latest methods that are worked for the solutions of pre-defined research questions. Such solutions are designed in the form of the systematic review process (SLR) by following the Kitchen ham guidance. The literature survey is a two phase’s process, at first phase collect the articles that were published in IEEE Xplore, Scopus, science direct and Springer from 2008 to 2023. It consist of steps; the first level is executed by filtrating the articles on the basis of keyword phase of title and abstract filter. Similarly, at two level the manuscripts are scanned through filter of eligibility criteria of articles selection. The last level belongs to the quality assessment of articles, in such level articles are rectified through evaluation of domain experts. Results. Finally, the selected articles are addressed with research questions and briefly discuss these selected state-of-the-art methods that are worked for the T wave classification. These address units behave as solutions to research problems that are highlighted in the form of research questions. Conclusion and future directions. During the survey process for these solutions, we got some critical observations in the form of gaps that reflected the other directions for researchers. In which feature engineering, different dependencies of ECG features and dimensional reduction of ECG for the better ECG analysis are reflection of future directions.

Peer Review reports

Introduction and Background

Pattern recognition is a game changer in time series data. Differentiating between regular and irregular patterns is highly desirable in such cases that involve urgency and risk factors. In the context of these statements, data from MRI monitors the unusual behavior of the brain (brain tumor), data from CT SCAN relates to the broader picture of the human body, and data from ECG highlights the usual and non-invasive heart activities. The scope of this article deals with anomalous behaviors of the heart in terms of ECG analysis. Unusual activities of the heart are referred to as anomalous behavior that represents cardiovascular diseases (CVDs). Accurate and robust classification of these CVDs has especially attracted cardiologists and ECG researchers. Some of the CVDs are critical for diagnostic purposes like one of them is a myocardial infarction (MI). MI depends on the effects in the ST part and T wave and main focus is on changes of the T wave. The primary cause of myocardial infarction (MI) stems from anomalous T-wave episodes [ 1 , 2 ]. Automated classification of T-wave episodes is a game changer for cardiologists and ECG researchers.

Accurate classification of T wave demonstrates the concept of different types of T wave episodes like flattened T wave, inversion T wave, and negative T wave. Identification of the exact characteristics of flattened T is an open research problem for ECG researchers. In operational investigations of literature for better classification of T-wave episodes, In one case, the identification of flattened T-wave characteristics involves assessing parameters such as time duration, peak value, and start and end times. Additionally, dependencies on various features related to T-wave episodes, such as the utilization of the R peak as a reference point for calculating the T-onset parameter [ 3 ], play a significant role in classifying diverse T-wave episodes. These calculations of dependency factors are crucial for achieving accurate and robust classification of different T-wave episodes, highlighting the importance of the literature problem definition. During the literature survey, numerous studies directly and indirectly related to T-wave classification were discovered, underscoring the relevance of this research. In several studies, one of them is a Manifold algorithm that works for anomaly detection in ECG data and gets the accuracy at 96% level. Such an algorithm works in three phases. In the first phase, segmentation, and feature extraction are performed. In the second phase, the manifold structure was discovered and mapped with findings. The last phase deals with the anomaly detection and recognition [ 2 , 3 ]. Similarly, the Myocardial infraction detection algorithm MI detection algorithm surpasses the accuracy of traditional algorithms, achieving an impressive 98% accuracy rate [ 4 ]. It has been specifically designed for the detection of abnormalities in the ST segment and T-wave [ 5 , 6 , 7 , 8 , 9 , 10 , 11 ]. The MI detection algorithm is particularly focused on distinguishing between ST-segment elevation (STEMI) and non-ST segment elevation (NSTEMI) cases [ 12 , 13 , 14 , 15 , 16 ]. This study is intended to enhance the accuracy of state-of-the-art methods in classifying various T-wave episodes.

This article is designed for solutions to the defined problems through a literature survey. Such a solution belongs to the robust and accurate classification of different T wave episodes by using the hybrid approach. Concepts of behavior analytics are integrated with parametric analysis of the T wave for calculating the dependencies factor. This factor plays a critical role for another perspective in terms of classifying the T wave anomalies, such perspective reflects in terms of different neural models [ 17 , 18 , 19 ]. These three different perspectives are merged for a better and more accurate classification solution [ 20 , 21 , 22 ].For the conduction of this hybrid approach, the dependencies indicator of T wave parameters is a central theme of this approach like interconnectivity between T-onset, T-offset, T peak value, and T wave time duration [ 23 , 24 , 25 , 26 , 27 , 28 , 29 ].

By using our best knowledge this domain, so far, we are the first ones who proposed the best possible solution in terms of accurate and efficient classification of different T wave episodes based on nature. Such natural identification entirely relies on the parameters of T wave. In literature, a number of studies found that build a reasonable accuracy level in terms of classification of different T wave episodes, but due to these methods, the difference between flattened T wave and inversion T wave is still unclear [ 30 , 31 , 32 , 33 , 34 ]. Accurate and robust classification of different T wave episodes relies entirely on the identification of the values of T wave parameters especially in terms of T-onset and T-offset [ 35 , 36 , 37 , 38 , 39 , 40 , 41 ]. Such findings are further helpful for highlighting the intensity of MI in terms of ST-segment elevation and ST-segment depression [ 42 , 43 , 44 , 45 , 46 ].

This study delivers the good practice of Kitchenham guidelines [ 47 ]. Execution of this guideline in a systematic way like the first phase reflects the collection of 97 articles through four primary databases. These 97 studies were rectified by applying three different rectification levels and after these levels finally fetched the 26 articles. These 26 studies are the primary source for the execution of this review article. The core focus of this systematic study is to review the latest techniques of T wave anomaly classification and detection. A summary of our contributions is highlighted below.

Execution of in-depth classification based on parametric analysis of different T-wave episodes.

Introducing the discussion session of dependencies factors that work for robust and better classification.

Proposed three-way perspectives with the help of selected articles. These three-way perspectives are designed for robust and accurate classification by using state-of-the-art terminologies like parametric analysis of T waves and classification of different T waves through neural models.

The rest of the paper is organized into different sections. Sect. "  Selection Protocols " will deliver the complete methodology of research conduction. Section 3 covers the discussion portion of selected studies. Sect. "  Methodological Comparison " delivers a short summary of relevant articles in the sense of results. Sects. "  Taxonomical View:Three -  Way Handshake " and 6 highlight the taxonomy and the future directions. Section 7 concludes the summary of this article.

Selection protocols

Healthcare systems give special consideration to scenarios involving risk factors, particularly in the context of cardiac process monitoring. Even minor details of heart activities cannot be overlooked in this process. Accurate and timely identification is crucial for diagnosing various cardiac conditions. In light of these considerations, providing specific attention to diagnostic purposes is essential when dealing with patients affected by myocardial infarction. In myocardial infarction, the most significant part is to identify the defective shape of the T wave. Classification is used to identify the abnormalities of T wave but identifying the abnormality of Flatten T wave is still a problem for researchers and cardiologists. This article delivers a way how to sort out the robust and accurate classification of the different T-wave episodes by manipulating the literature [ 48 , 49 ].

Research questions

RQ1: how it is possible to classify the different anomalies of T wave through parameters of T wave?

RQ2: Which Parameters of T wave plays a vital role for identification the different T wave nature?

RQ3: how neural models are the best methods for identifying the feature dependencies of ECG?

RQ4: What are the main factors that play a critical role in the classification of different T-wave episodes?

Keywords for searching

The formation of SLR is built on the base of keywords, and such keywords are used in the query string for the collection of articles. Table 1 highlights the possible synonyms in this SLR. Table 2 reflects the possible acronyms.

Data source

Four primary databases that are highlighted in Table  3 are used for extraction articles.. This extraction process is executed by considering the last twenty years of articles

The following items are searched based on research questions and relevant literature. Proper classification of the various T wave anomalies based on nature is a hot issue for medical professionals especially flattened T wave. Accurate and robust classification of different T wave episodes is one of the primary purposes of this SLR. For this purpose, the first stage is to discuss all the possible interconnect dependencies of T wave parameters. This SLR is a joint alignment of different behaviors of different T wave episodes along with a discussion of T wave dependencies analysis. Finally, the usage of the above combination with neural models works for the achievement of robust and accurate classification of different T waves [ 50 , 51 , 52 , 53 , 54 ]. In the context of accurate and robust classification, the proposed idea is executed with the query string generation method. Such a method is stated below.

(1). “Behavior impact analysis” OR “Negative Behavior sequences” OR “Behavior Informatics.”

(2). “OnTo Behavioral Model” OR “Behavior Checkpoint”.

(3). “Neural networks” OR “Different methods of neural models for classification” OR “ECG classification through neural models.”

(4). “ECG morphology analysis” OR “ECG feature extraction” OR “ECG feature analysis.”

(5). “T wave behavior” OR “T wave start time” OR “T-onset.”

(6). “T wave behavior” OR “T wave end time” OR “T-offset.”

(7). “T wave behavior” OR “T wave peak value” OR ‘T wave amplitude” OR.

(8). “T wave behavior” OR “T wave time duration.”

These above search items are combined in the form of a string generator by using the Conjunction (AND) and Disjunction (OR) operators. The result of a search string for the collection of relevant literature is as follows:

(“Behavior impact analysis” OR “Negative Behavior sequences” OR “Behavior Informatics” OR” OnTo Behavioral Model” OR “Behavior Checkpoint”) AND (“Neural networks” OR “Different methods of Neural models for Classification” OR “ ECG classification through neural models”) AND (“ECG morphology analysis” OR “ECG feature extraction” OR “ECG feature analysis”) AND( “T wave behavior” OR “T wave start time” OR “T-onset”) AND( “T wave end time” OR “T-offset” OR “T wave behavior”) AND(“T wave behavior” OR” T wave peak value” OR ‘T wave amplitude”) AND (“T wave behavior” OR “T wave time duration”).

Inclusion and Exclusion Criteria

The above connection string is valuable for the selection of related articles. These articles passed through the other filtration process that is mentioned in Table  4 . The formation of inclusion and exclusion checks in Table  4 is formated based on technical-oriented clinical research contributions which belongs to human participants for the collection of data and execution of experiments with ethical clearance. Footnote 1 . The schema of inclusion and exclusion criteria relies on the above connection string. At the first phase, the inclusion of articles primarily depends on any novel approach or technique for the detection of anomalies of T wave. For understanding the different anomalous behaviors of the T wave in ECG, the induction of behavior analytics articles delivers the better knowledge of different dependencies concept. Conversely, articles excluded on the basis of unclear results and invalid techniques. Such type of articles is completely aborted. A few of the traditional techniques are also excluded due to their least effectiveness in percentage accuracy measurement..

Data extraction strategy

All the articles are selected after clearing the inclusion and exclusion stage. These research articles are assessed on the basis of parameters which are highlighted in Table  5 . Specific information was extracted on the basis of predefined extraction items like Title, Publication venue,Publication Year,Approach (Feature analysis, Parametric discussion of T wave and classification of abnormalities in ECG through neural models),Research methods,Research questions addressed.

Above Table  5 displays the data extraction strategy in the form of first extracted identifier S1. Extraction policy is applied to all selected articles that captured unit information in tabularized form, and few samples are discussed in Appendix-A.

Literature quality assessment

The quality assessment activity is the third stage of study selection flow that is highlighted in Table  5 . At first stage of screening, the combination of both schema’s inclusion and exclusion are worked with assessment criteria of literature for the identification of relative research articles .

Queries of Table  6 are implemented on those articles that are selected after first rectification process. Answer the queries of Tables 6 in the format of YES, NO and PARTIALLY that is the next step for further improvement of rectification process by the induction of five domain reviews (Rev). These reviews mark the score of the second stage selected articles according to the format of 0.5,0 and 1(represents the PARTIALLY, NO, and YES).

Selected Search Item

Execution of this systematic study relies on the rectification process, at first stage collection of 97 articles is done based on our best knowledge of the domain. In the next stage, another filter out of scope is worked on the basis of the title and abstract. After this stage, we get 60 articles out of 97 articles bank (excluded 28 articles on the basis of out of scope and 9 excluded on the basis of improper discussion towards problem). After exclusion of articles on such stage then again implement the next rectification process. At such point the articles based on the criteria for inclusion and exclusion that are highlighted in Table  4 . In this rectification, we get 42 articles out of 60 articles (exclude 18 articles on the basis of another analysis discussion). Now after such processes again implement the second rectification process but this time consider such articles which are more in-depth discussion towards T wave anomalies, and also concern the quality parameters of literature that are mentioned in Table. The scoring range of 42 articles is displayed in appendix-B. Appendix-B displays the scoring calculator which works based on five reviewers (Rev). The selection of five reviewers on the bias of standard EASE 7 which covers the expertise, and potential computing interests of the reviewer considers the diversity and inclusion of reviewer Footnote 2 After selection. After the Selection of reviewers mark the score based on the literature quality assessment. The range of scoring is 0.5 to 1, in this range 0 represents false assessment, 0.5 shows partially, and 1 indicates accurate response towards quality assessment.The next step in this rectification process is to set the low threshold value that is equal to 3. Such a statement indicates that when your article scores equal to or higher than the threshold point, then the inclusion of the article is possible otherwise skip that particular article. After this rectification process, we fetched 26 articles out of 42 (excluding 16 articles based on the disappointing result in terms of quality assessment). Figure  4 is a pictorial representation of the whole selection process of articles.

Execution of the scoring table at a stage of 42 articles reveals the importance of different reviewer thinking, such a thoughtful process is highlighted in Fig.  1 . According to Fig.  1 , the maximum range of scoring is 4.5, and the minimum one is 0.

Articles maps with a scoring range

In the scoring calculator, the 5 reviewers investigated each article and delivered the score with their best knowledge. The replica of the above statements is represented in the form of a showcase of reviewers’ trends in Fig.  2 . The data mounted below showcase highlights the average, maximum, and minimum scoring rates. According to Fig.  2 , the trend of the reviewer’s rating is displayed in such a manner that represents the article's rating in the form of 0. 0.5 and 1. Such a point indicates the selected 42 articles partially satisfied the pre-defined quality assessment, but with the help of this showcase, further rectification investigations are so simple.

Rating ratio of different reviewers

A further observation in terms of review analysis delivers a broad picture of selected articles that are mapped with a research problem. The expansion of the reviewer’s trend in the form of the reviewer’s analysis is represented in Fig.  3 . Figure  3 represents the in-depth investigations that indicate the variations in the reviewing process. Such a process indicates the domain knowledge of these reviewers towards the defined problems. The above statements are executed in such a manner that reviewer 1(rev1) marks the article [ 2 ] as 1 based on the satisfaction of the quality parameters. The same reviewer (rev1) assesses the quality of the article [ 55 ] with parameters of quality assessment; such a reviewing process is applied to all selected articles. Figure  3 highlights the different behaviors of different reviewers towards the selected articles as the process is described in Fig.  4 .

Reviewer analysis of selected articles

Selection Process of articles for composition of this systematic study

Before highlighting the results from the analysis, the bibliometric data and overview of these studies are initially reported. Table 7 highlights the overview of all selected studies and bibliometric information.

The construction of the above table highlights the research questions addressed in different 26 rectified articles. Representation of the ✓ defines the research question address up to satisfied level. Similarly ✗ represents the unsatisfactory results that are not addressed to any research questions. The above construction represents that selected articles are in a range of years 2008 to 2023.However, taxonomical view some artciles consider from 1990 to 2007. Article [ 57 ] is the most cited article in the database of 26 research articles. This article addresses the three research questions except research question 3.. Figure  5 display the percentage level of conference, journal and book chapters [ 37 ]

Categorization of selected articles in the form of pie view

Methodological comparison

ECG researchers and cardiologists highly value the classification of various T-wave episodes with minimal ambiguity. In the context of this aim, such study is executed in the form of different operational investigations processes that represented the solutions to the defined problems [ 38 , 39 ]. With the aim of this solutions, an increasing number of studies have found that relates the parametric importance of different ECG features [ 40 ]. According to literature, these parametric factors rely on external or internal variables, which can be referred to as feature dependencies [ 41 , 43 , 45 ]. Various state-of-the-art classification techniques for different T-wave patterns are sourced from the existing literature [ 58 , 59 ]. In the construction of combo pack of solution for better classification of T wave anomalies, firstly designed or identified the best possible research questions after reviewing the literature [ 5 , 50 , 51 , 52 , 53 ]. These research questions are analyzed in a systematic way and then report the solutions to these questions in a combo pack solution (better classification and visibility of T wave anomalies). Figure  6 is a showcase categorization of research questions [ 60 , 61 ].

Categorization of research questions address up for result generation

RQ1: Parametric analysis of T wave

Distinguishing typical and non-invasive patterns in T-waves is crucial for identifying the nature of myocardial infarction (MI). For achieving such fruitful results of classification by recognizing the keynotes. An increasing number of studies found during the process of literature review that different features of ECG are dependent upon their own parameters. By the help of these parameters’ the possibility of natural extraction of any feature is increased one. The importance of parametric solutions in the case of T wave is always on the high node. Literature displays the four critical parameters of T wave for the extraction of the nature of T wave during the classification. Identification of T wave parameters T-onset, T-offset, T-peak, and T-duration are helpful in own nature extraction and also fruitful for different analysis and techniques like beat-to-beat analysis, the myocardial infarction detection algorithm, and the T-wave alternans detection algorithm (TWA) are discussed in [ 11 , 12 , 22 ]. In beat-to-beat analysis, results are based on the QT interval, which is determined by the parameters Q-onset (start time) and T-offset (end time). Likewise, the myocardial infarction detection algorithm employs the window detector method to capture the peak value of the T-wave (T-peak). Finally, the TWA detection algorithm operates using the R peak as a fiducial point to calculate the T-onset. This algorithm mainly highlights the intensity of the T wave and also reflects the dependencies factor of features that indicate other research problems. [ 62 , 63 , 64 , 65 ] Such a research problem is a part of this SLR in RQ4. Below Fig.  7 is a showcase of four parameters of T wave [ 66 , 67 ]

Parametric analysis of T wave in ECG

RQ2: T wave anomalies through a T-onset parameter

Identification of abnormalities in any time series in the dataset is highly desirable especially when you work out on any healthcare system [ 68 , 69 , 70 ]. Sometimes these systems deal with a risky situation like a case of MI in ECG. In connection with previous statements, accurate MI identification is essential for diagnostic purposes, as it addresses high-risk factors. In the diagnosis of MI, the primary focus is on recognizing sudden, non-invasive changes in the T-wave; this is a crucial initial step is to highlight the abnormal T-wave episodes [ 71 , 72 ]. Better classification T wave anomalies depends on a parametric solution but primarily relied on T-onset parameters. An increased number of studies were found during the investigation of dependencies calculation of the T-onset parameter. T wave alterations detection (TWA) algorithm is one of them that is picked from the literature. This algorithm primarily functions to emphasize the energy intensity of the T-wave [ 73 ]. The TWA algorithm operates based on the R peak as a fiducial point and takes into account the RR intervals [ 22 , 74 ]. A key component of the TWA algorithm is the RR interval, which is represented in the equation below. The presentation of this equation reflects the energy intensity of the T-wave, whether in a standard form or as anomalies, depending on the T-onset parameter of the T-wave [ 22 ].

4.3 RQ3: Classification through Neural Models. Detection of the irregularity in the sequences of data reveals the difference like the data. The classification process is commonly employed to identify irregular or unconventional patterns (novelty detection) in ECG data. This process differentiates between regular and irregular patterns. Traditionally, various ionic classification techniques are utilized to distinguish irregular responses, except for the predictive element. In cases of rapid and accurate classification commonly used methodologies are a neural network (NN) or artificial neural network (ANN) [ 75 , 76 , 77 , 78 ]. Figure  6 refers to the basic structure of NN with the inclusion of input, hidden, and output layers for classification.

Conducting operational investigations in any neural network methodology [ 79 , 80 , 81 ] involves a structured approach. The primary objective is to train the neural network by adjusting the weights of each unit to minimize the error between the desired output and the actual output. Error derivation of the outputs is a critical phase that shows changes in error as the weights are adjusted slightly, either increased or decreased [ 58 ]. The Back Propagation (BP) algorithm is commonly employed to assess and minimize error. The primary focus of this study is to classify anomalies in the T-wave of ECG signals using a feed-forward multi-layer neural network in conjunction with the backpropagation algorithm. In an ideal scenario, the neural network's target values closely approximate the expected output values. The Levenberg–Marquardt algorithm has been found to yield the best results in this context. The primary achievement of this algorithm is the accurate discrimination between two types of heartbeats, namely regular heartbeats and premature ventricular contractions (PVC). [ 32 , 59 ].

Another success story of the artificial neural network (ANN) as in Fig.  8 is to detect the most significant part of the ECG waveform (QRS complex detection). In the scenario of accurate detection of the QRS complex, after the R peak detection by the formation of the feature vectors under usage the amplitude of the significant frequency components of the DFT frequency spectrum [ 58 ]. By using the knowledge from the literature, categorized the classification of different features of ECG through two different scenarios. One is a supervised scenario in which training samples are labeled as a standard or anomalous (abnormal) [ 82 , 83 ]. The second one is unsupervised scenarios; this scenario works in such a manner that training samples are inducted in the neural network without any label [ 53 , 54 ].

The basic structure of the neural network, in input, hidden, and output layers are essential builder part

RQ4: Dependencies impact of the T wave

An anomalous pattern classifier is a valuable tool for cardiac specialists, particularly in high-risk situations such as the sudden onset of myocardial infarction (MI). The initial step in identifying these patterns, especially in the case of MI, is to pinpoint the underlying cause by analyzing T-wave changes [ 84 , 85 ]. Understanding the root cause becomes feasible by highlighting the dependencies of the T-wave. Calculating these dependencies requires having access to all the parameters of the T-wave, including T-offset, T-wave duration, T-wave amplitude, and T-onset values, which are essential for accurately tagging T-wave anomalies All these parameters have equal importance that already discussed in the above address up part of RQ1, but T onset parameter has a unique concentration in case of extraction the nature of the anomalies. In the literature review session, Numerous studies have contributed to ECG feature analysis, and this analysis is often conducted using the advanced methodology known as Wavelet Transform Module Maxima (WTMM). The T-wave detection algorithm employs WTMM to identify T-waves, relying on various T-wave parameters, including a combination of T-wave amplitude and slope. In addition to this, another state-of-the-art approach, the Trapezium method, is utilized for T-wave detection, primarily through the T-peak amplitude parameter. These two state-of-the-art methods also indirectly aid in uncovering dependencies. In connection with such a statement, the same case is applied to different ECG features by using the above concept of dependencies. Highlighting the dependencies factors are quite hard without finding the exact nature of any ECG feature [ 86 , 87 , 88 ]. Similarly, in the specific case of two T-wave anomalies, T-wave inversion, and flattened T-waves, they are often grouped together, yet their behaviors in the context of MI differ significantly. To emphasize the distinctions between inverted and flattened T wave, we need some sort of novelty in our solution [ 89 , 90 ]. For the achievement of this aim, we need particular checkpoints that are worked on the basis of the parametric solution, like behaviors identifier that worked in the Ontology behavior model (OntoB). The ontology behavior model is a composition of several factors which are briefly discussed in below Fig.  13 and the flow of these factors is highlighted [ 15 , 30 , 52 ].

Behavior descriptor: Behavior descriptor represents the core behavior properties or elements. Behavior Aggregation: Combination of behaviors hierarchical and hybrid These components are integrated to produce Semitics of TS through both intra-coupling and inter-coupling relationships within a set of behaviors [ 56 ]. The Behavior Constraint Indicator module formalizes natural language descriptions of behaviors into logical formulas. The Behavior Checker module serves as a checkpoint to verify the accuracy of the behavior constraints and properties claimed by the Behavior Constraint Indicator [ 51 ]. The Behavior Model Refiner module is used to make further refinements to the behavior model, addressing any identified issues. Finally, the Behavior Model Exporter exports a stable and desired behavior model as a result of the modeling process [ 91 , 92 ]. Figure  9 higlighted the ontology behavior model.

Ontology Behavior model: Identification of intercouple and intracouple relationship in behaviors and behavior refiner

Efficient formation of the classifier is highly desirable for tagging the anomalies in ECG signals due to life care issues. We cannot afford any minor ambiguity in the monitoring process of the heart. ECG signals would be real-time patient’s heart data and risky if we missed any activity at any particular time that may cause the death of the patient. For the last decade, cardiologists and medical professionals are still searching for a way to highlight the intensity of myocardial infarction. This intensity is measured with the help of dependencies factors. In one scenario these dependent factors are measured with the help of changes in the T wave or anomalies of T wave [ 48 ]. For reduction of risky situations by integration the novel approach for determining the dependencies which are helpful for broad classification of different T wave episodes. Analysis of ECG is primarily dependent upon the classification result which represents the regularity and irregularity in ECG signals [ 29 ].

Considering the focus of this article, our primary concern is to find a way how it is possible to get robust and accurate results of diverse T wave classification. In the literature survey process, a lot of research found that worked in the form of, different methodologies and techniques for T wave classification. But efficient and accurate results have always attracted the researchers. In-depth analysis of literature, survey process creates a path for solution-oriented results. This idea works on the basis of three different perspectives, such are T wave parametric analysis, the combination of feature analysis of ECG and the behavior analytics techniques in the light of coupling concept of behavior analytics quire clears the concept of dependencies of T wave, finally add up the backpropagation and multi feed-forward algorithms of neural network in different models of neural network. Figure  14 is the pictorial representation of our proposed idea towards the robust and accurate classification of different T wave episodes. Figure  10 represnts the parametric classfication of different T-Wave anomalies.

Classification of T wave anomalies analysis

The most important part of this proposed study is our critical analysis that we get the knowledge after the literature survey session. According to our knowledge so far, we are the first ones who discussed the dependencies factor for the robotic classification of T wave anomalies. Such discussion will not be limited to robotic classification of T wave anomalies, but also creates a new room for researchers to crucially think about the different features of ECG in the light of dependencies factor. The inclusion of dependencies factor in feature analysis of ECG makes sense for depth analysis of different CVDs like myocardial infarction (MI), atrial fibrillation (AF), and premature ventricular contraction (PVC). Detection of these CVDs depends upon the different features of ECG [ 93 , 94 ].

Taxonomical View:Three-Way Handshake

A joint venture of backpropagation approach of neural network (NN), parametric analysis of T wave, and novelty of behavior analytics plays an important role in the rapid and exact classification of T wave anomalies based on nature. Results of the above combination are already achieved as work on singleton, but our approach towards classification is robust and accurate. The methods of DWT for T wave detection, T wave detection algorithm, TRA approach, and wavelet analysis all are worked under the coverage of parametric analysis of T wave classification. Figure  11 indicates the addressing literature in a pictorial model [ 3 , 49 , 50 ].

Parametric Classification for T wave

The novelty of behavior analytics in this combination is playing the critical role for highlighting the dependences of T wave on the other features of ECG. In latest TWA algorithm, the same concept of features dependencies is reflected in the form of highlighting the T wave changes with the integration of R peak. The dependencies concept is further explained in behavior analytics in the reflection of the intercouple concept of behavioral theory. Below Fig.  12 highlights the concepts of behavior analytics with studied articles [ 50 , 52 ].

Dependencies perspective of the T wave

In the perspective of a neural network using the combination of feedforward and backpropagation algorithms with the conventional techniques of neural network (pattern recognition and time clustering). Classification of T wave through neural models for the identification of dependencies is clearly highlighted. These dependencies are identified by some checkpoint; Fig.  13 capture the whole scenario [ 53 , 54 , 59 ].

Classification of T wave with NN

A complete package of three-way handshake process is highlighted in Fig.  14 . The result of this combination is worked in such manner that the usage of different neural models in the first perspective is for validation and testing of our different results which we get from other two perspectives. In second dependences perspective, the OnToB model is worked on the basis of the output of neural models in a queued manner (first come first serve). This OnToB checks the indication of T wave in the light of coupling concepts of behavior analytics (intercouple and intracouple) that reflects the identification of dependencies factor [ 95 ]. This dependencies factor works as an input of the parametric perspective of T wave for the classification of the regular and anomalous T wave [ 96 , 97 ]. One output of each regular and anomalous T wave is rebooted into the OnToB model and the second output of each regular and anomalous T wave are also rebooted in the combination of algorithms for further subclassification.

Three-way handshake structure for robust classification of different T wave episodes

Future research directions

In an ideal case highlighting the CVDs at early time delivers the in time treatment to patients. In Process of extraction of different features in ECG, at first, stage implemented the different noise filtration methods by setting the threshold value. From literature, we got different methodologies for feature extraction with different techniques. But after reviewing the articles, one issue is prevalent in every technique such issue is noise filtration. Reason for such issue is due to lack standard or state-of-the-art work on the threshold value. In case of T wave applied noise filtration, we need some sort of standard baseline value or threshold value (need a peak point and time duration of T wave) like the representation of Table  7 and reading parameters of ECG from Table  8 .

Some essential research directions are highlighted in Fig.  15 after analyzing the selected studies.

Analysis of dependencies factor of different CVDs, such analysis helps to provide the predictive information

Calculation of dependencies factor through dimensional reduction techniques PCA, SAX on ECG signal

Feature engineering embeds on ECG and works with the presence of noise in signal

Future direction for ECG analysis in terms of different anomalies analysis

Dependency analysis of different CVDs

Identification, the dependencies of each feature, is an ideal case for diagnostic purposes refering to Fig.  15 . Such scenario will be implemented through the parameters of each feature as the above parametric discussion of T wave. Every CVDs depends on different features, and such features have some critical parameters. Highlight the dependencies between these parameters; such process will be performed by conducting the parametric analysis along with the coupling concepts of behavior analytics.

Calculation of dependencies through dimension reduction

Complexities reduction is the best way to proceed the data analysis in smooth and accurate manner. With concern of this ECG analysis, the dimensional reduction is highly demandable due to such reduction calculation of dependencies between different features will be simpler one. Such statement operates in ECG analysis by using the primary tacts of dimensional reduction. Most important part of the ECG is considered QRS complex. Reduction or segmentation of such part will be valuable for better analysis of R peak detection. PAC and SAX techniques will be used for segmentation of QRS complex and also cross-check the results of each technique. With the help of such segmentation, premature ventricular contraction (PVC) detection will be a more accurate job than previous work.

Feature engineering in ECG

Implementation of feature engineering reduces the noise involvement in ECG signal. In simple words, we will perform better ECG analysis with the presence of noise by embedding the feature engineering. In this engineering components of the system will be an enhancement and scalable for in-depth investigations.

Rapid and accurate classification of different T wave episodes is highly desirable in terms of identification of T wave anomalies. T wave anomalies is a crucial concern for the ECG researchers in the sense of analyzing the MI. This systematic study highlights the critical question which relates the behavior of T wave anomalies and dependencies between them. The purpose of this article is to find the solution of queries. Proceed this aim by a collection of 97 articles from four prime databases. In next phase set the rectification process for Pursuing a resolution for the specified issue by applying the filtration on 97 articles. Such filtration criteria set Based on the criteria for inclusion and exclusion, literature quality assessment and data extraction strategy. After these stages finally get the 26 refined articles that are the part of this systematic study. These articles included the parametric analysis of T wave, features analysis of ECG for understanding the dependencies of T wave parameters along with articles of behavior analytics for understanding the dependencies factors.

Few gaps are highlighted after the literature survey process, these gaps are related to the requirement of further qualitative improvement of noise filtration technique and feature detector window method. In the end portion of this study, we have used our best knowledge and skills by critically brainstorming activities. Such activities are worked under the coverage of selected articles for the best possible solution for rapid classification of T wave episodes. Our research is based on the joint version of three-way perspective. The proposed method is a three-way handshake that works by inclusion the neural models with their prime algorithms, and after then OnToB behavior model works under the light of coupling concepts of behavior analytics along with parameters of T wave. Such approach is a novelty theme-based solution and a perfect one for the rapid classification of the different T wave episodes.

Availability of data and materials

The extracted process of articles is mainly relying on four primary databases, and these are all available publicly.

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Acknowledgements

This work was supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University (IMSIU) (grant number IMSIU-RP23034)

This work was supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University (IMSIU) (grant number IMSIU−RP23034).

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Uzair Iqbal

Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), 11432, Riyadh, Saudi Arabia

Riyad Almakki, Abdullah Altameem & Mubarak Albathan

Department of Computer Science and Technology, Harbin Institue of Technology, Harbin, Heilongjiang, China

Muhammad Usman

Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia

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Uzair Iqbal and M. Usman works on the design of protocols of this research work. Riyad Almakki and Abdullah Altameem work on the collection of clinical studies and funded this work. Mubarak Albathan and Abdul Khader Jilani validate the conducted research work.

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Iqbal, U., Almakki, R., Usman, M. et al. Methodological identification of anomalies episodes in ECG streams: a systematic mapping study. BMC Med Res Methodol 24 , 127 (2024). https://doi.org/10.1186/s12874-024-02251-0

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