research paper on role model

Loading metrics

Open Access

Research Matters

Research Matters articles provide a forum for scientists to communicate why basic research in their field matters.

See all article types »

The Importance of Role Models in Research

* E-mail: [email protected]

Affiliation Department of Molecular Genetics and Microbiology, Center for Infectious Diseases, Stony Brook University, Stony Brook, New York, United States of America

• James B. Bliska

Published: June 2, 2016

• https://doi.org/10.1371/journal.ppat.1005426
• Reader Comments

Citation: Bliska JB (2016) The Importance of Role Models in Research. PLoS Pathog 12(6): e1005426. https://doi.org/10.1371/journal.ppat.1005426

Editor: Grant McFadden, University of Florida, UNITED STATES

Copyright: © 2016 James B. Bliska. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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

Competing interests: The author has declared that no competing interests exist.

I have been extremely fortunate to have a number of exceptional role models during my research career. By emulating these individuals, I gained the skills and the knowledge that I needed to achieve a successful research career in the field of bacterial pathogenesis. It is not surprising that my PhD dissertation advisor at Berkeley, Nicholas Cozzarelli, and my postdoctoral advisor at Stanford, Stanley Falkow, both highly eminent and successful scientists, were important role models. However, as I look back, it becomes clear that my interactions with mentors when I was an undergraduate were of equal importance in my scientific development. Below I describe some of my experiences as an undergraduate, and I hope that this story will serve as a reminder of the importance of role models for young scientists.

My research career got its start at a bus stop on a bitterly cold morning in the winter of 1980 in Madison, Wisconsin. I was on my way to the University of Wisconsin, where I was an undergraduate, to interview for a research aid position at a laboratory. A middle-aged woman at the bus stop commented to me that I should be wearing a hat and gloves on such a cold morning. I politely thanked her for her advice, but was secretly irritated, in part due to my nervousness about the upcoming interview. When I arrived at the laboratory to meet with a secretary for the interview, who was sitting behind the desk in the office? The woman from the bus stop! Her name is Frances Mann and, after I was hired, Frances often joked that she gave me the job because I needed the money to buy a hat and gloves. She also enjoys taking full credit for starting my research career.

By sheer luck I was hired into the laboratory of Oliver Smithies, who would go on to win a Nobel prize in physiology or medicine in 2007 for his role in discovering how to modify genes in mice using embryonic stem cells. I was initially employed to wash glassware, but soon was given other tasks, including purifying recombinant plasmids from Escherichia coli . These were the early days of recombinant DNA and “plasmid preps” were done in high-containment suites and took days to complete. Supercoiled plasmid DNA was isolated using equilibrium density centrifugation in a cesium chloride–ethidium bromide solution. In one of the last steps, in a darkened room, a UV light was used to illuminate the band of supercoiled plasmid DNA in the centrifuge tube, which was collected with a pipette. Siphoning off that glowing band of supercoiled plasmid DNA was exciting and satisfying, and the experience likely contributed to my long-standing interest in the biology of bacteria and their plasmids. It was in this environment that I encountered my first scientific role model, a technician in the laboratory named Natalie Borenstein. By following Natalie’s example, I learned to always follow good sterile technique, to never cut corners, and to pay close attention to details. Students who have passed through my laboratory can attest to my attention to detail, and they can thank Natalie for this attribute.

Eventually I became immersed in the laboratory, attended group meetings, and pursued a research project for my undergraduate honors thesis. Nobuyo Maeda, an outstanding postdoc in the laboratory, guided my research and became a second influential role model. With Nobuyo’s help I was able to determine the sequence of 3.1 kb of a Chimpanzee globin gene by the Maxam and Gilbert technique. From her example, I learned how to analyze and publish data, and she taught me that success in science requires dedication and hard work.

The highlight of my research experience as an undergraduate was when I was placed at a bench space adjacent to Oliver’s. Although he had a conveniently located office, it was always empty because Oliver preferred to spend all of his time at the bench. Whether he was running experiments, writing manuscripts, or discussing results, he was always in the laboratory. Oliver had an enormous impact on me and, as a result, I decided to apply to graduate school. From his example, I learned that a successful scientist can have boundless enthusiasm for research and a balanced approach to life. Oliver is fond of saying, “Make your first love your hobby and your second love your career.” His first love is flying. When Oliver invited me to fly in a glider with him one day, I jumped at the chance. That was an experience I will never forget.

In my own laboratory I have made an effort to hire undergraduate research aides. I try to foster a laboratory environment in which all members (technicians, graduate students, postdocs, and principal investigators) can serve as scientific role models. It seems to be working, as several of these research aides have developed into outstanding undergraduate researchers and have gone on to obtain PhDs or MD/PhDs and successful careers in science.

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

Left to right: Hachung Chung, Celine Pujol, Ryan Rampersaud, Yue Zhang, Maya Ivanov, Gloria Viboud, Jens Grabenstein, James Bliska. Hachung and Ryan were laboratory aides that that became outstanding undergraduate researchers and then went on to obtain a PhD at Harvard and a MD/PhD at Columbia, respectively. Hachung is currently a postdoc at Rockefeller University and Ryan is a resident in psychiatry at UCSF. Photo provided by Michelle Ryndak.

https://doi.org/10.1371/journal.ppat.1005426.g001

• Open access
• Published: 02 December 2021

Which role models are effective for which students? A systematic review and four recommendations for maximizing the effectiveness of role models in STEM

• Jessica R. Gladstone 1   nAff2 &
• Andrei Cimpian   ORCID: orcid.org/0000-0002-3553-6097 1  

International Journal of STEM Education volume  8 , Article number:  59 ( 2021 ) Cite this article

25k Accesses

63 Citations

64 Altmetric

Metrics details

Is exposing students to role models an effective tool for diversifying science, technology, engineering, and mathematics (STEM)? So far, the evidence for this claim is mixed. Here, we set out to identify systematic sources of variability in STEM role models’ effects on student motivation: If we determine which role models are effective for which students , we will be in a better position to maximize role models’ impact as a tool for diversifying STEM. A systematic narrative review of the literature (55 articles) investigated the effects of role models on students’ STEM motivation as a function of several key features of the role models (their perceived competence, their perceived similarity to students, and the perceived attainability of their success) and the students (their gender, race/ethnicity, age, and identification with STEM). We conclude with four concrete recommendations for ensuring that STEM role models are motivating for students of all backgrounds and demographics—an important step toward diversifying STEM.

Introduction

In the United States, STEM fields fail to attract and retain women and racial/ethnic minorities in numbers proportional to their share of the population (e.g., National Science Foundation, 2017 ). Concerned about this inequity, many researchers, teachers, and policymakers have been seeking ways to increase the gender and racial/ethnic diversity of the STEM workforce. A popular tool for this purpose is to introduce students to STEM role models, which we define here as individuals who can positively shape a student’s motivation by acting as a successful exemplar. Well captured by the slogan “you can’t be what you can’t see” (variously attributed to Gloria Steinem, Sally Ride, and Marian Wright Edelman, founder of the Children’s Defense Fund), the use of role models is often billed as the one-stop solution for increasing diversity in STEM (e.g., Dean, 2014 ; Levere, 2018 ). And role models seem like a promising tool indeed—for instance, exposure to role models is relatively low-cost (Herrmann et al., 2016 ) and is flexible and adaptable to a wide range of contexts and student ages (Lawner et al., 2019 ).

However, the empirical evidence for the effectiveness of role models as a means of motivating diverse students to pursue STEM is sparse and not altogether straightforward. For example, a 2007 practice guide released by the Institute of Education Sciences on means to encourage girls in math and science concluded that there is a “low” level of empirical evidence for the recommendation to expose girls to role models (Halpern et al., 2007 ). Although this guide was released more than a decade ago, the empirical evidence for role models as a means of increasing diversity in STEM remains complex and contradictory (e.g., Lawner et al., 2019 ). Yet, these nuances have not blunted excitement among educators and the general public about role models as a motivational tool—if anything, enthusiasm for role models has probably increased in the intervening time.

Thus, our goal here was to provide an accessible and brief, but at the same time systematic (Siddaway et al., 2019 ), review of the current literature on role models to assess their effectiveness as a motivational tool for diversifying STEM. However, rather than focusing on the general question of whether role models can motivate diverse groups of students (the answer to which likely depends on many specifics), we set out to answer a more targeted set of questions about which role models are effective for which students in STEM. Specifically, we examined two types of potential moderators of role models’ effectiveness: (1) the features of the role models themselves (which we term “role model moderators”), and (2) the characteristics of the students exposed to the role models (which we term “student moderators”). We briefly unpack each of these categories of moderators before describing the theoretical frameworks that anchored this research.

With respect to role model moderators, our systematic review aimed to identify which features of a role model are effective in motivating STEM pursuits and, conversely, which features of a role model backfire, lowering student motivation. We used prominent theories of motivation to identify the features of role models that might be expected a priori (on theoretical grounds) to influence their effectiveness as motivational tools. Specifically, we focused on social cognitive theory (Bandura, 1977 , 1986 , 1997 ), expectancy–value theory (Eccles & Wigfield, 2020 ), mindset theory (Dweck, 1999 , 2006 ; Dweck & Leggett, 1988 ), and attribution theory (Graham, 2020 ; Weiner, 1985 ) and drew out each theory’s implications for the features a role model should have to be effective. We then assessed the level of evidence in the literature for each of these theoretically-motivated role model moderators.

With respect to student moderators, our systematic review investigated how students’ gender and race/ethnicity moderated the effects of role models, consistent with the focus of our review on diversifying STEM. In addition, we investigated whether role models vary in their effectiveness as a function of student age, from elementary school to college. The final student moderator that was considered here was students’ attitudes toward STEM: Students who have not yet identified with this domain (which is likely true of many students from underrepresented backgrounds) may have different concerns, and as a result, may be motivated by different types of role models, than students who are farther along in their STEM educational trajectory (Drury et al., 2011 ). In other words, the qualities of a role model that can help attract a student to STEM in the first place and make them more likely to identify with this domain may be different from the qualities of a role model that can help retain a student and solidify their identification with STEM.

To maximize the usefulness of this systematic review for the education research and practice community, we conclude with a set of concrete recommendations extracted from our review of the literature. These recommendations are intended to increase the effectiveness of role-model interventions as a tool for diversifying STEM. We hope these guidelines will be useful not just for researchers and policymakers who are considering large-scale interventions that involve role models but also for educators who might want to use role models in their classrooms.

Defining role models

Above, we defined role models as individuals who can positively shape a student’s motivation by acting as a successful exemplar. To arrive at a more precise definition, it is helpful to further differentiate role models from mentors and sponsors (e.g., Crosby, 1999 ; Downing et al., 2005 ; Gibson, 2004 ). Although all three types of career guides can be helpful to students, role models are distinguished from the other two types of career guides by the fact that they need not (and in fact often do not) have any prior relationship with the students whom they are influencing. In contrast, mentors and sponsors must have a prior relationship with the students to qualify as such, with the difference between them being that mentors provide counseling and general encouragement, whereas sponsors provide practical guidance (e.g., Downing et al., 2005 ). Thus, for purposes of our systematic review, we focused exclusively on empirical evidence concerning the motivational effects of exposure to STEM experts with whom students have no prior relationship. This distinction ensures that the conclusions we draw from our systematic review pertain to role models per se rather than capturing a more diffuse mix of role models, mentors, and sponsors.

Role model moderators: What features do effective role models possess?

To inform our systematic review, we look to four well-established theories of motivation that have direct implications for the features that a role model should embody to be motivating. We briefly summarize each theory before drawing out its implications with respect to role models. To clarify, our analysis of these theories is not part of the systematic review per se—it simply lays the conceptual groundwork for the systematic review of the empirical evidence, which provided a test of the features highlighted here. It is also important to note that we are not the first to identify these role model features. Inspired in part by the same theoretical frameworks we integrate here, previous studies have explored how some of these features (e.g., a role model’s competence; Marx & Roman, 2002 ) relate to students’ motivation. Thus, the contribution of the present work does not lie in identifying the role model moderators per se but rather in systematically reviewing the evidence for them, which to our knowledge has never been done. The section below simply provides a theoretical anchor for the systematic review that follows. One final caveat: In this section, we discuss which features make role models motivating in a general sense, without factoring in whether these motivational effects vary as a function of students’ own characteristics; the latter issue is tackled in the section on student moderators.

Social cognitive theory

At the heart of Bandura’s social cognitive theory (Bandura,  1977 , 1986 , 1997 ) is the motivational construct of self-efficacy—one’s perceived ability to learn and do well in a domain. There is a large and growing body of work that demonstrates that self-efficacy is an important predictor of various student outcomes, including students’ choice of activities, their persistence in a task or domain, and their achievement (Bandura, 1997 ; Klassen & Usher, 2010 ; Schunk & Usher, 2019 ). Another central tenet of social cognitive theory is that students’ self-efficacy is shaped by their social environments (Schunk & Usher, 2019 ) and that engaging in observational learning is part of the process by which students’ own self-efficacy develops (Schunk & DiBenedetto, 2016 , 2020 ).

Social cognitive theory’s connection to role models is straightforward. In fact, Bandura (e.g., 2001 ) explicitly stated that a key source of self-efficacy is observing a relatable role model succeed on a similar task. Specifically, there are at least three features of the role model that can affect students’ self-efficacy and achievement (Bandura & Walters, 1963 ; Schunk & DiBenedetto, 2020 ). The first feature is the perceived competence of the role model Footnote 1 : Students who observe others perform successfully on a task may believe that they can also be successful on the task. In other words, students may believe they can emulate a role model’s performance, which then raises their own self-efficacy (Schunk & DiBenedetto, 2020 ; Schunk & Usher, 2019 ). The second feature is the perceived similarity (or relatability) of the role model to the self (i.e., the student). According to Schunk and Usher ( 2019 ), students who perceive the role model to be similar to themselves are more likely to be influenced by the model. This is because sharing a degree of similarity makes the role model’s success more informative about the students’ own chances of succeeding in the future (Bandura, 1986 ; Schunk & DiBenedetto, 2020 ). Third, students’ self-efficacy should benefit to the extent that they perceive the role model’s success to be attainable. If what the role model has achieved feels out of reach for a student, their self-efficacy in the relevant domain may suffer.

The third feature (perceived attainability) is related to the second (perceived similarity) insofar as the success of a model who is perceived as similar to the self may thereby also be perceived as more attainable. However, a role model’s success can be perceived as attainable for other reasons as well (e.g., the steps to achieve it are clear), regardless of the student’s similarity to the model. The first feature we discussed (perceived competence) has implications for attainability as well. Although portraying the role model as successful may inspire a student to believe that they can succeed as well, an extraordinary level of success might feel unattainable and therefore be demotivating (e.g., “she can do that, but I can’t!”).

In summary, social cognitive theory suggests that role models can increase students’ self-efficacy, an important motivational variable. Extrapolating from this theory of motivation, we argue that the features of a role model that are key for students’ self-efficacy are the perceived competence of the role model, the perceived similarity to the role model, and the perceived attainability of the role model’s success.

Expectancy–value theory

According to expectancy–value theory, newly labeled situated expectancy–value theory (Eccles & Wigfield, 2020 ), students’ motivation to pursue an activity is a function of students’ beliefs about the likelihood of success in that activity (“expectancy”) and the value they perceive that activity to have for them (“value”) (Eccles & Wigfield, 2002 , 2020 ). Consistent with this claim, students’ expectancies for success in a domain (e.g., STEM) and the subjective value they attach to that domain are strong predictors of outcomes such as achievement, course enrollment intentions, and college aspirations (e.g., Lauermann et al., 2017 ; Nagengast et al., 2011 ; Perez et al., 2014 ). Relevant for our purposes here, expectancy–value theory proposes that students’ expectancies for success and subjective task values are shaped by various socialization factors, including role models (Eccles & Wigfield, 2020 ; Parsons et al., 1982 ; Simpkins et al., 2015 ; see also Morgenroth et al., 2015 ). In what follows, we extrapolate from expectancy–value theorizing the features that should make a role model motivating, focusing in turn on expectancies for success and subjective values.

With respect to expectancies, the arguments that we made in the context of social cognitive theory apply here as well. This is so because students’ self-efficacy in a domain shapes their expectancies for success in that domain (see Wigfield & Eccles, 2000 , for some nuances); thus, the features of a model that increase students’ self-efficacy should increase their perceived likelihood of success as well. With respect to subjective values, we argue that the model’s similarity to the self may be particularly effective in boosting the value that students assign to a particular domain. If students see someone who is meaningfully like them pursue a STEM career, for example, they may infer that STEM careers are a good fit for people like them—that these careers allow people like them to pursue goals that are important to them and express core aspects of their selves. Such inferences should increase the value of a STEM career in students’ eyes. Footnote 2 In addition, these inferences seem particularly likely if students perceive the role model to be similar at a deeper, psychological level (“who they are as a person”)—not merely in terms of surface-level demographic characteristics (“what they look like”).

In summary, although expectancy–value theory and social cognitive theory focus on different aspects of motivation, they converge on similar desirable features in a role model: Extrapolating from expectancy–value theory, we argue that the features of a role model that are key for boosting students’ expectancies for success in STEM and their STEM subjective value are the perceived competence of the role model, students’ perceived similarity to the model, and the perceived attainability of the model’s success. A role model’s ability to boost the subjective value of STEM may depend in particular on their psychological (vs. merely demographic) similarity to the student, which highlights STEM as a good match with students’ deeper selves/identities and with their long-term goals and aspirations.

Mindset theory

The term “mindsets” refers to students’ beliefs about the malleability (vs. fixedness) of ability in a domain (e.g., Dweck, 1999 , 2006 ; Dweck & Leggett, 1988 ). Specifically, a growth mindset is the belief that ability in a domain can be developed over time, whereas a fixed mindset is the belief that ability is fixed and stable over time. Growth mindsets put students in the “driver’s seat”—they give students a sense of control over their outcomes and promote the belief that success is attainable regardless of one’s current ability level, which in turn emboldens students to seek challenges and use setbacks as opportunities to learn (e.g., Blackwell et al., 2007 ; Cimpian et al., 2007 ; Mueller & Dweck, 1998 ; Porter et al., 2022 ; Yeager et al., 2019 ).

Students’ mindsets are shaped by messages from those around them, such as teachers and parents (e.g., Cimpian et al., 2007 ; Gunderson et al., 2013 ). Extrapolating from mindset theory, we argue that role models are motivating when they project the message that the abilities needed for success in STEM can be developed (rather than being fixed quantities that only some students possess), which should foster a growth mindset toward this domain. Because the upshot of these growth-oriented messages is to make success appear within reach for all students, we classify this feature of a role model under the broader category of perceived attainability. Although a role model’s STEM success may seem attainable for other reasons as well, the role model’s growth-oriented messages (e.g., that they have failed and learned from it) should be powerful motivators from the viewpoint of mindset theory.

Attribution theory

The predictions of mindset theory with respect to role models are aligned with those of another classic framework for understanding motivation: attribution theory (Graham, 2020 ; Weiner, 1985 ). According to attribution theory, student motivation is influenced by how students explain the causes of success and failure in a domain, with certain causal attributions being particularly likely to maintain or even increase motivation and others decreasing motivation instead (for a recent review, see Graham, 2020 ).

Concerning role models, the key question from the perspective of attribution theory is this: How do students explain the role model’s success in STEM? (Or, as the case may be, how does the role model explain their own success to students?) Some attributions might make the role model’s success seem more attainable than others and might thereby boost students’ own motivation to pursue this domain. Extrapolating from attribution theory, we argue that attributions to internal, unstable, and controllable factors (e.g., effort) are most likely to convey to students that they too can achieve success in STEM. This combination of causal features is necessary, we claim, for a role model to be effective from the viewpoint of attribution theory. If the role model’s success is instead portrayed as being due to, say, external factors (e.g., luck) or factors beyond the student’s control (e.g., innate ability), many students may justifiably doubt that they can duplicate the role model’s success, which would, in turn, undermine their motivation. Given that the recommendation we derived from mindset theory also promotes a focus on an internal, unstable, and controllable attribute (i.e., malleable STEM ability), the two theories are largely in agreement about what makes a role model effective. Footnote 3

Interim summary

Our analysis of four theories of motivation suggested several features of a role model that should be motivating to students. Specifically, a role model’s perceived competence, their perceived similarity to the student, and the seeming attainability of their STEM career should each increase students’ own motivation to pursue STEM. We used these features to structure our systematic review of the literature on role models, assessing the level of evidence for each feature.

Student moderators: Do student characteristics moderate the effect of role models?

In keeping with the present focus on role models as a tool for diversifying STEM, our systematic review first examined students’ gender and race/ethnicity as potential moderators of role models’ effectiveness; these two dimensions are particularly relevant to efforts to diversify STEM (e.g., Leslie, Cimpian, et al., 2015 ). Several patterns of results are possible. We might find, for example, that the features identified in the preceding section make a role model equally motivating for all students, regardless of their gender and race/ethnicity. In this case, role models could still serve as a tool for making STEM more diverse since they may bring underrepresented-group students closer to the threshold where they could envision a career in these fields. Alternatively, these features might be differentially effective in motivating underrepresented- and majority-group students, in part because the experiences of these groups in STEM contexts are different as well—for instance, students from underrepresented groups feel less welcome and supported in these contexts (e.g., Boston & Cimpian, 2018 ; Cheryan et al., 2015 ; Chestnut et al., 2018 ). Of course, it is also possible that exposure to role models could backfire, among students more generally or among underrepresented-group students specifically. The latter possibility is more concerning and perhaps also more likely, since students from underrepresented groups may more often perceive STEM role models as dissimilar to themselves and pursuit of these careers as beyond their reach.

In addition to gender and race/ethnicity, we also considered student age as a moderator of role models’ effects. Since STEM-related motivational variables undergo considerable change over the course of childhood and adolescence (e.g., Weidinger et al., 2017 ), as do students’ more general achievement-related beliefs and attitudes (e.g., Butler, 2005 ; Cimpian, 2017 ), it is important to examine the effectiveness of role models across a broad age range. Such an examination can potentially answer a range of fundamental questions about role models as a motivational tool: What is the earliest age at which role models have been documented to be effective? Does their overall effectiveness vary with student age? Do role model features vary in their importance with student age? If so, which features are most motivating for students of various ages?

The final student moderator on which our systematic review focused was students’ identification with STEM. Footnote 4 Briefly, students who identify with a domain see that domain as an important part of who they are, such that their sense of self-worth is to some extent linked with their outcomes in that domain (Boaler, 2002 ; Cobb et al., 2009 ; Martin, 2000 ; Steele, 1997 ). Whether or not a student has identified with STEM may matter because students with differing levels of STEM identification tend to have different concerns in the STEM classroom (Cheryan & Plaut, 2010 ; Easterbrook & Hadden, 2021 ; Steele, 1997 ). In particular, students who have not yet identified with STEM are typically less concerned with negative stereotypes about their ability and are more concerned with belonging than students who have already identified with STEM domains (Cheryan & Plaut, 2010 ; Drury et al., 2011 ; Steele, 1997 ). As a result, students who have not identified with STEM might be more effectively motivated by role models who foster a sense of belonging (that is, of being accepted and valued by others) in STEM, whereas students who have already identified with STEM might benefit most from exposure to role models who can decrease concerns about negative stereotypes, allowing them to maintain and strengthen their identification with STEM.

Distinctive theoretical and empirical contributions of the present research

Before describing our methods and findings, we differentiate the present systematic review from two recent articles that have also sought to summarize segments of the evidence on role models: a 2018 qualitative review by Olsson and Martiny and a 2019 meta-analysis by Lawner and colleagues. How does the present work build on these previous reviews to make a distinctive contribution?

First, Olsson and Martiny ( 2018 ) and Lawner et al. ( 2019 ) did not structure their reviews to investigate which specific features of a role model explain their effects on students’ motivation. Rather, these previous reviews focused on aggregate categories such as counterstereotypical role models (Olsson & Martiny, 2018 ) or ingroup role models (Lawner et al., 2019 ), which are undoubtedly important but may also miss systematic variability in the effectiveness of a role model. Here, we unpacked these categories into specific features or dimensions that can more precisely identify the reasons why role models are effective or ineffective. For instance, an ingroup role model may be either motivating or demotivating depending on the level of competence they project and whether their success seems attainable to the students.

Second, we extended what was previously reviewed to include a broader range of students and role models. Olsson and Martiny ( 2018 ) focused their review on the effects of role model exposure on girls and women, whereas we included a broader range of students. Lawner et al. ( 2019 ) focused on ingroup role models—that is, role models who share students’ group membership—whereas we included results obtained with outgroup role models as well. The breadth along these two dimensions (students and role models) was essential considering that our guiding question is, “Which role models are effective for which students?”.

Third, we anchored our review in the education research on motivation because this research has the potential to be informative about the psychological mechanisms that underlie role models’ effects. By situating our review in the motivation literature, we sought to provide a theoretical framework that can adequately explain when role models motivate (or fail to motivate) and maximize the relevance and usefulness of our review for the education research and practice communities.

A fourth and final distinctive feature of our review is that we distilled a summary of take-aways from the role model literature for use by researchers or practitioners who wish to implement role model interventions in their local contexts. Given the enduring enthusiasm about role models as a tool for diversifying STEM, it is important to extract digestible recommendations from this unwieldy literature. Formulating such recommendations, even if they are tentative, is preferable to the current state of affairs, in which most educators are forced to rely on their intuitions about what works when introducing their students to role models.

The present systematic review

To summarize, our goal here was to provide a systematic review of the literature on role models in STEM in order to answer the key question of which role models are effective for which students. The answer to this question is likely to provide an accessible tool for diversifying STEM. Our review of this literature was organized in terms of three role model moderators (the role model’s perceived competence, their similarity to the student, and the attainability of their STEM career) and four student moderators (gender, race/ethnicity, age, and identification with STEM). We then used this review to formulate practical recommendations for maximizing the benefits of exposing diverse students to STEM role models.

The choice of a systematic review, rather than a traditional narrative review or a meta-analysis, was motivated by two considerations. First, unlike traditional narrative reviews, systematic reviews are characterized by a “methodical, replicable, and transparent approach” (Siddaway et al., 2019 , p. 749). The methodical, comprehensive nature of systematic reviews lowers the likelihood of bias in selecting and summarizing research, and their transparently reported methodology (e.g., search terms, inclusion criteria) facilitates reproducibility by enabling other researchers to verify the authors’ conclusions. Second, a meta-analysis was not feasible here because our key research goal—namely, identifying which role models are effective for which students—led us to include studies whose methodologies (in terms of research design, outcome variables, etc.) were too diverse for quantitative summarization via meta-analysis.

Our methodology in conducting this systematic review followed the most recent guidelines provided in the literature (Moher et al., 2015 ; Siddaway et al., 2019 ), as we describe next.

Search process

Multiple databases were searched to obtain relevant research studies: PsycINFO, Psychology and Behavioral Sciences Collection (EBSCO), ERIC, and ProQuest Dissertations and Theses. The following search terms were used in this electronic database search: (“role model” or “role models”) AND (math* OR science OR “STEM”). These electronic databases were searched through August 13, 2019, and 1562 articles were generated from the electronic database search.

Because researchers use a variety of terms to refer to role models (e.g., Lawner et al., 2019 ), in a second step, we broadened our search to include other, related terms: (“role model” or “role models” OR ingroup mentor OR ingroup expert OR ingroup peer) AND (math* OR science OR “STEM”). Although our definition of role models explicitly differentiates them from mentors, not everyone follows this terminological distinction, so we included the term “mentor” in this broader list to identify relevant research that simply used different terminology. We found 14 additional articles through this broader search. Any of these articles that did not match our definition of a role model (e.g., because the STEM expert had a prior relationship with the students) were excluded during the screening process, as detailed below.

We additionally performed a search on Google Scholar using the two sets of search terms above to ensure we were not missing any articles that may not have come up in the other databases. We found 13 additional articles to include in the screening process through this search.

Screening process

To be included, studies had to (a) expose participants to a role model, (b) be conducted in a STEM-relevant domain (e.g., math), and (c) include STEM-specific outcome variables (e.g., performance, interest, sense of belonging). In addition, (d) the full text had to be available and in English. We did not make random assignment an inclusion criterion because this would have excluded a large number of field studies and program evaluations that did not randomly assign participants to conditions. However, following the definition of a role model articulated above, (e) we excluded any studies that examined parents, teachers, or other individuals with whom students were personally acquainted as role models.

We first screened the titles and abstracts of the articles according to these inclusion/exclusion criteria. However, due to the limited information provided in abstracts and titles, articles were excluded at this stage only if the given information was sufficient to clearly conclude that a criterion was not fulfilled (e.g., the research did not concern a STEM domain). This process narrowed down the list of potential articles to 394 records.

After eliminating duplicates ( n  = 37), a set of 357 articles were subjected to full-text screening. The first author and two trained research assistants went through these texts and determined whether they met the inclusion criteria (inter-rater agreement = 94%; disagreements were resolved via discussion). Articles were excluded because participants were not exposed to a role model ( n  = 208), because the studies were not conducted in a STEM domain ( n  = 10), because no STEM-specific student outcome was assessed ( n  = 31), because the full text was not available ( n  = 16), or because the STEM expert had a prior relationship with students ( n  = 37). After this last screening step, the first author and the research assistants agreed that 55 articles met the inclusion criteria (see Fig.  1 for screening flowchart). A complete list of the articles included is provided in Additional file 1 .

Flowchart of the steps in the screening process

Data extraction and synthesis process

For each of the moderators of interest, two research assistants (the same as those involved in inclusion/exclusion coding) and the first author examined the 55 articles in the final sample to extract relevant results. The articles relevant to a moderator were divided into those that pertained to a single level of the moderator (e.g., a high level of role model competence) vs. those that compared across levels of the moderator (e.g., high vs. low levels of role model competence). Studies that provide comparisons are more informative about the effects of a moderator and were thus privileged at the synthesis stage. The three researchers worked independently and then came together to arrive at a consensus for each moderator via discussion, in consultation with the second author. The first author then synthesized the output of this data extraction process into a narrative summary that the second author double-checked for accuracy against the original text of the articles and revised accordingly.

Additional methodological considerations

Implementing the data extraction and synthesis process above led us to omit from consideration an additional moderator that we had initially intended to examine: the type of outcome measured. Given the wide range of motivation-related outcomes measured in the literature on role models, especially relative to the somewhat modest size of this literature, it was not feasible to draw robust conclusions about the effects of specific role model features (e.g., competence) on specific outcome variables such as self-efficacy or interest. Footnote 5 Thus, we use the umbrella term “motivation” to synthesize the results of the articles in our target sample, except in cases where a more specific term was useful. Notably, we also included achievement outcomes (e.g., grades, test performance) under this umbrella. Although achievement and motivation are distinct constructs, motivational variables reliably influence achievement (e.g., Eccles & Wigfield, 2020 ; Schunk & Usher, 2019 ; Yeager et al., 2019 ), so it is likely that role model features that affect motivation will also affect achievement.

In addition, we made an a priori decision not to formally assess study quality as a moderator. Although still common, the practice of scoring study quality has come under increasing scrutiny because of a general lack of agreement on which elements of a study speak to its quality (e.g., Sanderson et al., 2007 ) and—more fundamentally—on what is even meant by “quality” (e.g., Valentine & Cooper, 2005 ), as well as because of evidence that existing tools for measuring study quality are not valid (e.g., Jüni et al., 1999 ). Instead of using a formal measure of study quality, we followed Siddaway et al.’s ( 2019 ) recommendation to adopt a presentation style that includes information about the sample and methods whenever possible, which enables readers to form their own conclusions about the quality of the evidence being synthesized.

Results and discussion

We organize our synthesis of the findings into two sections. First, we discuss evidence pertinent to role model moderators—that is, features of role models that might increase vs. decrease students’ STEM motivation—focusing in particular on the three features identified through our analysis of prominent theories of motivation: role models’ competence, their similarity to the students, and the attainability of their STEM career. Second, we discuss evidence pertinent to student moderators, focusing in particular on students’ gender, race/ethnicity, age, and their identification with STEM. Table 1 summarizes the evidence described in more detail below.

Throughout, our discussion of each moderator is often limited to evidence on its “main effects” (e.g., does the perceived competence of the role model make a difference?). We acknowledge, however, that these moderators likely interact with each other in complex ways. We were occasionally able to incorporate this interactive complexity into our synthesis of the literature, especially when relating role model moderators to student moderators—an important part of answering the question of which role models are effective for which students. Nevertheless, there are many other interactive relationships among these moderators that we do not discuss because they have not yet been investigated in the literature. Thus, the present summary of the effects of role models as tools for diversifying STEM is by necessity (over)simplified.

Role model moderators

Most articles in our sample presented students with a successful, competent STEM role model—unsurprisingly, since being successful is a key reason why someone would be considered a role model. For our purposes, however, the most informative studies are those in which role models of varying competence levels are presented; without variability in this feature, we cannot assess its relationship to student motivation. The relevant studies suggest that this relationship likely has an inverted-U shape: Describing the role model as competent is likely to increase student motivation and performance—especially if other features are in place as well, such as a certain degree of similarity between the student and role model—but only up to a point. When a role model’s competence is described as exceptional, exposure to the role model can backfire and demotivate (see also Lockwood & Kunda, 1997 ).

Illustrating the positive slope of the inverted-U function, several studies suggested that exposure to an ingroup role model who is portrayed as competent (vs. of ambiguous competence) has beneficial effects on students’ motivation and performance (Buunk et al., 2007 ; Marx & Roman, 2002 ; Marx et al., 2013 ; McIntyre et al., 2005 , 2011 ), particularly when the role model’s background is also similar to students’ own backgrounds (e.g., grew up in the same place, enjoyed the same types of activities; Marx & Ko, 2012 ).

Evidence for the negative slope of the inverted-U (at more extreme levels of role model competence) is relatively sparse but suggestive. Woodcock ( 2012 ) found that exposure to female STEM role models described as “exemplary students with exceptional grades” (p. 21) decreased identification with math (measured implicitly, but not explicitly; Study 2) and math performance (Study 3) among female undergraduates compared to exposure to a role model of average competence. Similarly, Ziegler and Stoeger ( 2008 ) found that exposure to a female role model who was “extremely” competent (p. 512) decreased confidence and interest in STEM among girls with low (but not high) baseline levels of interest in STEM compared to exposure to a role model that was “discernibly untalented” (p. 509).

We caution that our claim of an inverted-U-shaped relationship between role model competence and student motivation in STEM is extrapolated from a (small) set of studies that were quite different in terms of their design, dependent variables, etc. This claim should thus be viewed as tentative until further empirical tests are performed. It is also important to keep in mind that most of the studies reviewed in this section focused on the effects of role models’ competence on ingroup students (e.g., female students exposed to a female role model). The few that also examined outgroup students (e.g., male students exposed to a female role model) suggest a different pattern of results—either no consistent effects of the outgroup role model (e.g., Ziegler & Stoeger, 2008 ; male participants in Study 1 of Marx and Roman ( 2002 )) or a reverse pattern whereby performance is facilitated at low levels of role model competence (e.g., Study 2 of Marx and Roman ( 2002 )). These results serve as a reminder that the features of a role model often act in tandem to influence student motivation, with the effects of one feature (e.g., competence) sometimes varying as a function of the values of another (e.g., similarity).

Similarity to students

We now synthesize the results pertaining to whether the STEM role model’s similarity to students is motivating. Two distinct types of similarity between role models and students emerged in our review (e.g., Eby et al., 2013 ; Hernandez et al., 2017b ). The first is demographic similarity. A role model is demographically similar to a student if the two share a social identity—most important for our purposes here, gender or race/ethnicity. The second type of similarity is psychological. A role model is psychologically similar to a student to the extent that the two share certain preferences, values, struggles, etc., regardless of whether they also share any social identities. While the two types of similarity are correlated, they also have distinct relationships with student motivation. We discuss each in turn.

Demographic similarity. Anecdotally, it is often assumed that a role model must be demographically similar to students in order to motivate them. For instance, the slogan “you can’t be what you can’t see” presupposes a match of (visible) social identities between the role model and the student. While our review is operating with a broader notion of role models, the literature nevertheless reflects this narrower view: Many articles in our sample simply matched participants and role models in their demographics—specifically, gender and/or race/ethnicity (Bamberger, 2014 ; Betz & Sekaquaptewa, 2012 ; Davis, 2001 ; Dubetz & Wilson, 2013 ; Ferreira, 2001 ; Fox, 1976 ; Gilbert, 2015 ; Goldberg & Sedlacek, 1995 ; Hammrich et al., 1999 ; Herrmann et al., 2016 ; Holmes et al., 2012 ; Johnson, 1989 ; Kant et al., 2018 ; Liu et al., 2014 ; Mbano & Nolan, 2017 ; Murray et al., 2009 ; O’Brien et al., 2017 ; Phelan et al., 2017 ; Rosenthal et al., 2013 ; Schriver et al., 1995 ; Scott, 2013 ; Swindell & Phelps, 1991 ). Most of these studies used versions of a pre- vs. post-test design to assess the effects of exposure to demographically similar role models on students’ motivation for STEM, finding a preponderance of positive results on outcomes such as confidence, interest, identity, course-taking intentions, and achievement (for exceptions, see Bamberger, 2014 ; Betz & Sekaquaptewa, 2012 ; Holmes et al., 2012 ).

However, as in the preceding section, in the absence of a comparison (demographically similar vs. dissimilar role model), it is difficult to attribute these positive effects to the similarity between the role model and the students. Thus, we next looked to the subset of studies in our sample that varied the demographic match between role models and students and that also reported the results separately for the students who matched vs. mismatched the role models’ demographics. Footnote 6

This set of studies revealed an interesting asymmetry in the breadth of role models’ positive effects depending on their demographics: When the role models belonged to groups that are underrepresented in STEM (such as women or Black people), they often had broad positive effects on students, regardless of demographic match vs. mismatch between the role model and the students (Bagès & Martinot, 2011 ; Conner & Danielson, 2016 ; Evans et al., 1995 ; Plant et al., 2009 ; Smith & Erb, 1986 ; Ziegler & Stoeger, 2008 ; for an exception, see Hoffman & Kurtz-Costes, 2019 ). For instance, Evans et al. ( 1995 ) found that exposure to female role models improved attitudes toward science among girls and (to a lesser extent) boys in a sample of 964 ninth-grade students. Conner and Danielson ( 2016 ) reported similar findings in a sample of 231 students in grades 2–6, except that some of their effects were actually stronger for boys than for girls. In a smaller number of studies, underrepresented-group STEM role models had a positive effect on ingroup students, but no effect on the outgroup (i.e., majority-group) students (McIntyre et al., 2005 ; Stout et al., 2011 )—or the models’ effects on majority-group students were not measured (Johnson et al., 2019 ; Shapiro et al., 2013 ). Notably, a “bonus feature” of underrepresented-group STEM role models is that—beyond their effects on students’ own motivation—they also seem to reduce students’ stereotypes against marginalized minorities in STEM (Plant et al., 2009 ; Smith & Erb, 1986 ).

Although STEM role models from underrepresented groups often motivate majority-group students, the converse is not true: In many of the studies that reported the relevant comparisons, majority-group STEM role models (e.g., men, White people) did not motivate students from underrepresented groups (Dennehy & Dasgupta, 2017 ; Shapiro et al., 2013 ; Stout et al., 2011 ) and were sometimes demotivating (Marx & Roman, 2002 ; Stout et al., 2011 ). Exposure to majority-group role models had broader positive effects only in a restricted range of circumstances: In particular, these broader effects emerged when the majority-group models displayed additional features or behaviors that increased their similarity and appeal to students—for instance, when they were young, attractive, and “cool” (Plant et al., 2009 ), when they were friendly toward students (e.g., nodding, smiling; Krämer et al., 2016 ), when their success was explained as a function of malleable factors such as their efforts (Bagès et al., 2016 ), or when they contradicted the stereotype of STEM professionals as “nerdy loners” (e.g., the models reported liking to play sports rather than video games; Cheryan et al., 2011 , 2013 ). We return to this issue in the section on psychological similarity.

It may be that majority-group STEM role models are less-than-effective motivators for marginalized students because they highlight the stereotypes that make this domain unwelcoming for students like them (e.g., that STEM is a masculine domain). In contrast, underrepresented-group STEM role models may not activate similar threats in majority-group students, who are generally unlikely to question their place in STEM and might therefore see the role model simply as an inspiring adult whose success they can try to emulate.

This asymmetry in the role of demographic similarity, whereby role models belonging to groups that are traditionally underrepresented in STEM are more broadly motivating (regardless of their demographic similarity to students) than majority-group models, has important practical implications: Because most classrooms consist of students with a mix of social identities and backgrounds, one might reasonably worry that exposing an entire classroom to a role model (e.g., a female scientist) might demotivate the subset of students who do not share that role model’s social identities (e.g., the boys). In light of this research, it seems that exposure to role models from underrepresented groups avoids this potential issue and is likely to result in a net motivation gain on average while also not being discouraging to any subsets of students.

Psychological similarity. Students can feel similar to a STEM role model—and thus be inspired by the role model’s success—not just because they are part of the same group but also because they perceive the role model to be “like them” at a deeper, psychological level. Our analysis of the articles in our sample revealed two distinct strategies for leveraging psychological similarity to increase the effectiveness of role models. The first strategy targets the role models, highlighting a subset of their psychological characteristics that students may be likely to identify as similar to their own. The second strategy targets the students, asking them to reflect on the role models’ characteristics and identify similarities to themselves. We discuss each of these strategies in turn. We should also note that, as before, only some of the articles included a comparison between the effects of role models in the presence vs. absence of strategies to leverage psychological similarity; we mark these articles, which are particularly informative, with an asterisk in the two subsections that follow.

Strategy targeting the role models. Our review revealed that the role model characteristics that were highlighted for students tended to fall at the intersection of two categories. First, the highlighted role model characteristics tended to be ones that most people share (e.g., a preference for spending time with close others rather than alone). Highlighting this category of broadly shared characteristics is potentially beneficial because it maximizes the probability that students will perceive the role models as similar to themselves from a psychological standpoint. Second, the role model characteristics that were highlighted for students tended to be ones that contradicted common stereotypes of scientists (e.g., the stereotype that they prefer solitude and are innately brilliant; Boston & Cimpian, 2018 ; Cheryan et al., 2015 ). Highlighting this category of characteristics is potentially beneficial because it corrects students’ mistaken assumptions about scientists, portraying scientists as more psychologically similar to the “average person” than students might have otherwise assumed them to be.

Specifically, the studies in our sample highlighted two role model characteristics that fell at the intersection of these categories, both of which had positive effects on student motivation: (1) STEM role models’ preference to be around and help other people (Cheryan et al., 2011 *,  2013 *; Marx & Ko, 2012 *; Plant et al., 2009 ; Tan-Wilson & Stamp, 2015 ), a preference that is common in the general population and stereotypically assumed to be rare among scientists, and (2) STEM role models’ history of working hard and persisting in the face of failure (Bagès & Martinot, 2011 *; Bagès et al., 2016 *; Hernandez et al., 2017a ; Herrmann et al., 2016 ; Hong & Lin-Siegler, 2012 *; Lin-Siegler et al., 2016 *; Shin et al., 2016 ), which—again—is common in students’ lives and stereotypically assumed to be rare in scientists’ lives (given their supposed brilliance).

A couple of caveats are in order here. First, Lawner ( 2014 *) is an exception to the pattern that psychological similarity to a role model boosts STEM motivation; Lawner’s experimental studies found no effect of messages that STEM role models work with and help others. Second, some of these studies included other cues that could have increased role models’ psychological similarity to the students, beyond their other-oriented preferences and the hard work they put in to succeed. These cues included hobbies and other leisure preferences (e.g., reading Rolling Stone magazine rather than Electronic Gaming Monthly ; Cheryan et al., 2011 *, 2013 *) and aspects of the role models’ personal histories intended to match the students’ own (e.g., growing up in the same region; Marx & Ko, 2012 *). Thus, while we are confident in the conclusion that psychological similarity is a reliable moderator of role models’ effects, the specific (combinations of) features that are sufficient to induce this sense of similarity is a matter that deserves further investigation.

Strategy targeting the students. The goal of increasing the perceived psychological similarity between role models and students can be achieved not only by highlighting select characteristics of the role model (as described in the preceding section) but also by prompting the students to reflect on the ways in which they are similar to the role models. Indeed, the experimental studies that adopted versions of this strategy generally found positive effects on students’ STEM attitudes (Gilbert, 2015 ; O’Brien et al., 2017 *; Van Camp et al., 2019 *; for an exception, see Hoffman & Kurtz-Costes, 2019 ). Arguably, role model- and student-focused strategies could be combined as well, which might further enhance their effectiveness in promoting STEM motivation.

Attainable success

Is perceiving a role model’s success as attainable (i.e., within reach) motivating for students? Because the features that determine a role model’s motivational effects are interrelated, several patterns of results described in previous sections are relevant to understanding the role of attainability as well. First, the inverted-U-shaped relationship between a role model’s competence and their ability to motivate students is relevant here because extreme levels of competence are also less attainable. Second, the findings concerning the importance of the perceived similarity (demographic and psychological) between role models and students are relevant here because the career of a role model who seems similar to the self may thereby also appear more attainable. Thus, these two sets of findings can be reasonably interpreted as providing evidence for the claim that the attainability of a role model’s success is positively related to student motivation.

A distinct dimension of attainability identified in our review pertains to role models’ ability to fill in informational gaps about the wide range of STEM careers that exist, the specific responsibilities that come with such jobs, and the concrete steps that a student might take to pursue them. Studies in which role models provided such information, thus making the possibility of a STEM career less hazy and more attainable, seemed to generally benefit students’ motivation to pursue STEM (Hughes et al., 2013 ; Johnson, 1989 ; Kant et al., 2018 ; Mbano & Nolan, 2017 ; Mills & Katzman, 2015 ; Newbill, 2005 ; Phelan et al., 2017 ; Wyss et al., 2012 ).

Finally, we also note that, in the articles in our sample, every instance in which exposure to a role model backfired—lowering STEM motivation (vs. increasing it or having no effect)—was linked in some way to the perceived unattainability of the role model’s career. There are many reasons why role models’ success can appear to students to be too difficult to match. In addition to the examples already discussed that involved extreme competence (Woodcock, 2012 ; Ziegler & Stoeger, 2008 ), Bamberger ( 2014 ) reported that their sample of Israeli ninth-grade girls were “frightened” (p. 557) by their interactions with female scientists and engineers from a nearby technology company, perhaps in part because the role models used complex scientific concepts that the students were unfamiliar with or because they described some of the challenges involved in working in a male-dominated environment (which may have made their jobs seem undesirable in addition to unattainable). In contrast, the backfire effect reported by Betz and Sekaquaptewa ( 2012 ) in their sample of US middle-school girls had a different source: Female STEM role models were demotivating when they also displayed stereotypically feminine features (e.g., wearing pink clothes and makeup). Footnote 7 The success of a feminine-presenting woman in a domain that is both male-dominated and (stereotypically) unfeminine may have seemed too difficult to emulate. Considering the elevated risk that accompanies this role model moderator, it seems important to carefully calibrate the extent to which a role model’s success will appear attainable to students in any future interventions, regardless of scale.

Student moderators

We now discuss whether role models’ effectiveness varies as a function of four student characteristics. We first focus on students’ (1) gender and (2) racial/ethnic background as potential moderators of role models’ effects, in part because these demographic dimensions are particularly relevant to the goal of making STEM more diverse. Next, we address the question of whether role models may be differentially effective for (3) students of different ages and for (4) students who have vs. have not identified with STEM as a domain.

Gender and race/ethnicity

Our ability to assess the moderating effects of student gender and race/ethnicity was constrained by the fact that 69% of studies in our sample included as participants only members of groups that are underrepresented in STEM—in particular, most of these studies included only girls or women, and the rest included only Black and/or Latinx students. Although this is a reasonable design decision considering that a key goal of this literature is to understand how to increase the representation of these groups in STEM, it does make it more difficult to discern whether, and in what ways, role models are differentially effective depending on students’ demographics.

Even so, aggregating across the subset of papers that reported separate results by gender and race/ethnicity, an important conclusion emerges—the flip side of the earlier conclusion about the breadth of role models’ effects as a function of their demographics: The range of role models that are motivating for students from groups that are underrepresented in STEM is narrower than that for majority-group students, and the probability of backfire effects is also greater among underrepresented-group students.

This difference can be seen most clearly when examining the effects of outgroup role models. While outgroup role models are usually motivating for majority-group students (e.g., Bagès & Martinot, 2011 ; Conner & Danielson, 2016 ), their effects on underrepresented-group students are much more mixed (Johnson et al., 2019 ; Marx & Roman, 2002 ; Shapiro et al., 2013 ; Stout et al., 2011 ). This is not to say that outgroup models cannot motivate students from underrepresented groups—the bar is simply higher, in that outgroup role models need to display some additional features that increase their relatability (e.g., Bagès et al., 2016 ; Cheryan et al., 2011 , 2013 ; Krämer et al., 2016 ). We also are not claiming that ingroup role models are always motivating for students from underrepresented groups. In fact, in the preceding section, we discussed several striking instances where exposure to female role models demotivated girls (e.g., Bamberger, 2014 ; Betz & Sekaquaptewa, 2012 ; see also Hoffman & Kurtz-Costes, 2019 ). Note, however, that no studies in our sample reported that exposure to an ingroup model demotivated majority-group students. Footnote 8 Thus, these additional datapoints are consistent with the claim that the set of circumstances under which role models are motivating for underrepresented-group students is smaller (and, conversely, that the set of circumstances under which role models are demotivating for these students is larger). This pattern of findings is not unexpected considering the reality of belonging to a group that is underrepresented and stigmatized in STEM contexts: For these students, the range of day-to-day STEM environments where they feel psychologically “safe” is narrower (e.g., Schmader & Hall, 2014 ), and the findings of our systematic review likely reflect this simple but powerful fact.

Among the studies in our sample, 9% focused on elementary school students, 25% focused on middle school students, 24% focused on high school students, and 42% focused on college students. Thus, the literature is skewed toward older students, and studies on elementary school students are relatively rare.

There was no noticeable difference in the extent to which role models were effective as a function of student age. Role models were as likely to motivate elementary school students as they were to motivate undergraduates. This conclusion is consistent with prior findings, in that Lawner and colleagues’ ( 2019 ) meta-analysis did not find evidence of moderation by student age either.

However, the conclusion that age is not a moderator of role models’ effects is also qualified by several limitations of the evidence. Most critically, almost all studies included only a narrow age range and thus could not compare the effectiveness of role models for students of different ages. Only two studies reported age/grade comparisons (Conner & Danielson, 2016 ; Wyss et al., 2012 ), and neither found age differences. Although these two null effects are consistent with our overall conclusion of no moderation by age, the possibility remains that this conclusion is confounded by methodological differences between studies with younger vs. older students. For example, as many as 80% of the studies with elementary school students exposed students to the role models “live” (i.e., in person), whereas only 25% of the studies with college students did so. In addition, more of the studies with younger children were broader interventions to boost STEM motivation that included elements other than role models (e.g., hands-on activities). In contrast, more of the studies on college studies were lab-based experiments that manipulated only aspects of students’ exposure to role models. It is also noteworthy that role models were generally closer in age (i.e., more demographically similar) to the participants in studies with college students (which always used other adults as role models) than in studies with younger students (which also used mostly adults as role models).

In summary, the data provided qualified support for the conclusion that student age does not moderate the effectiveness of STEM role models as motivational tools. However, our review also revealed gaps in the evidence needed to establish this conclusion with a high degree of confidence. In particular, more work is needed that conducts closely matched, within-study comparisons of the effects of role models on students of different ages.

Identification with STEM

As a reminder, the rationale for looking at identification with STEM as a moderator of the effects of role models is that students with differing levels of identification with STEM have different concerns in the classroom and may therefore be looking to role models to fulfill different psychological needs. According to prominent arguments (e.g., Drury et al., 2011 ), a salient question for students who are not (yet) identified with STEM is whether they fit in with people in this field (e.g., Bian et al., 2018 ; Cheryan & Plaut, 2010 ). As a result, role models should be able to boost motivation to pursue STEM among these students to the extent that they lead students to anticipate fitting in and belonging. In principle, a broad range of role models should be able to convey this sense of belonging in STEM, not just ones that are demographically similar to the students (i.e., from the same social group) but also ones that are only similar at a psychological level, in the sense of sharing students’ own goals, preferences, values, etc. In contrast, once a student has identified with STEM and decided to pursue a career in this domain, the question that rises to the fore is whether they will be able to succeed. For students from groups that are underrepresented in STEM, the negative stereotypes that target their groups are a particular source of concern, so these students may derive unique benefits from exposure to demographically similar role models, who have succeeded in spite of these stereotypes. To summarize, this argument predicts that a general sense of similarity to people in STEM, regardless of whether this similarity is demographic or psychological, might be sufficient to attract students to STEM in the first place, whereas at subsequent stages, demographic similarity to successful role models might take on particular importance—especially for students from underrepresented groups, who have to contend with negative perceptions about their groups.

A direct test of this argument would involve exposing students who are high vs. low in STEM identification to role models who are high vs. low in demographic similarity to the students and, orthogonally, high vs. low in psychological similarity to the students. As far as we know, this study has not been conducted—at the very least, it was not in our analytic sample. However, some of the studies examined here provide partial support for this prediction. For instance, several studies on non-STEM-identified students suggested that, for these students, psychological similarity to a role model was at least as motivating as demographic similarity (Bagès & Martinot, 2011 ; Bagès et al., 2016 ; Cheryan et al., 2011 , 2013 ; Hong & Lin-Siegler, 2012 ), consistent with the argument that providing a general sense of fitting in is often sufficient at the recruitment stage. Also consistent with this argument, studies that focused on STEM-identified students highlighted the benefits of exposure to demographically similar role models (Marx & Roman, 2002 ; Stout et al., 2011 ).

On the other hand, we did not find any evidence that demographic similarity is particularly important for STEM-identified students, largely because the relevant comparisons were absent in our sample (and likely in the literature more generally). Somewhat relevant, one study that included STEM-identified students suggested that psychological similarity continues to be beneficial, above and beyond demographic similarity, even at this later stage (Marx & Ko, 2012 ). However, in this study, students’ STEM identification was analyzed as a covariate rather than a moderator, so the finding reported technically pertains to students with average (rather than high) levels of STEM identification. The take-away here is that, despite a few promising hints, much more research is needed before we can confidently describe how students’ identification with STEM moderates the effects of exposure to role models.

How do we maximize the motivating effects of STEM role models? Four tentative recommendations

Based on our synthesis of the evidence (see Table 1 for a summary), we formulated a set of concrete recommendations that the education research and practice community can consider when exposing students from diverse backgrounds to role models as a means of boosting their STEM motivation. Certainly, the evidence base for these recommendations could be stronger; as we have pointed out throughout, many questions still await systematic investigation. However, given that the enthusiasm for role model interventions among educators and the general public continues to run ahead of the research, the benefits of formulating a set of recommendations on the evidence so far, limited as it is, outweigh the potential drawbacks. To enable readers to share these recommendations with others, we have created an infographic (see Fig.  2 ) that we have also made available in several high-resolution formats via Figshare: https://doi.org/10.6084/m9.figshare.c.5681674.v1 .

Four recommendations to consider when exposing students to role models as a means of boosting their STEM motivation. High-resolution versions of this infographic in .eps, .jpg, .pdf, and .png formats are freely available at https://doi.org/10.6084/m9.figshare.c.5681674.v1

First, we recommend that role models are portrayed as being competent and successful. The preponderance of the evidence reviewed suggested that these features are motivating in a role model. However, portraying extreme levels of role model competence or success can backfire, demotivating students. Although it may be tempting to make the role model seem like a super(wo)man in terms of what they have achieved, more is not always better in this respect because students may conclude “I could never do all that” and look elsewhere for a career.

Second, we recommend that role models are portrayed as being meaningfully similar to the students. Although the fact that a role model belongs to the same social group as they do may in and of itself be motivating to some students, the role model’s motivational effects can be broadened by highlighting other ways in which the role model is similar to students (e.g., the model worked hard for their success rather than being effortlessly brilliant; the model likes to do “regular person” things in their spare time). Asking students to reflect on similarities to the role model might help as well.

Third, we recommend prioritizing exposure to role models who belong to groups that are traditionally underrepresented in STEM, especially in cases where only a small number of role models can be presented. Role models from underrepresented groups are likely to have the broadest positive effects on students, regardless of students’ own social identities. To avoid imposing an additional burden on scientists from underrepresented groups, they could be introduced to students via videos or printed materials (rather than live) because these materials can be presented to large numbers of students without any additional effort on the role model’s part and are at least as effective as live interactions (Lawner et al., 2019 ).

Fourth, we recommend that role models’ success is portrayed as attainable. To the fullest extent possible, information should accompany the exposure to the role model that makes it clear how the students could also achieve what the role model has achieved. Perhaps the greatest risk of demotivating students arises when a role model’s career success seems unattainable to students.

Limitations and future directions

The conclusions of the present systematic review are somewhat tentative, in part because the evidential base is still limited. This literature may also suffer from publication bias. For example, Lawner and colleagues’ ( 2019 ) meta-analysis found that lab studies from this literature that had small sample sizes produced larger effects than expected, suggesting a file-drawer problem. In addition, we caution that some of the studies in our target sample (especially those conducted outside the lab) included enrichment activities beyond exposure to role models. Discerning the separate benefits of role models vs. these other activities is impossible when interpreting the results of these studies.

Another limitation of the present systematic review is that we considered motivation- and achievement-related outcomes together rather than separately. The decision to do so was motivated both by practical considerations—the sparsity of the research on each moderator meant that we could not feasibly analyze the data separately by outcome—and by theoretical considerations, insofar as motivation is a causal antecedent of achievement (e.g., Eccles & Wigfield, 2020 ; Schunk & Usher, 2019 ; Yeager et al., 2019 ). In addition, Lawner and colleagues’ ( 2019 ) meta-analysis found that interest (a motivational variable) and performance were affected to similar degrees by exposure to role models, which suggests that combining these outcomes is defensible. However, motivation is not the only antecedent of achievement, so it is possible that certain role model or student moderators affect motivation and performance differently. Future research on this topic would be valuable.

Relatively few of the studies we identified through our search focused on students from racial/ethnic minority groups that are underrepresented in STEM. None focused on students with disabilities or on students who identify as lesbian, gay, bisexual, transgender, or queer (LGBTQ), groups that are also underrepresented in STEM careers (e.g., Freeman, 2018 ; National Science Foundation, 2017 ). Broadening the scope of research in this literature to include overlooked social identities would be beneficial. Greater attention to the question of effective role models for students who embody multiple identities that are underrepresented in STEM is needed as well. Only one of the 55 articles in our sample focused on this important issue (Johnson et al., 2019 ).

At a more conceptual level, we caution that focusing on role models as a solution for increasing diversity in STEM risks sending the message that diversifying STEM is simply a matter of motivating students to pursue it. The problem with this message is that it overlooks the systemic biases present in STEM contexts (e.g., racism, sexism, heterosexism, ableism), which make it difficult for some students to succeed regardless of how motivated or capable they are. Although it may be intuitively appealing and cost-effective to intervene by exposing students to role models, this strategy can only succeed if it is part of a broader set of measures to make STEM more welcoming to all students who might choose it as a career path.

The idea of inspiring students—particularly female and racial/ethnic minority students—to pursue STEM by exposing them to role models is ever popular. Here, we systematically reviewed the literature on this topic to identify strategies for maximizing the motivational impact of role models: Which role models are effective for which students? Our findings, which we distilled into four simple take-aways, provide a bird’s-eye view of the literature for researchers and a practical guide for any educators and policymakers who might want to implement role-model interventions in their local contexts. Our review also identified open questions about why and for whom role models are motivating, questions that we hope will guide future research on this topic.

Availability of data and materials

This is a systematic review of prior literature. All articles we reviewed here are either openly available via Google Scholar or accessible through a library subscription. A complete list of the papers included in the systematic review is provided in the supplementary online materials. The infographic summarizing our recommendations for maximizing the effectiveness of role models in STEM (see Fig.  2 ) is available at https://doi.org/10.6084/m9.figshare.c.5681674.v1 .

What matters for student motivation is not whether a role model objectively possesses a certain feature such as competence but rather whether students subjectively perceive the model to possess that feature. Thus, our use of the term “role model features” in this section (and throughout the paper) is intended to stand in for “perceived role model features.”.

This argument pertains to attainment and utility value most directly (Eccles and Wigfield, 2020 ). The term “attainment value” refers to perceived importance of an activity that is somehow tied to a student’s sense of self and to their identity (e.g., a student who has identified with STEM is likely to place high attainment value on this domain). “Utility value” refers to the perceived usefulness of a particular activity for a student’s goals, regardless of whether these goals reflect the student’s deeper self/identity (e.g., a student who wants a job in STEM because STEM careers are lucrative is likely to place high utility value on taking advanced math courses).

However, mindset theorists and attributional theorists disagree in how they conceptualize ability per se (e.g., Graham, 2020 ): Attributional theorists typically view ability as stable and uncontrollable (akin to aptitude), whereas mindset theory is premised on the existence of multiple perspectives on ability (as stable and uncontrollable [fixed mindset] vs. unstable and controllable [growth mindset]).

This variable is correlated with age: Students who have identified with STEM are likely to be older on average than students who have not. However, STEM identification is also meaningfully distinct from age, not just statistically (in that one can find STEM-identified and non-STEM-identified students in any age group) but also conceptually (in that STEM identification is a psychological, attitudinal variable, whereas age is a demographic one).

We also note that previous attempts to draw such distinctions between outcomes did not find that outcomes were differentially sensitive to role model exposure (Lawner et al., 2019 ).

Of course, students can match the role model’s demographics on one dimension (e.g., gender) and mismatch them on another dimension (e.g., race). With a single exception (Johnson et al., 2019 ), no studies in our sample explicitly examined the effects of a match vs. mismatch along multiple social identities. Johnson and colleagues found that Black women students who were exposed to a Black woman or a Black man as a role model had greater feelings of belonging in STEM compared to Black women students who saw either a White woman or White man as a role model; there was no difference between the two White role models. In this instance, it appears that a demographic match along the race/ethnicity dimension mattered more for Black women students’ motivation than a match along the gender dimension. We return to the issue of how few studies examine multiple social identities in the later section on limitations.

This effect was only present among girls who did not identify with STEM.

It is important to keep in mind, however, that fewer studies included majority-group students in the first place.

Abbreviations

Lesbian, gay, bisexual, transgender, or queer

Science, technology, engineering, and mathematics

Bagès, C., & Martinot, D. (2011). What is the best model for girls and boys faced with a standardized mathematics evaluation situation: A hard-working role model or a gifted role model? British Journal of Social Psychology, 50 , 536–543. https://doi.org/10.1111/j.2044-8309.2010.02017.x

Article   Google Scholar  

Bagès, C., Verniers, C., & Martinot, D. (2016). Virtues of a hardworking role model to improve girls’ mathematics performance. Psychology of Women Quarterly, 40 , 55–64. https://doi.org/10.1177/0361684315608842

Bamberger, Y. M. (2014). Encouraging girls into science and technology with feminine role model: Does this work? Journal of Science Education and Technology , 23 , 549–561. https://www.jstor.org/stable/24019742

Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84 , 191–215. https://doi.org/10.1037/0033-295X.84.2.191

Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory . Prentice Hall.

Google Scholar  

Bandura, A. (1997). Self-efficacy: The exercise of control . W. H. Freeman.

Bandura, A. (2001). Social cognitive theory: An agentic perspective. Annual Review of Psychology, 52 (1), 1–26. https://doi.org/10.1146/annurev.psych.52.1.1

Bandura, A., & Walters, R. H. (1963). Social learning and personality development . Holt Rinehart and Winston.

Betz, D. E., & Sekaquaptewa, D. (2012). My fair physicist? Feminine math and science role models demotivate young girls. Social Psychological and Personality Science, 3 , 738–746. https://doi.org/10.1177/1948550612440735

Bian, L., Leslie, S. J., Murphy, M. C., & Cimpian, A. (2018). Messages about brilliance undermine women’s interest in educational and professional opportunities. Journal of Experimental Social Psychology, 76 , 404–420. https://doi.org/10.1016/j.jesp.2017.11.006

Blackwell, L. S., Trzesniewski, K. H., & Dweck, C. S. (2007). Implicit theories of intelligence predict achievement across an adolescent transition: A longitudinal study and an intervention. Child Development, 78 (1), 246–263. https://doi.org/10.1111/j.1467-8624.2007.00995.x

Boaler, J. (2002). The development of disciplinary relationships: Knowledge, practice and identity in mathematics classrooms. For the Learning of Mathematics , 22 , 42–47. https://www.jstor.org/stable/40248383

Boston, J. S., & Cimpian, A. (2018). How do we encourage gifted girls to pursue and succeed in science and engineering? Gifted Child Today, 41 (4), 196–207. https://doi.org/10.1177/1076217518786955

Butler, R. (2005). Competence assessment, competence, and motivation between early and middle childhood. In A. J. Elliott & C. S. Dweck (Eds.), Handbook of competence and motivation (pp. 202–221). Guilford Press.

Buunk, A. P., Peiró, J. M., & Griffioen, C. (2007). A positive role model may stimulate career-oriented behavior. Journal of Applied Social Psychology, 37 , 1489–1500. https://doi.org/10.1111/j.1559-1816.2007.00223.x

Cheryan, S., Drury, B. J., & Vichayapai, M. (2013). Enduring influence of stereotypical computer science role models on women’s academic aspirations. Psychology of Women Quarterly, 37 (1), 72–79. https://doi.org/10.1177/0361684312459328

Cheryan, S., Master, A., & Meltzoff, A. N. (2015). Cultural stereotypes as gatekeepers: Increasing girls’ interest in computer science and engineering by diversifying stereotypes. Frontiers in Psychology, 6 , 49. https://doi.org/10.3389/fpsyg.2015.00049

Cheryan, S., & Plaut, V. C. (2010). Explaining underrepresentation: A theory of precluded interest. Sex Roles, 63 , 475–488. https://doi.org/10.1007/s11199-010-9835-x

Cheryan, S., Siy, J. O., Vichayapai, M., Drury, B. J., & Kim, S. (2011). Do female and male role models who embody stem stereotypes hinder women’s anticipated success in stem? Social Psychological and Personality Science, 2 , 656–664. https://doi.org/10.1177/1948550611405218

Chestnut, E. K., Lei, R. F., Leslie, S. J., & Cimpian, A. (2018). The myth that only brilliant people are good at math and its implications for diversity. Education Sciences, 8 (2), 65. https://doi.org/10.3390/educsci8020065

Cimpian, A. (2017). Early reasoning about competence is not irrationally optimistic, nor does it stem from inadequate cognitive representations. In A. J. Elliot, C. S. Dweck, & D. S. Yeager (Eds.), Handbook of competence and motivation: Theory and application (pp. 387–407). Guilford Press.

Cimpian, A., Arce, H. M. C., Markman, E. M., & Dweck, C. S. (2007). Subtle linguistic cues affect children’s motivation. Psychological Science, 18 (4), 314–316. https://doi.org/10.1111/j.1467-9280.2007.01896.x

Cobb, P., Gresalfi, M., & Hodge, L. L. (2009). An interpretive scheme for analyzing the identities that students develop in mathematics classrooms. Journal for Research in Mathematics Education , 40, 40–68. https://www.jstor.org/stable/40539320

Conner, L. D. C., & Danielson, J. (2016). Scientist role models in the classroom: How important is gender matching? International Journal of Science Education, 38 (15), 2414–2430. https://doi.org/10.1080/09500693.2016.1246780

Crosby, F. J. (1999). The developing literature on developmental relationships. In A. J. Murrell, F. J. Crosby, & R. J. Ely (Eds.), Mentoring dilemmas: Developmental relationships within multicultural organizations (pp. 3–20). Erlbaum. http://doi.org/ https://doi.org/10.4324/9781410601612-7

Davis, N. J. (2001). A study of the impact of a science symposium designed to influence middle school females toward further studies in science. Ph.D., The University of Iowa. ProQuest Dissertations and Theses (3018568).

Dean, J. (2014). Girls need ‘cool’ role models in math and engineering. The Times . Retrieved from http://www.thetimes.co.uk/tto/business/industries/engineering/article4033193.ece

Dennehy, T. C., & Dasgupta, N. (2017). Female peer mentors early in college increase women’s positive academic experiences and retention in engineering. Proceedings of the National Academy of Sciences, 114 , 5964–5969. https://doi.org/10.1073/pnas.1613117114

Downing, R. A., Crosby, F. J., & Blake-Beard, S. (2005). The perceived importance of developmental relationships on women undergraduates’ pursuit of science. Psychology of Women Quarterly, 29 (4), 419–426. https://doi.org/10.1111/j.1471-6402.2005.00242.x

Drury, B. J., Siy, J. O., & Cheryan, S. (2011). When do female role models benefit women? the importance of differentiating recruitment from retention in STEM. Psychological Inquiry, 22 , 265–269. https://doi.org/10.1080/1047840X.2011.620935

Dubetz, T., & Wilson, J. A. (2013). Girls in engineering, mathematics and science, GEMS: A science outreach program for middle-school female students. Journal of STEM Education, 14 (3), 41–47.

Dweck, C. S. (1999). Self-theories: Their role in motivation, personality, and development . Psychology Press.

Dweck, C. S. (2006). Mindset . Random House.

Dweck, C. S., & Leggett, E. L. (1988). A social-cognitive approach to motivation and personality. Psychological Review, 95 , 256–273. https://doi.org/10.1037/0033-295X.95.2.256

Easterbrook, M. J., & Hadden, I. R. (2021). Tackling educational inequalities with social psychology: Identities, contexts, and interventions. Social Issues and Policy Review, 15 (1), 180–236. https://doi.org/10.1111/sipr.12070

Eby, L. T. D. T., Allen, T. D., Hoffman, B. J., Baranik, L. E., Sauer, J. B., Baldwin, S., et al. (2013). An interdisciplinary meta-analysis of the potential antecedents, correlates, and consequences of protégé perceptions of mentoring. Psychological Bulletin, 139 (2), 441–476. https://doi.org/10.1037/a0029279

Eccles, J. S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53 , 109–132. https://doi.org/10.1146/annurev.psych.53.100901.135153

Eccles, J. S., & Wigfield, A. (2020). From expectancy-value theory to situated expectancy-value theory: A developmental, social cognitive, and sociocultural perspective on motivation. Contemporary Educational Psychology . https://doi.org/10.1016/j.cedpsych.2020.101859

Evans, M. A., Whigham, M., & Wang, M. C. (1995). The effect of a role model project upon the attitudes of ninth-grade science students. Journal of Research in Science Teaching, 32 (2), 195–204.

Ferreira, M. (2001). Building communities through role models, mentors, and hands-on-science. School Community Journal, 11 (2), 27–38.

Fox, L. H. (1976). Changing behaviors and attitudes of gifted girls. Paper presented at the Annual Meeting of the American Psychological Association, Washington, DC.

Freeman, J. (2018). LGBTQ scientists are still left out. Nature, 559 , 27–28. https://doi.org/10.1038/d41586-018-05587-y

Gibson, D. E. (2004). Role models in career development: New directions for theory and research. Journal of Vocational Behavior, 65 (1), 134–156. https://doi.org/10.1016/S0001-8791(03)00051-4

Gilbert, P. (2015). The role of role models: How does identification with STEM role models impact women’s implicit STEM stereotypes and STEM outcomes? Ph.D., Tulane University. ProQuest Dissertations and Theses (3703388).

Goldberg, J. L., & Sedlacek, W.E. (1995). Summer Study in Engineering for High School Women. (ERIC Document Reproduction Service No. ED 399 876).

Graham, S. (2020). An attributional theory of motivation. Contemporary Educational Psychology, 61 , 101861. https://doi.org/10.1016/j.cedpsych.2020.101861

Gunderson, E. A., Gripshover, S. J., Romero, C., Dweck, C. S., Goldin-Meadow, S., & Levine, S. C. (2013). Parent praise to 1-to 3-year-olds predicts children’s motivational frameworks 5 years later. Child Development, 84 (5), 1526–1541. https://doi.org/10.1111/cdev.12064

Halpern, D. F., Aronson, J., Reimer, N., Simpkins, S., Star, J. R., & Wentzel, K. (2007). Encouraging girls in math and science (NCER 2007–2003). Washington, DC: National Center for Education Research, Institute of Education Science, U.S. Department of Education.

Hammrich, P. L., Richardson, G. M., & Livingston, B. (1999). Sisters in science: An intergenerational science program for elementary school girls. School Community Journal, 8 (2), 21–35.

Hernandez, D., Rana, S., Rao, A., & Usselman, M. (2017a). Dismantling stereotypes about Latinos in STEM. Hispanic Journal of Behavioral Sciences, 39 (4), 436–451. https://doi.org/10.1177/0739986317731100

Hernandez, P. R., Estrada, M., Woodcock, A., & Schultz, P. W. (2017b). Mentor qualities that matter: The importance of perceived (not demographic) similarity. Journal of Experimental Education, 85 (3), 450–468. https://doi.org/10.1080/00220973.2016.1246405

Herrmann, S. D., Adelman, R. M., Bodford, J. E., Graudejus, O., Okun, M. A., & Kwan, V. S. Y. (2016). The effects of a female role model on academic performance and persistence of women in STEM courses. Basic and Applied Social Psychology, 38 , 258–268. https://doi.org/10.1080/01973533.2016.1209757

Hoffman, A. J., & Kurtz-Costes, B. (2019). Promoting science motivation in American Indian middle school students: An intervention. Educational Psychology, 39 (4), 448–469. https://doi.org/10.1080/01443410.2018.1527019

Holmes, S., Redmond, A., Thomas, J., & High, K. (2012). Girls helping girls: Assessing the influence of college student mentors in an afterschool engineering program. Mentoring & Tutoring: Partnership in Learning, 20 (1), 137–150. https://doi.org/10.1080/13611267.2012.645604

Hong, H. Y., & Lin-Siegler, X. (2012). How learning about scientists’ struggles influences students’ interest and learning in physics. Journal of Educational Psychology, 104 (2), 469–484. https://doi.org/10.1037/a0026224

Hughes, R. M., Nzekwe, B., & Molyneaux, K. J. (2013). The single sex debate for girls in science: A comparison between two informal science programs on middle school students’ STEM identity formation. Research in Science Education, 43 , 1979–2007. https://doi.org/10.1007/s11165-012-9345-7

Johnson, J. (1989). Effects of successful female role models on young women’s attitudes toward traditionally male careers. Paper presented at the Annual Meeting for the Association for Educational Communications and Technology, Dallas, TX.

Johnson, I. R., Pietri, E. S., Fullilove, F., & Mowrer, S. (2019). Exploring identity-safety cues and allyship among black women students in STEM environments. Psychology of Women Quarterly, 43 (2), 131–150. https://doi.org/10.1177/0361684319830926

Jüni, P., Witschi, A., Bloch, R., & Egger, M. (1999). The hazards of scoring the quality of clinical trials for meta-analysis. JAMA, 282 (11), 1054–1060. https://doi.org/10.1001/jama.282.11.1054

Kant, J., Burckhard, S. & Meyers, R. (2018). Engaging high school girls in Native American culturally responsive STEAM activities. Journal of STEM Education, 18 (5), 15–25. https://www.learntechlib.org/p/182466/

Klassen, R. M., & Usher, E. L. (2010). Self-efficacy in educational settings: Recent research and emerging directions. In T. C. Urdan & S. A. Karabenick (Eds.), The decade ahead: Theoretical perspectives on motivation and achievement (pp. 1–33). Emerald Group Publishing Limited. https://doi.org/10.1108/s0749-7423(2010)000016a004

Chapter   Google Scholar  

Krämer, N. C., Karacora, B., Lucas, G., Dehghani, M., Rüther, G., & Gratch, J. (2016). Closing the gender gap in STEM with friendly male instructors? On the effects of rapport behavior and gender of a virtual agent in an instructional interaction. Computers & Education, 99 , 1–13. https://doi.org/10.1016/j.compedu.2016.04.002

Lauermann, F., Tsai, Y. M., & Eccles, J. S. (2017). Math-related career aspirations and choices within Eccles et al.’s expectancy–value theory of achievement-related behaviors. Developmental Psychology, 53 (8), 1540–1559. https://doi.org/10.1037/dev0000367

Lawner, E. K. (2014). Impact of role model gender and communality on college women’s math performance and interest in STEM. Unpublished master’s thesis. Storrs, CT: University of Connecticut.

Lawner, E. K., Quinn, D. M., Camacho, G., Johnson, B. T., & Pan-Weisz, B. (2019). Ingroup role models and underrepresented students’ performance and interest in STEM: A meta-analysis of lab and field studies. Social Psychology of Education, 22 , 1169–1195. https://doi.org/10.1007/s11218-019-09518-1

Leslie, S. J., Cimpian, A., Meyer, M., & Freeland, E. (2015). Expectations of brilliance underlie gender distributions across academic disciplines. Science, 347 , 262–265. https://doi.org/10.1126/science.1261375

Levere, J. (2018). Role models tell girls that STEM’s for them in new campaign. The New York Times . https://www.nytimes.com/2018/09/09/business/media/ad-council-stem-girls.html

Lin-Siegler, X., Ahn, J. N., Chen, J., Fang, F. F. A., & Luna-Lucero, M. (2016). Even Einstein struggled: Effects of learning about great scientists’ struggles on high school students’ motivation to learn science. Journal of Educational Psychology, 108 , 314–328. https://doi.org/10.1037/edu0000092

Liu, Y., Lou, S., & Shih, R. (2014). The investigation of STEM Self-Efficacy and Professional Commitment to Engineering among female high school students. South African Journal of Education, 34 (2), 1–15. https://doi.org/10.15700/201412071216

Lockwood, P., & Kunda, Z. (1997). Superstars and me: Predicting the impact of role models on the self. Journal of Personality and Social Psychology, 73 (1), 91–103. https://doi.org/10.1037/0022-3514.73.1.91

Martin, D. B. (2000). Mathematics success and failure among African-American youth: The roles of sociohistorical context, community forces, school influence, and individual agency . Routledge. https://doi.org/10.4324/9781410604866

Book   Google Scholar  

Marx, D. M., & Ko, S. J. (2012). Superstars ‘like’ me: The effect of role model similarity on performance under threat. European Journal of Social Psychology, 42 , 807–812. https://doi.org/10.1002/ejsp.1907

Marx, D. M., Monroe, A. H., Cole, C. E., & Gilbert, P. N. (2013). No doubt about it: When doubtful role models undermine men’s and women’s math performance under threat. The Journal of Social Psychology, 153 , 542–559. https://doi.org/10.1080/00224545.2013.778811

Marx, D. M., & Roman, J. S. (2002). Female role models: Protecting women’s math test performance. Personality and Social Psychology Bulletin, 28 , 1183–1193. https://doi.org/10.1177/01461672022812004

Mbano, N., & Nolan, K. (2017). Increasing access of female students in science, technology, engineering, and Mathematics (STEM), in the University of Malawi (UNIMA). Science Education International, 28 (1), 53–77.

McIntyre, R. B., Lord, C. G., Gresky, D. M., Ten Eyck, L. L., Frye, G. D. J., & Bond, C. F., Jr. (2005). A social impact trend in the effects of role models on alleviating women’s mathematics stereotype threat. Current Research in Social Psychology, 10 , 116–137.

McIntyre, R. B., Paulson, R. M., Taylor, C. A., Morin, A. L., & Lord, C. G. (2011). Effects of role model deservingness on overcoming performance deficits induced by stereotype threat. European Journal of Social Psychology, 41 (3), 301–311. https://doi.org/10.1002/ejsp.774

Mills, L. A., & Katzman, W. (2015). Examining the effects of field trips on science identity. Paper presented at the 12th International Conference on Cognition and Exploratory Learning in Digital Age, Kildare, Ireland.

Moher, D., Shamseer, L., Clarke, M., Ghersi, D., Liberati, A., Petticrew, M., et al. (2015). Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Systematic Reviews, 4 (1), 1. https://doi.org/10.1186/2046-4053-4-1

Morgenroth, T., Ryan, M. K., & Peters, K. (2015). The motivational theory of role modeling: How role models influence role aspirants’ goals. Review of General Psychology, 19 , 465–483. https://doi.org/10.1037/gpr0000059

Mueller, C. M., & Dweck, C. S. (1998). Praise for intelligence can undermine children’s motivation and performance. Journal of Personality and Social Psychology, 75 (1), 33–52. https://doi.org/10.1037/0022-3514.75.1.33

Murray, N. G., Opuni, K. A., Reininger, B., Sessions, N., Mowry, M. M., & Hobbs, M. (2009). A multi-media educational program that increases science achievement among inner-city non-Asian minority middle-school students. Academic Medicine, 84 , 803–811. https://doi.org/10.1097/ACM.0b013e3181a425e7

Nagengast, B., Marsh, H. W., Scalas, L. F., Xu, M. K., Hau, K. T., & Trautwein, U. (2011). Who took the “×” out of expectancy-value theory? A psychological mystery, a substantive-methodological synergy, and a cross-national generalization. Psychological Science, 22 (8), 1058–1066. https://doi.org/10.1177/0956797611415540

National Science Foundation. (2017). Women, minorities, and persons with disabilities in science and engineering . Arlington, VA: National Center for Science and Engineering Statistics, Directorate for Social, Behavioral, and Economic Sciences. Retrieved from https://www.nsf.gov/statistics/2017/nsf17310/

Newbill, P. L. (2005). Instructional strategies to improve women’s attitudes toward science . Ph.D., Virginia Polytechnic Institute and State University. ProQuest Dissertations and Theses (3164126).

O’Brien, L. T., Hitti, A., Shaffer, E., Camp, A. R. V., Henry, D., & Gilbert, P. N. (2017). Improving girls’ sense of fit in science: Increasing the impact of role models. Social Psychological and Personality Science, 8 (3), 301–309. https://doi.org/10.1177/1948550616671997

Olsson, M., & Martiny, S. E. (2018). Does exposure to counterstereotypical role models influence girls’ and women’s gender stereotypes and career choices? A review of social psychological research. Frontiers in Psychology, 9 , 2264. https://doi.org/10.3389/fpsyg.2018.02264

Parsons, J. E., Adler, T. F., & Kaczala, C. M. (1982). Socialization of achievement attitudes and beliefs: Parental influences. Child Development, 53 , 310–321.

Perez, T., Cromley, J. G., & Kaplan, A. (2014). The role of identity development, values, and costs in college STEM retention. Journal of Educational Psychology, 106 (1), 315–329. https://doi.org/10.1037/a0034027

Phelan, S. A., Harding, S. M., & Harper-Leatherman, A. S. (2017). BASE (broadening access to science education): A research and mentoring focused summer STEM camp serving underrepresented high school girls. Journal of STEM Education: Innovations and Research, 18 (1), 65–72.

Plant, E. A., Baylor, A. L., Doerr, C. E., & Rosenberg-Kima, R. B. (2009). Changing middle school students’ attitudes and performance regarding engineering with computer-based social models. Computers & Education, 53 (2), 209–215. https://doi.org/10.1016/j.compedu.2009.01.013

Porter, T., Catalán Molina, D., Cimpian, A., Roberts, S., Frederiks, A., Blackwell, L. S., & Trzesniewski, K. (2022). Growth mindset intervention delivered by teachers boosts achievement in early adolescence. Psychological Science .

Rosenthal, L., Levy, S. R., London, B., Lobel, M., & Bazile, C. (2013). In pursuit of the MD: The impact of role models, identity compatibility, and belonging among undergraduate women. Sex Roles, 68 (7–8), 464–473. https://doi.org/10.1007/s11199-012-0257-9

Sanderson, S., Tatt, I. D., & Higgins, J. (2007). Tools for assessing quality and susceptibility to bias in observational studies in epidemiology: A systematic review and annotated bibliography. International Journal of Epidemiology, 36 (3), 666–676. https://doi.org/10.1093/ije/dym018

Schmader, T., & Hall, W. M. (2014). Stereotype threat in school and at work: Putting science into practice. Policy Insights from the Behavioral and Brain Sciences, 1 (1), 30–37. https://doi.org/10.1177/2372732214548861

Schriver, M. L., Wolfe, L., & Strickland, W. J. (1995). A case study of a science and mathematics day camp as experienced by seven girls from rural Georgia . Paper presented at the annual meeting of the National Association for Research in Science Teaching, San Francisco, CA.

Schunk, D. H., & DiBenedetto, M. K. (2016). Self-efficacy theory in education. In K. R. Wentzel & D. B. Miele (Eds.), Handbook of motivation at school (2nd ed., pp. 34–54). Routledge. https://doi.org/10.4324/9781315773384-9

Schunk, D. H., & DiBenedetto, M. K. (2020). Motivation and social cognitive theory. Contemporary Educational Psychology, 60 , 101832. https://doi.org/10.1016/j.cedpsych.2019.101832

Schunk, D. H., & Usher, E. L. (2019). Social cognitive theory and motivation. In R. M. Ryan (Ed.), The Oxford handbook of human motivation (2nd ed., pp. 11–26). Oxford University Press. https://doi.org/10.1093/oxfordhb/9780190666453.013.2

Scott, J. R. (2013). Einstein girls: Exploring STEM careers interest and identity in an online mentoring community (Publication No.3584495) [Doctoral dissertation, University of Florida] . ProQuest Dissertations Publishing.

Shapiro, J. R., Williams, A. M., & Hambarchyan, M. (2013). Are all interventions created equal? A multi-threat approach to tailoring stereotype threat interventions. Journal of Personality and Social Psychology, 104 (2), 277–288. https://doi.org/10.1037/a0030461

Shin, J. E. L., Levy, S. R., & London, B. (2016). Effects of role model exposure on STEM and non-STEM student engagement. Journal of Applied Social Psychology, 46 , 410–427. https://doi.org/10.1111/jasp.12371

Siddaway, A. P., Wood, A. M., & Hedges, L. V. (2019). How to do a systematic review: A best practice guide for conducting and reporting narrative reviews, meta-analyses, and meta-syntheses. Annual Review of Psychology, 70 , 747–770. https://doi.org/10.1146/annurev-psych-010418-102803

Simpkins, S. D., Fredricks, J. A., & Eccles, J. S. (2015). The role of parents in the ontogeny of achievement-related motivation and behavioral choices. Monographs of the Society for Research in Child Development, 80 (2), 1–169. https://doi.org/10.1111/mono.12156

Smith, W. S., & Erb, T. O. (1986). Effect of women science career role models on early adolescents’ attitudes toward scientists and women in science. Journal of Research in Science Teaching, 23 (8), 667–676.

Steele, C. M. (1997). A threat in the air: How stereotypes shape intellectual identity and performance. American Psychologist, 52 , 613–629. https://doi.org/10.1037/0003-066X.52.6.613

Stout, J. G., Dasgupta, N., Hunsinger, M., & McManus, M. A. (2011). STEMing the tide: Using ingroup experts to inoculate women’s self-concept in science, technology, engineering, and mathematics (STEM). Journal of Personality and Social Psychology, 100 , 255–270. https://doi.org/10.1037/a0021385

Swindell, R. & Phelps, M. (1991). Designing and implementing science enrichment programs for rural females. Paper presented at the Rural Education Symposium, Nashville, TN.

Tan-Wilson, A., & Stamp, N. (2015). College students’ views of work–life balance in STEM research careers: Addressing negative preconceptions. CBE—Life Sciences Education, 14 (3), 1–13. https://doi.org/10.1187/cbe.14-11-0210

Valentine, J. C., & Cooper, H. M. (2005). Can we measure the quality of causal research in education? In G. D. Phye, D. H. Robinson, & J. R. Levin (Eds.), Empirical methods for evaluating educational interventions (pp. 85–111). Academic Press. https://doi.org/10.1016/B978-012554257-9/50006-1

Van Camp, A. R., Gilbert, P. N., & O’Brien, L. T. (2019). Testing the effects of a role model intervention on women’s STEM outcomes. Social Psychology of Education, 22 (3), 649–671. https://doi.org/10.1007/s11218-019-09498-2

Weidinger, A. F., Steinmayr, R., & Spinath, B. (2017). Math grades and intrinsic motivation in elementary school: A longitudinal investigation of their association. British Journal of Educational Psychology, 87 (2), 187–204. https://doi.org/10.1111/bjep.12143

Weiner, B. (1985). An attributional theory of achievement motivation and emotion. Psychological Review, 92 (4), 548–573. https://doi.org/10.1037/0033-295x.92.4.548

Wigfield, A., & Eccles, J. S. (2000). Expectancy–value theory of achievement motivation. Contemporary Educational Psychology, 25 (1), 68–81. https://doi.org/10.1006/ceps.1999.1015

Woodcock, A. (2012). Implicit ingroup biases and performance under threat: Do successful counterstereotypic exemplars help or harm? (Publication No. 3544586) [Doctoral dissertation, Purdue University]. ProQuest Dissertations Publishing.

Wyss, V. L., Heulskamp, D., & Siebert, C. J. (2012). Increasing middle school student interest in STEM careers with videos of scientists. International Journal of Environmental and Science Education, 7 (4), 501–522.

Yeager, D. S., Hanselman, P., Walton, G. M., Murray, J. S., Crosnoe, R., Muller, C., et al. (2019). A national experiment reveals where a growth mindset improves achievement. Nature, 573 (7774), 364–369. https://doi.org/10.1038/s41586-019-1466-y

Ziegler, A., & Stoeger, H. (2008). Effects of role models from films on short-term ratings of intent, interest, and self-assessment of ability by high school youth: A study of gender-stereotyped academic subjects. Psychological Reports, 102 (2), 509–531. https://doi.org/10.2466/pr0.102.2.509-531

Download references

Acknowledgements

We thank Tanner LeBaron Wallace, Shanette Porter, Allan Wigfield, the other Inclusive Mathematics Environments Fellows, and the members of the Cognitive Development Lab at New York University for insightful feedback on this work. We also thank Gabrielle Applebaum and Theodora Simons for their assistance with screening the manuscripts and synthesizing the evidence.

This work was supported by an Inclusive Mathematics Environments Early Career Fellowship from the Mindset Scholars Network, with the support of the Bill & Melinda Gates Foundation, to JRG. The funder played no part in the design of the study and collection, analysis, and interpretation of data.

Author information

Jessica R. Gladstone

Present address: Department of Foundations of Education, Virginia Commonwealth University, 1015 West Main Street, Richmond, VA, 23284, USA

Authors and Affiliations

Department of Psychology, New York University, 6 Washington Place, New York, NY, 10003, USA

Jessica R. Gladstone & Andrei Cimpian

You can also search for this author in PubMed   Google Scholar

Contributions

JRG and AC conceived the research. JRG performed the research and wrote the manuscript, with input from AC. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Jessica R. Gladstone or Andrei Cimpian .

Ethics declarations

Competing interests.

The authors declare that they have no competing interests.

Additional information

Publisher's note.

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

Supplementary Information

Additional file 1..

Complete list of articles included in the systematic review.

Rights and permissions

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

Reprints and permissions

About this article

Cite this article.

Gladstone, J.R., Cimpian, A. Which role models are effective for which students? A systematic review and four recommendations for maximizing the effectiveness of role models in STEM. IJ STEM Ed 8 , 59 (2021). https://doi.org/10.1186/s40594-021-00315-x

Download citation

Received : 22 July 2021

Accepted : 02 November 2021

Published : 02 December 2021

DOI : https://doi.org/10.1186/s40594-021-00315-x

Share this article

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

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

Provided by the Springer Nature SharedIt content-sharing initiative

• Role models

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

Role Models for Young People: What makes an effective role model program? A report to the National Youth Affairs Research Scheme

Related Papers

Susan Beltman , Judy MacCallum

Department of Education, …

Judy MacCallum

Lynda Wiest

Magnus Dahlstedt , Andreas Fejes

The aim of this paper is to analyse how a discourse on the role model operates in youth recreational work. Drawing on Michel Foucault’s concepts of subjectivity, discourse and power/knowledge, the paper analyses interviews with youth recreation leader students enrolled at a folk high school, and their talk about occupational choice. The analysis illustrates how a discourse on the role model emerge and operate through the ways students’ descriptions of ‘being’, and ‘doing’ intersect with their becoming as role models. The analysis raises several important questions. Should youth recreational work only target those young people who are already marginalised? Are there other subject positions than the role model, available to take up in youth recreation work? Are experiences of marginalisation necessary in order for a desirable youth recreation leader to emerge?

Üniversitepark Bülten

Necdet Aykaç

Ronald G. Ehrenberg

Haleema Din

International Journal of Medical Education

Ali Haghdoost

Dominic Abrams

Cypriot Journal of Educational Sciences

William Briddick

Loading Preview

Sorry, preview is currently unavailable. You can download the paper by clicking the button above.

RELATED PAPERS

Lindsey Peck

Philosophical Inquiry in Education

Hyemin Han , Nafsika Athanassoulis

Aiysha Majid

Educational Research

Mary Thornton

Environmental Education Research

Robert B Powell

Quality & Quantity

John Skvoretz

Applied Developmental Science

Julie Moore

Journal of Community Psychology

Joseph Ciarrochi

The Teachers College Record

Sabrina Zirkel

2018 ASEE Annual Conference & Exposition Proceedings

Widening Participation and Lifelong Learning

Johnsavio Romano

Gail M Ferguson

David DuBois

Petey Ndichu

Career Development International

Thomas Zagenczyk

Getahun Yacob Abraham

Medical Students Perspective about Attributes of Role Models - The Other Side of Story</p>

Fatima Shaikh

Tonya Price

Public/Private …

Fatemeh Alhani

Luqman A Wajid

RELATED TOPICS

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024

More From Forbes

Seeing is believing: female role models inspire girls to think bigger.

• Share to Facebook
• Share to Twitter
• Share to Linkedin

French Microbiologist Emmanuelle Charpentier (L) and US professor of Chemistry and of Molecular and ... [+] Cell Biology, Jennifer Doudna 2015 Princess of Asturias Award for Technical and Scientific Reseach in 2015. In October 2020 they were awared teh Nobel Prize for Chemistry. (Photo credit: MIGUEL RIOPA/AFP via Getty Images)

What does a scientist look like?

When the draw-a-scientist study (DAST) study was first conducted 50 years ago with elementary school students in the US and Canada, only twenty-eight of the 5,000 students (0.06%) drew a female scientist. Reflective of the times you might say. 

When it was repeated with a similar cohort of American students in 2018, 24% of the students drew a female scientist. Progress? Sure. But the results point to just how entrenched restrictive gender roles stereotypes still are for women and, more so, how vital it is to raise the visibility of female role models for girls and women, particularly those succeeding in fields long dominated by men.

Role models matter, particularly for women.

Research  shows that role models have an amplified benefit for women due to the gender biases, institutional barriers and negative stereotypes women have long had to contend with across a wide swathe of professional domains.

In short, seeing is believing. 

As Dr Nina Ansary wrote in  Anonymous Is A Woman , a compelling chronicle of global gender inequality that features extraordinary women throughout history whose names you’ve likely never heard of (but should have!) – “it’s hard to be what we cannot see.” Indeed, many of the statistics from this article have come from Dr Ansary’s deep research into global gender inequity - past and present.

Positive female role models have a magnified impact on women relative to male role models for men.

Best Travel Insurance Companies

Best covid-19 travel insurance plans.

Dr Ansary’s research resonates with my personal experience. Growing up on a small dairy farm in rural Australia, my own ambitions extended little further than the back paddock. Neither of my parents had graduated high school and, until I went to college (the first in my family to do so), I’d never even met a woman who’d graduated university, much less pursued a professional career.  

Looking back, I sometimes wonder what different choices I might have made in my early adult years had I been in closer proximity to strong female role models. As research shows, not being able see other women who look and act like them succeeding in areas of interest to them – particularly in the field of STEM and politics like -  discourages girls  from pursuing such careers.  

On the flipside, when girls can readily see women rising, on  their  terms, it spurs their ambitions. For instance,  female students are more likely to choose a major in STEM (science, technology, engineering or math) when they are assigned a female professor instead of a male one .

Research  shows that role models have three core benefits for women:

• role models represent and expand what is possible
• role models inspire women to be more ambitious and aim higher
• role models demonstrate the mindsets and behaviors of how to rise

The disproportional impact of the COVID pandemic on women (participation in the US labor force by women aged 25 to 54 dropped to 74.2% in September) should be a siren call the world over. So as we celebrate International Day of the Girl this Sunday, here are six ways to harness and magnify the impact of role models to empower more young women (and maybe a few not-so-young) to defy the social norms, stereotypes and biases that have too long stifled the potential of half the world’s population. 

Dial up the spotlight on more women - past and present

Of the world’s top 200 universities, only 34 (17%) were led by women in 2019. And if you walk into the libraries of those universities, you’ll be searching hard for stories of women wielding power and making history. Indeed, as Dr Ansary points out in her book, women occupy only 0.5% of history books. 

It’s little wonder that when scientists Jennifer Doudna and Emmanuelle Charpentier won the 2020 Nobel Prize in chemistry this week it made headlines. Of the 904 individuals awarded the Nobel Prize between 1901 and 1918, only 51 (less than 6%) were women (one of the many compelling statistics from Anonymous Is A Woman. )

With statistics like this, it’s little wonder that our mental template for power is intrinsically masculine. The ripple effect of the paucity visible female role models – past and present - impacts not just our beliefs about women’s ability to rise or to hold power today, but what we project for women in the future.

Dr Ansary, UN Women Global Champion, shared with me that raising the visibility of women in every sphere of endeavor is vital to empowering women and achieving gender parity. “It takes an accumulation of roles models – real life examples of what girls and women can do if given the opportunity – to change entrenched attitudes.” 

Spotlight more women succeeding on their terms 

If you’re a working woman who has ever struggled with  mothers’ guilt (as I have), you’ll know all too well that women have to deal with a greater complexity of expectations and conflicting social pressures than men. During my dissertation research, women leaders cited the impact of having strong female role models succeeding on  their  terms - women demonstrating the ability to manage the responsibilities of career and family - with authenticity and without apology.

Likewise, examples of women who’ve approached their career as a matrix rather than a ladder helps younger women think more laterally as they navigate their own career path. Knowing that there is not one linear path to forward can be both liberating and empowering. 

Highlight women taking risks and daring to fail

Women tend underestimate their abilities more so than men, reducing their confidence and making them less likely to put themselves forward for roles until they feel 100% ready.  As Kathy Calvin, former CEO and President of the United Nations Foundation shared with me on my Live Brave Podcast , “We women hold ourselves back. We think we aren’t experienced enough when in fact we are.”

Hearing stories of women starting businesses or taking career risks before they felt fully prepared can embolden other women to back themselves more and take a ‘leap of faith’ in themselves .  So too, when women see other women confidently embracing their unique feminine strengths and defying stereotypes, it encourages them to speak up more confidently, ask for what they want or say no to what they don’t.  

Celebrate women taking care while taking charge 

A  study  across 29 countries found that daughters whose mothers were pursuing careers were 1.21 times more likely to have higher annual earnings. This is partially because mothers, more so than fathers, are the most influential role models for daughters. Having a working mother has also been found to ameliorate against the gendered ‘think manager, think male’ stereotypes that fuel imposter syndrom e, make women less comfortable with power and more reticent to see themselves as future leaders.

Diversify role models to optimize relatability 

Not all women aspire to be CEO or enter politics.  Not all women like dressing up or charting a linear path.  We need a diversity of role models, with different styles, stories, personalities and perspectives. Because let’s face it, not all women make it to the top and not all women aspire to.  

My daughter attends an all-female college. Outside of COVID, her college regularly invites inspiring women and former alum (including her ultimate idol/role model, Gretta Gerwig, in whose footsteps she’d love to follow.)  Yet the women who’ve impacted her the most have been those just a few years ahead of her and less removed from her current reality. 

Highlighting only women who’ve reached the C-suite, won the Oscar or built a billion-dollar business can be counterproductive if they’re perceived to be unreachably far ahead. Diversity, matters, in all its forms. 

More women must step up and be the role model they wish they had 

Sheryl Sandberg once advised women to accept any invitation, regardless of why they were invited, and use it as an opportunity to champion other women to ‘lean in.’  Yet in my work with women leaders, I meet many who are reticent to be held as a ‘role model.’  Prone as women often are to talking themselves down and discounting their success, they argue for their shortcomings - for what they have yet to achieve. I know the feeling all too well. Yet as I wrote in a previous column on women’s mentorship, even women who are a long way from the top of the totem pole still have valuable wisdom to offer those a few years or rungs behind them.

So if you are a woman reading this, I encourage you to be the role model you wished you’d had when you were starting out.

If you’re asked to be a mentor,  say yes .

If you’re asked to be interviewed,  say yes .

If you’re asked to be on a panel,  say yes (and invite another woman to join you!)

Your story may well inspire a young woman to take a leap of faith in herself that she never would otherwise. 

While the COVID pandemic has taken a disproportionate hit on women, it’s also shone a light on the  strengths that women bring to leadership tables and reinforced the research that shows gender diversity - on teams and seated at decision making tables - lead to better outcomes for all stakeholders.

Given the recent estimate by McKinsey that gender parity would add 12 trillion dollars to the global economy by 2025, there’s a compelling case to not just shine the spotlight on women role models, but to push harder for more opportunities for other women to join them. 

As Dr Ansary shared with me, “Rectifying the wrongs of the past and sweeping up the ravages of this pandemic requires putting aside differences, collective collaboration and coming back to our core values and shared humanity.”

Likewise, meeting the challenges of the 21st century will require harnessing the unrealized potential of all women, the world over; bringing their full quota of talents and leadership strengths to all places where decisions are made.

A passionate champion for women, Margie Warrell is the founder of Global Courage and the  Brave Women Rising  program. She is also author of   You’ve Got This! The Life-Changing Power of Trusting Yourself

• Editorial Standards
• Reprints & Permissions

An official website of the United States government

Here’s how you know

Official websites use .gov A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS A lock ( Lock A locked padlock ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

https://www.nist.gov/blogs/blogrige/how-role-models-support-organizational-resilience

The Official Baldrige Blog

How Role Models Support Organizational Resilience

Senior executives of a sampling of Baldrige Award recipients since 2017 recently described exemplary practices of their organizations that are aligned with the   Baldrige Award Criteria’s added focus in 2024 on organizational resilience . They credited such practices with strengthening their organizations’ abilities to surmount formidable challenges and sustain their long-term success.  

For example, Michael Gerusky, chief innovation and performance officer of the Pittsburgh, Pennsylvania-based Center for Organ Recovery and Education (CORE) , a  2019 Baldrige Award recipient , shared that “CORE has evolved its strategic planning process to focus on innovation for achieving breakthrough results. This includes changing the way we set goals, moving from a process capability approach to align with industry-elite tier expectations.”

Dr. John Chessare, president and CEO of the 2020 Baldrige Award-winning GBMC HealthCare system (GBMC) , cited his organization’s “strategic planning process that is linked to our leadership system” as well as its “daily Lean Management System walk” as examples of practices that support the organization’s resilience. 

Kelly DiMartino, city manager of the City of Fort Collins (CO) , a 2017 Baldrige Award recipient , said that her city’s “leadership system supports resiliency through an agreed-upon set of key components and processes that—when executed effectively—create conditions for organizational success and high-quality services to our community.” 

In describing another example of such a practice “that aligns directly with our leadership system,” DiMartino stated, “Our key processes of strategic planning, budgeting, and performance measurement and review are systematic, evaluated for effectiveness, and improved on a regular basis. These key processes stood firm during COVID and enabled us to deliver a recovery plan to our organization and our community that has now become a ‘resiliency’ plan.”

Brian Dieter, president and CEO of Mary Greeley Medical Center (MGMC) , a  2019 Baldrige Award recipient , provided three examples of his organization’s effective practices in support of resilience:

• “Our ability to engage our workforce in meaningful, two-way communication throughout the pandemic via our safety huddles was critical in communication flow—keeping everyone up to date and safe.”   
• “Our systematic planning and deployment of strategies through our ‘Big Dot Goal’ approach ensures that our workforce understands organizational priorities and how their work supports key achievement of our strategic objectives.”  
• “Our focus on our workforce through the development of career pathways allowed us to improve staff engagement and reduce costly travel staff. ”

Dieter credited use of the Baldrige framework for providing a “powerful accelerant in the buildup of organizational defenses against challenges and difficulties, as well as the ability to withstand those challenges and recover quickly.”

“Our steadfast commitment to the Baldrige framework has provided us with vital business structure as we navigate tremendous challenges in the health care environment,” said Dieter. 

Baldrige Excellence Framework®

The Baldrige Excellence Framework®  has empowered organizations to accomplish their missions, improve results, and become more competitive. It includes the Criteria for Performance Excellence®, core values and concepts, and guidelines for evaluating your processes and results.

Purchase your copy today!

Available versions: Business/Nonprofit , Education , and Health Care

About the author

Christine Schaefer

Christine Schaefer is a longtime staff member of the Baldrige Performance Excellence Program (BPEP). Her work has focused on producing BPEP publications and communications. She also has been highly involved in the Baldrige Award process, Baldrige examiner training, and other offerings of the program.

She is a Phi Beta Kappa graduate of the University of Virginia, where she was an Echols Scholar and a double major, receiving highest distinction for her thesis in the interdisciplinary Political & Social Thought Program. She also has a master's degree from Georgetown University, where her studies and thesis focused on social and public policy issues. 

When not working, she sits in traffic in one of the most congested regions of the country, receives consolation from her rescued beagles, writes poetry, practices hot yoga, and tries to cultivate a foundation for three kids to direct their own lifelong learning (and to PLEASE STOP YELLING at each other—after all, we'll never end wars if we can't even make peace at home!).

Related posts

The Baldrige Executive Fellows, Capstone Projects, and the A3 Problem-Solving Tool

What's Happening Now with Communities of Excellence 2026?

Senior Leadership Succession Planning: Who Cares?

Add new comment.

• No HTML tags allowed.
• Web page addresses and email addresses turn into links automatically.
• Lines and paragraphs break automatically.

New UNESCO global report highlights critical role of early childhood care and education

Co-published by UNESCO and UNICEF, the new report  delivers on a commitment made at the 2022  World Conference on ECCE , when 155 countries committed to ensuring that every child gets at least one year of free, compulsory pre-primary education and to work towards dedicating at least 10% of education budgets to this crucial life stage.

“Investing in our youngest children brings the greatest returns , both socially and economically. It is the best investment a country can make. The price of inaction can be very high, as our work shows,” said Stefania Giannini, UNESCO’s Assistant Director-General for Education at the launch of the report during the  Stocktake of Transformative Actions in Education event  on 17 June 2024.

Ms Giannini highlighted that the efforts to get early childhood education at the top of the global agenda is paying off: 95% of countries have now reported action on ECCE since the 2022 Transforming Education Summit, up from 40% who made initial national commitments.

Key challenges

The report advocates for the promotion of ECCE to prepare children for school. This includes developing programmes that enhance literacy, numeracy, and social-emotional skills, essential for robust educational outcomes. 

Three key action areas need particular attention: 

• Access : We are in a learning crisis. Without immediate action, 37% of the world’s children –over 300 million – won’t reach minimum reading proficiency by 2030. To reach SDG 4.2 dedicated to early childhood learning, we need to enroll 1.4 million children every year until 2030.
• Teacher shortages and qualifications : Many children, especially the most disadvantaged, are taught by underqualified teachers. In low-income countries, only 57% of pre-primary teachers have the necessary training. We need 6 million more pre-primary educators and teachers by 2030.
• Funding : ECCE is severely underfunded. We need an additional [$21] twenty-one billion US Dollars annually to meet national goals for pre-primary education through to 2030.

Nine recommendations

The report presents concrete recommendations on how governments and the international community can tackle global learning and well-being challenges by promoting an integrated early childhood care and education ecosystem that better supports children and families. 

Key among these are putting young children at the centre of our policies, boosting funding from both domestic and international sources, strengthening global partnerships, and expanding the right to education to ensure every child has a solid educational foundation. 

The report also underscores the need for a legally binding international framework to establish the right to ECCE is underscored, aiming to set clear state obligations, promote accountability, and ensure adequate funding for early education sectors. This is considered as critical to preventing the deepening of the global education crisis. 

The report's key findings and recommendations were presented to stakeholders at a launch event on 17 June. The event will include discussions current initiatives under the  Global Partnership Strategy  to promote ECCE equity and inclusion. 

The new ECCE report was published with the support of GPE, ILO, OECD and The LEGO Foundation.

Related items

• Early childhood education
• Early childhood
• Topics: Display
• See more add

More on this subject

Other recent news

News Summit on AI and the media: UNESCO in partnership with the AUB calls on the African media to seize technological advances 2 April 2024

• Facebook Share Button
• Twitter Share Button
• Linkedin Share Button

“The Road to the AI Era Is Paved in Semiconductor Manufacturing Innovation," an in-depth white paper written by The TechArena and sponsored by Lam Research, explores the pivotal role of chipmaking innovation in ushering in the next transformative technological age. The paper provides authoritative insights into the revolutionary effects of generative AI and large language models across various industries, the emerging challenges in delivering performance to meet these advanced requirements, and the critical importance of high bandwidth memory (HBM) in AI compute platforms. 

Further, the research uncovers the strategic collaborations and the promising future of chiplet-based processing solutions, as well as the advancements in 2.5D and 3D packaging technologies that are set to redefine the semiconductor landscape. 

As Bob O’Donnell, president and chief analyst at TECHnalysis Research, notes in the paper:

"The trick to continuing AI advancements is to give accelerator chips, involved with the training and inferencing of enormous foundation models, speedy access to as much memory as possible. While that sounds simple in theory, making it happen within the confined space of a chiplet-based SoC (System on Chip) requires clever advanced packaging and the continued innovation of semiconductor manufacturing processes by industry leaders like Lam Research. Stacked memory technologies such as HBM3e meet the critical performance requirements these AI accelerators need, but they’re only possible thanks to equipment that can create all the inter-chip connection elements—including robust TSVs (Through Silicon Vias)—reliably and with atomic-scale."

This article is an essential read for those seeking a comprehensive understanding of the technological advancements that are shaping the future of AI and semiconductor innovation. 

Read “ The Road to the AI Era Is Paved in Semiconductor Manufacturing Innovation ” on The TechArena. 

Social support as a key factor in chronic pain management programs: a scoping review

• Published: 24 June 2024

Cite this article

• Chan Gong 1 ,
• Hao Shan 2 ,
• Yuxue Sun 1 ,
• Jiewen Zheng 1 ,
• Chenchen Zhu 1 ,
• Weiquan Zhong 1 ,
• Jiabao Guo 1 &
• Binglin Chen   ORCID: orcid.org/0000-0002-7434-2551 1  

Chronic pain is a debilitating condition that profoundly and persistently affects individuals’ lives. Managing chronic pain presents complex challenges, and a growing body of literature underscores the pivotal role of social support in its management and its implications for treatment and rehabilitation. Gaining an understanding of the multifaceted aspects of social support in chronic pain management can contribute to the development of more effective interventions and strategies to enhance the lives of those impacted by chronic pain. However, at present, systematic reviews that investigate the correlation between social support and chronic pain are lacking. This paper presents a scoping review of 56 relevant literature sources, with the aim of improving our comprehension of the relationship between chronic pain and social support. The review comprehensively examines common types of evidence, particularly on inconsistencies in research tools within this field of study and divergent concepts of social support related to chronic pain. The paper delves into the correlation model between social support and chronic pain, explores relevant mechanisms, and emphasizes the differences found among relevant studies. By synthesizing these findings, the review emphasizes potential areas for further research and exploration, facilitating future studies and the development of new approaches in this field.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

The Impact of the Quantity and Quality of Social Support on Patients with Chronic Pain

Perceived Stress, Perceived Social Support, and Global Health in Adults with Chronic Pain

Older adults’ preferences for formal social support of autonomy and dependence in pain: development and validation of a scale, data availability.

The data are available from the corresponding author upon reasonable request.

Arksey, H., & O’Malley, L. (2005). Scoping studies: Towards a methodological framework. International Journal of Social Research Methodology, 8 (1), 19–32.

Article   Google Scholar  

A, F. J. (1994). Depression in painful chronic disorders: The role of pain and conflict about pain. Journal of Pain and Symptom Management 9:520–526. https://doi.org/10.1016/0885-3924(94)90114-7

A, R. K., Marisol, H., P, J. M. & D, C. D. (2007). Cognitions, coping, and social environment predict adjustment to pain in spinal cord injury. The Journal of Pain: official journal of the American Pain Society 8. https://doi.org/10.1016/j.jpain.2007.05.006

Abraido-Lanza, A. F. (2004). Social support and psychological adjustment among Latinas with arthritis: A test of a theoretical model. Annals of Behavioral Medicine: A Publication of the Society of Behavioral Medicine, 27 , 162–171. https://doi.org/10.1207/s15324796abm2703_4

Article   PubMed   Google Scholar  

Ascigil, E., Uysal, A., & Saracgil Cosar, S. N. (2019). Longitudinal associations between provision of autonomy support and well-being in spouses of individuals with chronic pain. Annals of Behavioral Medicine: A Publication of the Society of Behavioral Medicine, 53 , 372–382. https://doi.org/10.1093/abm/kay049

Aydede, M. (2019). Does the IASP definition of pain need updating? Pain Reports, 4 , e777–e777.

Article   PubMed   PubMed Central   Google Scholar  

B., W. S., B., P. J., Veronica, K., Tara, S. (2018). Close relationships as a contributor to chronic pain pathogenesis: Predicting pain etiology and persistence. Social Science & Medicine 237. https://doi.org/10.1016/j.socscimed.2019.112452 .

Beggs, R. T., Holtzman, S., & DeLongis, A. (2016). Predicting daily satisfaction with spouse responses among people with rheumatoid arthritis. Annals of Behavioral Medicine, 50 , 24–33. https://doi.org/10.1007/s12160-015-9728-x

Bennett-Brown, M., Adams, O. R., Campbell, J. T., Moscovici, Z. & Gesselman, A. N. (2022). Chronic vulvovaginal pain in patients of color: Benefits of partner supportiveness in relation to sexual dissatisfaction and distress. International Journal of Environmental Research and Public Health, 19 (7), 3975. https://doi.org/10.3390/ijerph19073975

Benville, J. R., Compton, P., Giordano, N. A., & Cheatle, M. D. (2021). Perceived social support in patients with chronic pain with and without opioid use disorder and role of medication for opioid use disorder. Drug and Alcohol Dependence,  221 , 8. https://doi.org/10.1016/j.drugalcdep.2021.108619

Cano, A. (2004). Pain catastrophizing and social support in married individuals with chronic pain: The moderating role of pain duration. Pain, 110 , 656–664. https://doi.org/10.1016/j.pain.2004.05.004

Cano, A. & Williams, A. C. d. C. (2010). Social interaction in pain: Reinforcing pain behaviors or building intimacy? Pain 149, 9–11. https://doi.org/10.1016/j.pain.2009.10.010

Cano, A., Leong, L., Heller, J. B., & Lutz, J. R. (2009). Perceived entitlement to pain-related support and pain catastrophizing: Associations with perceived and observed support. Pain, 147 , 249–254. https://doi.org/10.1016/j.pain.2009.09.023

Carissa, V. d. B. -C., L, W. T., David, S. F. (2017). Three types of intimate relationships among individuals with chronic pain and a history of trauma exposure. Healthcare (Basel, Switzerland) 5. https://doi.org/10.3390/healthcare5040068

Carriere, J. S., Lazaridou, A., Martel, M. O., Cornelius, M., Campbell, C., Smith, M., Haythornthwaite, J. A., & Edwards, R. R. (2020). The moderating role of pain catastrophizing on the relationship between partner support and pain intensity: A daily diary study in patients with knee osteoarthritis. Journal of Behavioral Medicine, 43 , 807–816. https://doi.org/10.1007/s10865-019-00121-5

Cecil, H., Stanley, M. A., Carrion, P. G., & Swann, A. (1995). Psychometric properties of the MSPSS and NOS in psychiatric outpatients. Journal of Clinical Psychology, 51 , 593–602.

Che, X., Cash, R., Ng, S. K., Fitzgerald, P., & Fitzgibbon, B. M. (2018). A systematic review of the processes underlying the main and the buffering effect of social support on the experience of pain. Clinical Journal of Pain, 34 , 1061–1076. https://doi.org/10.1097/AJP.0000000000000624

Chen, F., & Xu, J. (2017). Correlation between quality of life and social support of empty nest elderly patients with chronic pain. Chinese Journal of Modern Medicine, 27 (13), 91–95. (in Chinese).

Google Scholar  

Chen, F., Song, Y. & Xu, J. (2017). Correlation between life quality and social support of empty nest elderly patients with chronic pain. Modern Preventive Medicine , 44 (14), 2553–2556+2579. (in Chinese).

Cho, S., Zunin, I., Chao, P., Heiby, E., & McKoy, J. (2012). Effects of pain controllability and discrepancy in social support on depressed mood among patients with chronic pain. International Journal of Behavioral Medicine, 19 , 270–279. https://doi.org/10.1007/s12529-011-9175-4

Choi, J. W., So, W. Y., & Kim, K. M. (2022). The mediating effects of social support on the relationship between uncertainty and quality of life among patients with chronic low back pain: A cross-sectional survey. Healthcare (Basel) ,  10 (9), 1805 . https://doi.org/10.3390/healthcare10091805

Cohen, S. P., Vase, L., & Hooten, W. M. (2021). Chronic pain: An update on burden, best practices, and new advances. Lancet, 397 , 2082–2097.

Colquhoun, H. L., Levac, D., O’Brien, K. K., Straus, S., Tricco, A. C., Perrier, L., Kastner, M., & Moher, D. (2014). Scoping reviews: Time for clarity in definition, methods, and reporting. Journal of Clinical Epidemiology, 67 , 1291–1294. https://doi.org/10.1016/j.jclinepi.2014.03.013

D, K. R., Roberta, R. & D, O. J. (2002). Self-appraised problem solving and pain-relevant social support as predictors of the experience of chronic pain. Annals of behavioral medicine: a publication of the Society of Behavioral Medicine , 24. https://doi.org/10.1207/S15324796ABM2402_06 .

Danijela, S., Jun, Z. & Jiafan, H. (2020). The role of pain, disability and perceived social support in psychological and academic functioning of university students with pain: an observational study. International Journal of Adolescent Medicine and Health, 33 (3), 209–217 . https://doi.org/10.1515/ijamh-2019-0032

Di Tella, M., Tesio, V., Ghiggia, A., Romeo, A., Colonna, F., Fusaro, E., Geminiani, G. C., Bruzzone, M., Torta, R., & Castelli, L. (2018). Coping strategies and perceived social support in fibromyalgia syndrome: Relationship with alexithymia. Scandinavian Journal of Psychology, 59 , 167–176. https://doi.org/10.1111/sjop.12405

Driscoll, M. A., Higgins, D. M., Seng, E. K., Buta, E., Goulet, J. L., Heapy, A. A., Kerns, R. D., Brandt, C. A., & Haskell, S. G. (2015). Trauma, social support, family conflict, and chronic pain in recent service veterans: Does gender matter? Pain Medicine, 16 , 1101–1111. https://doi.org/10.1111/pme.12744

E., L. M. A., Rosa, E. Z. & Carmen, R. M. (2007). Perceived social support and coping responses are independent variables explaining pain adjustment among chronic pain patients. Journal of Pain , 9. https://doi.org/10.1016/j.jpain.2007.12.002

Edwards, R. R., Dworkin, R. H., Sullivan, M. D., Turk, D. C., & Wasan, A. D. (2016). The role of psychosocial processes in the development and maintenance of chronic pain. Journal of Pain, 17 , T70–T92. https://doi.org/10.1016/j.jpain.2016.01.001

Eisenberger, N. I., Master, S. L., Inagaki, T. K., Taylor, S. E., Shirinyan, D., Lieberman, M. D., & Naliboff, B. D. (2011). Attachment figures activate a safety signal-related neural region and reduce pain experience. Proceedings of the National Academy of Sciences of the United States of America, 108 , 11721–11726. https://doi.org/10.1073/pnas.1108239108

Ester, S., Mélanie, R., Catarina, T.-P., Santiago, G., P, J. M. & Jordi, M. (2020). Social factors, disability, and depressive symptoms in adults with chronic pain. The Clinical Journal Of Pain , 36. https://doi.org/10.1097/AJP.0000000000000815

Evers, A. W., Kraaimaat, F. W., Geenen, R., Jacobs, J. W., & Bijlsma, J. W. (2003). Pain coping and social support as predictors of long-term functional disability and pain in early rheumatoid arthritis. Behaviour Research and Therapy, 41 , 1295–1310. https://doi.org/10.1016/S0005-7967(03)00036-6

Fanmin, Z., Xueli, S., Bangxiang, Y. & Xiaoqian, F. (2016). Life events, anxiety, social support, personality, and alexithymia in female patients with chronic pain: A path analysis. Asia-Pacific Psychiatry: official journal of the Pacific Rim College of Psychiatrists , 8 . https://doi.org/10.1111/appy.12222

Fernandez-Pena, R., Molina, J. L., & Valero, O. (2018). Personal network analysis in the study of social support: The case of chronic pain. International Journal of Environmental Research and Public Health, 15 , 18. https://doi.org/10.3390/ijerph15122695

Fernandez-Pena, R., Molina, J. L., & Valero, O. (2020). Satisfaction with social support received from social relationships in cases of chronic pain: The influence of personal network characteristics in terms of structure, composition and functional content. International Journal of Environmental Research and Public Health, 17 , 17. https://doi.org/10.3390/ijerph17082706

Fordyce, W. E. (1997). Behavioral methods for chronic pain and illness. Pain, 3 , 291–292.

Franqueiro, A. R., Yoon J, Crago, M. A., Curiel M. & Wilson, J. M. (2023). The interconnection between social support and emotional distress among individuals with chronic pain: A narrative review. Psychology Research and Behavior Management , 16 :4389–4399. https://doi.org/10.2147/PRBM.S410606

Gatzounis, R., Schrooten, M. G., Crombez, G., & Vlaeyen, J. W. (2012). Operantlearning theory in pain and chronic pain rehabilitation. Current Pain and Headache Reports, 16 , 117–126.

Gerdle, B., Dragioti, E., Rivano Fischer, M., & Ringqvist, A. (2023). Pain intensity and psychological distress show different associations with interference and lack of life control: A clinical registry-based cohort study of >40,000 chronic pain patients from SQRP. Frontiers in Pain Research (Lausanne, Switzerland), 4 , 1093002–1093002.

Gong, G. (2012). Study on pain self-management status and influencing factors among middle-aged and older adults with arthritis. Huazhong University of Science and Technology  (in Chinese). https://doi.org/10.7666/d.D231150

Guillory, J., Chang, P., Henderson, C. R., Shengelia, R., Lama, S., Warmington, M., Jowza, M., Waldman, S., Gay, G., & Reid, M. C. (2015). Piloting a text message-based social support intervention for patients with chronic pain establishing feasibility and preliminary efficacy. Clinical Journal of Pain, 31 , 548–556. https://doi.org/10.1097/AJP.0000000000000193

Guo, T. (2015). The relationships among social support, self-efficacy and quality of life in community-dwelling elderly with chronic pain. Tianjin Medical University. (in Chinese).

Haringsma, R., Engels, G. I., Beekman, A. T., & Spinhoven, P. (2004). The criterion validity of the Center for Epidemiological Studies Depression Scale (CES-D) in a sample of self-referred elders with depressive symptomatology. International Journal of Geriatric Psychiatry, 19 , 558–563.

Holtzman, S. (2004). The role of social support in coping with daily pain among patients with rheumatoid arthritis. Journal of Health Psychology , 9 . https://doi.org/10.1177/13591053040456381

Jamison, R. N., & Virts, K. L. (1990). The influence of family support on chronic pain. Behaviour Research and Therapy, 28 , 283–287.

Jung, M. C., C, D. M., R, M. I., Paul, K., Won, S. H., M, E. D., Howard, T., D, K. R. & P, J. M. (2019). Personal resource profiles of individuals with chronic pain: Sociodemographic and pain interference differences. Rehabilitation psychology , 64. https://doi.org/10.1037/rep0000261

Junghaenel, D. U., Schneider, S., & Broderick, J. E. (2018). Partners’ Overestimation of Patients’ pain severity: Relationships with partners’ interpersonal responses. Pain Medicine, 19 , 1772–1781. https://doi.org/10.1093/pm/pnx217

Juyoung, P., David, N., Gabriella, E., M, H. L. & Anna, S. (2017). The moderating and covarying effects of social support and pain intensity on depressive symptomology among racially and ethnically diverse older adults. Pain management 7. https://doi.org/10.2217/pmt-2016-0027

Kostova, Z., Caiata-Zufferey, M., & Schulz, P. J. (2015). Can social support work virtually? Evaluation of rheumatoid arthritis patients’ experiences with an interactive online tool. Pain Research & Management, 20 , 199–209.

Leonard, M. T., Cano, A., & Johansen, A. B. (2006). Chronic pain in a couples context: A review and integration of theoretical models and empirical evidence. The Journal of Pain, 7 , 377–390. https://doi.org/10.1016/j.jpain.2006.01.442

Levac, D., Colquhoun, H. & O’Brien, K. K. (2010). Scoping studies: advancing the methodology. Implementation Science , 5 . https://doi.org/10.1186/1748-5908-5-69 .

Li, S. Y. (2021). The relationships among self-efficacy, social support, and self-care behavior in the elderly patients with chronic pain (a STROBE-compliant article). Medicine, 100 , 4.

Lurenda, W. M., Juel, L. C., Karen, A., Helms, A. A., & Højland, J. R. (2021). Poor social support and loneliness in chronic headache: Prevalence and effect modifiers. Cephalalgia: an International Journal of Headache , 41 (13), 1318–1331. https://doi.org/10.1177/03331024211020392

M, G. K., J, K. F., E, C. J., J, V. D. P. (1987). Social support and pain behavior. Pain , 29 .

Mallon, T., Schafer, I., Fuchs, A., Gensichen, J., Maier, W., Riedel-Heller, S., Konig, H. H., Mergenthal, K., Schon, G., Wegscheider, K., Weyerer, S., Wiese, B., van den Bussche, H., & Scherer, M. (2022). The moderating effects of social support and depressive symptoms on pain among elderly multimorbid patients-data from the multicentre, prospective, observational cohort study MultiCare. Aging & Mental Health, 26 , 803–809. https://doi.org/10.1080/13607863.2021.1916882

Mark, S., Tegan, C. & Kristen, M. (2020). Social support facilitates physical activity by reducing pain. British Journal of Health Psychology , 25 . https://doi.org/10.1111/bjhp.12424

Matthias, M. S., Hirsh, A. T., Ofner, S., & Daggy, J. (2022). Exploring the relationships among social support, patient activation, and pain-related outcomes. Pain Medicine, 23 , 676–685.

Matthias, M. S., McGuire, A. B., Kukla, M., Daggy, J., Myers, L. J., & Bair, M. J. (2015). A brief peer support intervention for veterans with chronic musculoskeletal pain: A pilot study of feasibility and effectiveness. Pain Medicine, 16 , 81–87. https://doi.org/10.1111/pme.12571

McMurtry, M., Viswanath, O., Cernich, M., Strand, N., Freeman, J., Townsend, C., Kaye, A. D., Cornett, E. M., & Wie, C. (2020). The impact of the quantity and quality of social support on patients with chronic pain. Current Pain and Headache Reports, 24 , 5. https://doi.org/10.1007/s11916-020-00906-3

Melloh, M., Salathé, C. R., Elfering, A., Käser, A., Barz, T., Aghayev, E., Röder, C., & Theis, J. C. (2013). Occupational, personal and psychosocial resources for preventing persistent low back pain. International Journal of Occupational Safety and Ergonomics, 19 , 29–40. https://doi.org/10.1080/10803548.2013.11076964

Mobbs, D., Yu, R., Rowe, J. B., Eich, H., FeldmanHall, O., & Dalgleish, T. (2010). Neural activity associated with monitoring the oscillating threat value of a tarantula. Proceedings of the National Academy of Sciences of the United States of America, 107 , 20582–20586. https://doi.org/10.1073/pnas.1009076107

Montoya, P., Larbig, W., Braun, C., Preissl, H., & Birbaumer, N. (2004). Influence of social support and emotional context on pain processing and magnetic brain responses in fibromyalgia. Arthritis and Rheumatism, 50 , 4035–4044. https://doi.org/10.1002/art.20660

Newton-John, T. R. (2002). Solicitousness and chronic pain: a critical review. Pain Review , 9 :7. [17] Schwartz L.

Newton-John, T. R. (2013). How signifificant is the signifificant other in patient coping in chronic pain? Pain Management, 3 , 485–493.

Nicolson, P. J. A., Williamson, E., Morris, A., Sanchez-Santos, M. T., Bruce, J., Silman, A., & Lamb, S. E. (2021). Musculoskeletal pain and loneliness, social support and social engagement among older adults: Analysis of the Oxford pain, activity and lifestyle cohort. Musculoskeletal Care, 19 , 269–277. https://doi.org/10.1002/msc.1526

Ozdemir, I., Ozdemir, O., & Ozkose, Z. (2021). A novel technique in the management of severe postpartum uterine atony bleeding: Three vertical uterine compression sutures. European Journal of Obstetrics & Gynecology and Reproductive Biology, 260 , 208–211. https://doi.org/10.1016/j.ejogrb.2021.03.038

Park, J., Newman, D., Engstrom, G., Hammar, L. M., & Swall, A. (2017). The moderating and covarying effects of social support and pain intensity on depressive symptomology among racially and ethnically diverse older adults. Pain Management, 7 , 19–32. https://doi.org/10.2217/pmt-2016-0027

Penn, T. M., Trost, Z., Parker, R., Wagner, W. P., Owens, M. A., Gonzalez, C. E., White, D. M., Merlin, J. S., & Goodin, B. R. (2019). Social support buffers the negative influence of perceived injustice on pain interference in people living with HIV and chronic pain. Pain Reports, 4 , e710–e710.

Polomano, R. C., Droog, N., Purinton, M. C., & Cohen, A. S. (2007). Social support web-based resources for patients with chronic pain. Journal of Pain & Palliative Care Pharmacotherapy, 21 (3), 49–55.

Qian, R., Jing, Z., Shaoqing, W., Jue, H., & Menglin, J. (2022). Chronic pain and quality of life in maintenance hemodialysis patients in China: A multicenter, cross-sectional study. Journal of Pain Research , 15 , 147–157. https://doi.org/10.2147/JPR.S345610

Quartana, P. J., Campbell, C. M., & Edwards, R. R. (2009). Pain catastrophizing: A critical review. Expert Review of Neurotherapeutics, 9 , 745–758.

R, M. P. & C, T. (1993). The role of life event stress, coping and social support in chronic headaches. Headache , 33 . https://doi.org/10.1111/j.1526-4610.1993.hed3306301.x

Samulowitz, A., Hensing, G., Haukenes, I., Bergman, S., & Grimby-Ekman, A. (2022). General self-efficacy and social support in men and women with pain - irregular sex patterns of cross-sectional and longitudinal associations in a general population sample. Bmc Musculoskeletal Disorders , 23 (1), 1026. https://doi.org/10.1186/s12891-022-05992-5

Samulowitz, A., Haukenes, I., Grimby-Ekman, A., Bergman, S., & Hensing, G. (2023). Psychosocial resources predict frequent pain differently for men and women: A prospective cohort study. PLoS ONE , 18 (3):e0283222. https://doi.org/10.1371/journal.pone.0283222

Sarason, I. G., Levine, H. M., Basham, R. B., & Sarason, B. R. (1983). Assessing social support: The social support questionnaire. Journal of Personality and Social Psychology, 44 , 127–139. https://doi.org/10.1037/0022-3514.44.1.127

Schwartz, L., Slater, M. A., & Birchler, G. R. (1996). The role of pain behaviors in the modulation of marital conflflict in chronic pain couples. Pain, 65 , 227–233.

Shaygan, M., Boeger, A., & Kroener-Herwig, B. (2017). Valence and arousal value of visual stimuli and their role in the mitigation of chronic pain: What is the power of pictures? Journal of Pain, 18 , 124–131. https://doi.org/10.1016/j.jpain.2016.10.007

Sherbourne, C. D., & Stewart, A. L. (1991). The MOS social support survey. Social Science & Medicine, 1982 (32), 705–714. https://doi.org/10.1016/0277-9536(91)90150-B

Stroud, M. W., Turner, J. A., Jensen, M. P., & Cardenas, D. D. (2006). Partner responses to pain behaviors are associated with depression and activity interference among persons with chronic pain and spinal cord injury. Journal of Pain, 7 , 91–99. https://doi.org/10.1016/j.jpain.2005.08.006

Sullivan, M. J. L., Thorn, B., Haythornthwaite, J. A., Keefe, F., Martin, M., Bradley, L. A., & Lefebvre, J. C. (2001). Theoretical perspectives on the relation between catastrophizing and pain. Clinical Journal of Pain, 17 , 52–64. https://doi.org/10.1097/00002508-200103000-00008

Terkawi, A. S., Sullivan, M., Abolkhair, A., Al-Zhahrani, T., Terkawi, R. S., Alasfar, E. M., Khait, S. S. A., Elkabbani, A., Kabbani, N., Altirkawi, K. A. & Tsang, S. (2017). Development and validation of Arabic version of the pain catastrophizing scale. Saudi Journal of Anaesthesia . 11 (Suppl 1), S63-S70. https://doi.org/10.4103/sja.SJA_130_17

T, P., M, O., D, W., Z, T., J, M., B. & G. (2018). Social support buffers the negative influence of injustice perceptions on pain interference in persons living with HIV (PLWH) and chronic pain. Journal of Pain , 19 .

Wager, T. D., Scott, D. J., & Zubieta, J.-K. (2007). Placebo effects on human μ-opioid activity during pain. Proceedings of the National Academy of Sciences of the United States of America, 104 , 11056–11061.

Wang, Y., Wu, C., & Wu, A. (1997). The correlation study of chronic functional pain and psychosocial factors. Chinese Journal of Behavioral Medicine and Science, 04 , 23–26. (in Chinese).

Wilson, J. M., Colebaugh, C. A., Flowers, K. M., Meints, S. M., Edwards, R. R., & Schreiber, K. L. (2022). Social support and psychological distress among chronic pain patients: The mediating role of mindfulness. Personality and Individual Differences , 190,  111551. https://doi.org/10.1016/j.paid.2022.111551

Xiao, S. (1994). Theoretical foundation and research application of the social support rating scale. Clinical Psychiatry, 4 (2), 98–100. (in Chinese).

Yang, X. (2018). The impact of psychological intervention on the social and psychological status of chronic pain patients. Clinical Research, 26 (07), 184–185. (in Chinese).

Younger, J., Aron, A., Parke, S., Chatterjee, N. & Mackey, S. (2010). Viewing pictures of a romantic partner reduces experimental pain: Involvement of neural reward systems. PLoS ONE , 5 . https://doi.org/10.1371/journal.pone.0013309

Zeng, F., Sun, X., Yang, B., & Fu, X. (2016). Life events, anxiety, social support, personality, and alexithymia in female patients with chronic pain: A path analysis. Asia-Pacific Psychiatry, 8 , 44–50. https://doi.org/10.1111/appy.12222

Zhang, T. (2011). Analysis of the depressive disorder in patients with cancer pain . Hebei Medical University. (in Chinese).

Zhang, Y. (2013). Construction of the depressive attachment model in patients with chronic pain . Shanxi Medical University. (in Chinese).

Download references

This work was supported by the Natural Science Foundation of Jiangsu Province (grant no. BK20210907), Medical Research Project of Jiangsu Commission of Health (grant. no. Z2022004) and Research Foundation for Talented Scholars of Xuzhou Medical University (grant no. D2020056).

Author information

Authors and affiliations.

The Second School of Clinical Medical, Xuzhou Medical University, Xuzhou, Jiangsu, P.R. China

Chan Gong, Yuxue Sun, Jiewen Zheng, Chenchen Zhu, Weiquan Zhong, Jiabao Guo & Binglin Chen

Department of Clinical Laboratory, Jiangsu Rongjun Hospital, Wuxi, Jiangsu, P.R. China

You can also search for this author in PubMed   Google Scholar

Contributions

All authors have made substantial contributions to this research process. Binglin Chen and Jiabao Guo contributed to the study conception and design. Chan Gong, Hao Shan, Yuxue Sun, and Jiewen Zheng collected and analyses the data. Chan Gong and Chenchen Zhu wrote the manuscript. Binglin Chen Jiabao Guo, and Weiquan Zhong revised the manuscript. All authors read and approved the final version of manuscript.

Corresponding authors

Correspondence to Jiabao Guo or Binglin Chen .

Ethics declarations

Competing interests.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Additional information

Publisher's note.

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Gong, C., Shan, H., Sun, Y. et al. Social support as a key factor in chronic pain management programs: a scoping review. Curr Psychol (2024). https://doi.org/10.1007/s12144-024-06233-9

Download citation

Accepted : 03 June 2024

Published : 24 June 2024

DOI : https://doi.org/10.1007/s12144-024-06233-9

Share this article

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

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

Provided by the Springer Nature SharedIt content-sharing initiative

• Social support
• Chronic pain
• Scoping review
• Quality of life
• Social medicine
• Find a journal
• Publish with us
• Track your research

An official website of the United States government

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

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

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• v.336(7646); 2008 Mar 29

Teaching Rounds

Role modelling—making the most of a powerful teaching strategy.

Teachers need to be aware of the conscious and unconscious components of learning from role modelling, so that the net effect of the process is positive

• Role modelling is a powerful teaching tool for passing on the knowledge, skills, and values of the medical profession, but its net effect on the behaviour of students is often negative rather than positive
• By analysing their own performance as role models, individuals can improve their personal performance
• Being aware of the impact of what we are modelling (be it positive or negative)
• Protecting time to facilitate dialogue, reflection, and debriefing with students
• Making a conscious effort to articulate what we are modelling, and to make the implicit explicit

Educating future generations of physicians is one of the privileges and obligations of the medical profession. As an important part of this process, doctors historically have patterned their activities on those of practitioners whom they respect and trust. These have been called role models, “individuals admired for their ways of being and acting as professionals.” 1 Both consciously and unconsciously, we model our activities on such individuals. 2

“ We must acknowledge . . . that the most important, indeed the only, thing we have to offer our students is ourselves. Everything else they can read in a book.” – D C Tosteson 19

Although role modelling is at the heart of “character formation,” 3 medical students and junior doctors have observed that many clinical teachers are poor role models. In one study less than half of the teachers were identified as positive role models. 4 In another, half of the clinical clerks and a third of the residents surveyed felt that their teachers were not good role models for doctor-patient relationships. 1 Clearly, this is a situation that should concern us and the institutions in which we work.

Learning from role models occurs through observation and reflection, and is a complex mix of conscious and unconscious activities (fig 1 ​ 1 ). 5 We are all aware of the conscious observation of behaviours, but understanding the power of the unconscious component is essential. Active reflection on the process can convert an unconscious feeling into conscious thought that can be translated into principles and action. In an equally powerful process, observed behaviours are unconsciously incorporated into the belief patterns and behaviours of the student.

Fig 1 The process of role modelling 7

Role models differ from mentors. 6 Role models inspire and teach by example, often while they are doing other things. Mentors have an explicit relationship with a student over time, and they more often direct the student by asking questions and giving advice freely. 7

What makes for an effective role model?

As teachers we are capable of wide variation in our performance as role models, and we can demonstrate both positive and negative behaviours in a single encounter, as illustrated in the first vignette (box 1).

Box 1 Role modelling vignettes

1: in the middle of the night.

A house doctor on duty in the intensive care unit at night pages the consultant on call, Dr Smith, to inform her that a postoperative patient is not doing well. The consultant is obviously annoyed at being called in the middle of the night. Although she answers the questions and finally agrees to come in, the conversation is unpleasant and filled with tension, as Dr Smith is abrupt to the point of rudeness.

2: In the cafeteria

In the cafeteria, students and their mentor overhear Dr Reed, a senior surgeon, describing a patient in derogatory terms, using the patient’s name. The mentor asks to speak to Dr Reed privately, and reminds him that he is in a public place, violating patient confidentiality and failing to show respect.

3: In the clinic

A final year medical student on rotation in a general practitioner’s office finds that a patient with metastatic breast cancer is extremely upset and worried about her future. The general practitioner, Dr Jones, is extremely busy, with many patients waiting. Nevertheless, he enters the consultation room with the student, sits down and takes the patient’s hand, empathises with her concerns, and explains that he is very pressed for time; he also arranges a special appointment with her at a later date when he will have time to reassure her and answer her questions.

The characteristics of role models have been well documented 1 3 4 8 9 10 (fig 2 ​ 2) ) and can be divided into three categories:

Fig 2 Characteristics of role models

Clinical competence encompasses knowledge and skills, communication with patients and staff, and sound clinical reasoning and decision making. All of these skills must be modelled as they lie at the heart of the practice of medicine.

Teaching skills are the tools required to transmit clinical competence. A student centred approach incorporating effective communication, feedback, and opportunities for reflection is essential to effective role modelling.

Personal qualities include attributes that promote healing, such as compassion, honesty, and integrity. Effective interpersonal relationships, enthusiasm for practice and teaching, and an uncompromising quest for excellence are equally important.

Where does role modelling take place?

Medical education is complex and students can observe role models in a variety of educational settings. We now understand that there is a formal, an informal, and a hidden curriculum. 10 Role models function in all three.

The formal curriculum is outlined in mission statements and course objectives, detailing what faculty members believe they are teaching. Role modelling certainly takes place here, and the conduct of medical teachers at this level can have a profound effect. Teachers who show passion and enthusiasm for medicine can be extremely effective; failing to do so is to miss a valuable opportunity.

The informal curriculum, which consists of unscripted, unplanned, and highly interpersonal forms of teaching and learning, is very powerful. All role models, from peers to senior doctors, function in the informal curriculum, 10 11 and many of the corrosive effects of negative role modelling are experienced here. 4 12 As shown in the second vignette (box 1), conversations in both formal and informal settings reveal and transmit important attitudes and values.

Finally, a set of influences, which are largely hidden, function at the level of the organisational culture and structure of the institution. The influence of this hidden curriculum on role modelling can be profound, as many of the barriers to effective role modelling can be found here. For example, an institutional culture that promotes overwork, leaving insufficient time for harried clinical teachers to promote the type of reflective practice needed to demonstrate best practices among students, is detrimental to effective role modelling. Similarly, a culture that tolerates inadequate clinical care or poor interpersonal relationships inhibits positive modelling, as do administrative decisions that fail to show appreciation and support, both financial and non-financial, of those who are trying to be exemplary.

How can role modelling be improved?

The desire to better our own performance is a necessary first step in improving role modelling. Two areas need to be addressed: the personal level, where we analyse our own strengths and weaknesses as role models, and the organisational level, where we consider the impact of the institutional culture in which we teach.

Strategies to improve personal performance

Doctors can improve their teaching skills and possibly their effectiveness as role models. 2 13 Box 2 lists some strategies that can help to improve role modelling.

Box 2 Strategies to improve role modelling

• Be aware of being a role model
• Demonstrate clinical competence
• Protect time for teaching
• Show a positive attitude for what you do
• Implement a student centred approach to teaching
• Facilitate reflection on clinical experiences and what has been modelled
• Encourage dialogue with colleagues
• Engage in pertinent staff development
• Work to improve the institutional culture
• Whenever possible be explicit about what you are modelling

Awareness of being a role model

The conscious recognition of the importance of role modelling as a teaching and learning strategy, and the positive or negative impact of what we are modelling, is fundamental to improving performance. For example, is Dr Smith (vignette 1) aware of the impact of her “rude behaviour”—or of the fact that she is condoning it? We are role models at all times—when we are teaching and when we are in social situations. 9 We must also share our awareness with our students. For example, it is important for Dr Jones (vignette 3) to explain the encounter with the patient so that the student will reflect on and understand some of the lessons learnt.

Time to teach

Despite the challenges of clinical demands, we must “protect time” in order to facilitate dialogue, reflection, and debriefing, all of which are needed to make the lessons learnt through role modelling apparent to the learner. 14 15 For example, had Dr Smith (vignette 1) talked about her personal distress in the middle of the night, she would have given an important—and positive—message to the student. The lack of conversation led to a missed opportunity for learning.

Negative attitudes, such as those shown by Dr Smith (vignette 1) and Dr Reed (vignette 2), are as powerful as the positive ones demonstrated by the mentor in vignette 2 and Dr Jones in vignette 3. Consciously modelling competence, positive attitudes, and enthusiasm for the practice of medicine, as Dr Jones demonstrates, can be transformative. 1 4 However, we need to ensure that the behaviours being modelled are made explicit to the learner.

The importance of reflection: making the implicit explicit

The ability to be conscious of, and reflect on, the behaviours and attributes that we are demonstrating is crucial to effective role modelling. Whenever possible, we should examine and explain what we do in the presence of students, junior doctors, and colleagues. This has been called “reflection in action.” 14 It is also helpful to discuss with students the impact of the encounter on the patient, the student, and oneself after it has occurred—“reflection on action.” If we then relate this experience to future actions, we are engaged in “reflection for action.”

Many situations include a mix of positive and negative role modelling; reflecting on these experiences provides valuable lessons to students. For example, the mentor in vignette 2 demonstrates positive modelling through self regulation. The fact that Dr Smith (vignette 1) does come in during the middle of the night shows both commitment and altruism. However, without thinking about these events, students may not learn from what they have observed. Although important components of role modelling will always remain implicit, a conscious effort to articulate what we are modelling, and to make the implicit explicit, will benefit both the student and role model.

Participating in staff development

Staff development programmes designed to improve teaching skills are now widely available, both within institutions and at scientific meetings. 16 17 Some universities offer programmes specifically designed to improve role modelling. These can be of great value if they encourage reflection by the role models. Volunteering to participate in such activities indicates a desire to improve.

Strategies to improve the institutional culture

Given the multitude of factors that impact role modelling, working to improve the institutional culture in which we work cannot be ignored. The important institutional barriers to effective role modelling include an organisational structure that encourages overwork, resulting in insufficient time for teaching 15 ; a lack of institutional support for teaching 10 11 ; and a culture that accepts inadequate patient care or poor relationships between members of the healthcare team. 11 As individuals dedicated to student learning, we must work to overcome these barriers and take advantage of opportunities that can help to improve the environment in which we teach. Methods for doing so depend on the local organisation and culture; they include raising awareness, pointing out deficiencies, reinforcing strengths, analysing the local environment, and proposing remedial action, which will often include staff development. The object is to create an environment that supports positive role modelling.

In conclusion, role models do not only function at the bedside, in the clinic, or in an office. Role models in medicine fulfil their roles in virtually any situation in which a student can observe a clinical teacher. 18 In doing so, it is important to model a lifestyle characteristic of a professional. We must also recall that being an effective role model is an ideal to be pursued. No one is a perfect role model at all times. Our objective is to be as consistently good as we can be.

This series provides an update on practical teaching methods for busy clinicians who teach. The series advisers are Peter Cantillon, senior lecturer in the department of general practice at the National University of Ireland, Galway, Ireland, and Yvonne Steinert, professor of family medicine, associate dean for faculty development, and director of the Centre for Medical Education at McGill University, Montreal.

Contributors: All authors contributed equally to conceptualising and writing the manuscript.

Competing interests: None declared.

Provenance and peer review: Commissioned; not externally peer reviewed.

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Article Menu

• Subscribe SciFeed
• Recommended Articles
• Google Scholar
• on Google Scholar
• Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

Future horizons: the potential role of artificial intelligence in cardiology.

1. Introduction

1.1. terminology, 1.1.1. machine learning (ml), 1.1.2. deep learning (dl), 1.1.3. artificial neural networks (anns), 1.1.4. convolutional neural networks (cnns), 2. materials and methods, 3.1. electrocardiography (ecg), 3.1.1. echocardiography.

• Assessing and monitoring the left ventricular systolic and diastolic function;
• Evaluation of the right ventricular function;
• Evaluation and quantification of the cardiac chamber size;
• Assessing the functional significance of a valvular lesion and the evaluation of the prosthetic valve structure and function;
• Identification of the cardiac source of embolism and the evaluation of the cardiac masses;
• Evaluation of pericardial diseases [ 58 ].

3.1.2. Coronary Angiography

3.1.3. cardiac computed angiography, 3.1.4. computed tomography, 3.1.5. cardiac mri, 4. discussions, 4.1. challenges in ai implementation, 4.2. limitations, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

• Flora, G.D.; Nayak, M.K. A Brief Review of Cardiovascular Diseases, Associated Risk Factors and Current Treatment Regimes. Curr. Pharm. Des. 2019 , 25 , 4063–4084. [ Google Scholar ] [ CrossRef ]
• Roth, G.A.; Mensah, G.A.; Johnson, C.O.; Addolorato, G.; Ammirati, E.; Baddour, L.M.; Barengo, N.C.; Beaton, A.Z.; Benjamin, E.J.; Benziger, C.P.; et al. Global Burden of Cardiovascular Diseases and Risk Factors, 1990–2019: Update from the GBD 2019 Study. J. Am. Coll. Cardiol. 2020 , 76 , 2982–3021. [ Google Scholar ] [ CrossRef ]
• Haq, I.U.; Chhatwal, K.; Sanaka, K.; Xu, B. Artificial Intelligence in Cardiovascular Medicine: Current Insights and Future Prospects. Vasc. Health Risk Manag. 2022 , 18 , 517. [ Google Scholar ] [ CrossRef ]
• Amisha; Malik, P.; Pathania, M.; Rathaur, V.K. Overview of artificial intelligence in medicine. J. Fam. Med. Prim. Care 2019 , 8 , 2328. [ Google Scholar ] [ CrossRef ]
• Hamet, P.; Tremblay, J. Artificial intelligence in medicine. Metabolism 2017 , 69S , S36–S40. [ Google Scholar ] [ CrossRef ]
• Poalelungi, D.G.; Musat, C.L.; Fulga, A.; Neagu, M.; Neagu, A.I.; Piraianu, A.I.; Fulga, I. Advancing Patient Care: How Artificial Intelligence Is Transforming Healthcare. J. Pers. Med. 2023 , 13 , 1214. [ Google Scholar ] [ CrossRef ]
• Xu, N.; Yang, D.; Arikawa, K.; Bai, C. Application of artificial intelligence in modern medicine. Clin. eHealth 2023 , 6 , 130–137. [ Google Scholar ] [ CrossRef ]
• Fogel, A.L.; Kvedar, J.C. Artificial intelligence powers digital medicine. NPJ Digit. Med. 2018 , 1 , 5. [ Google Scholar ] [ CrossRef ]
• Zhang, J.; Zhang, J.; Jin, J.; Jiang, X.; Yang, L.; Fan, S.; Zhang, Q.; Chi, M. Artificial intelligence applied in cardiovascular disease: A bibliometric and visual analysis. Front. Cardiovasc. Med. 2024 , 11 , 1323918. [ Google Scholar ] [ CrossRef ]
• Noorbakhsh-Sabet, N.; Zand, R.; Zhang, Y.; Abedi, V. Artificial Intelligence Transforms the Future of Healthcare. Am. J. Med. 2019 , 132 , 795. [ Google Scholar ] [ CrossRef ]
• Bostrom, N. How Long Before Superintelligence? Int. J. Future Stud. 1998 , 2 , 11–30. [ Google Scholar ]
• Sun, K.; Roy, A.; Tobin, J.M. Artificial intelligence and machine learning: Definition of terms and current concepts in critical care research. J. Crit. Care 2024 , 82 , 154792. [ Google Scholar ] [ CrossRef ]
• Chakraborty, C.; Bhattacharya, M.; Pal, S.; Lee, S.-S. From machine learning to deep learning: Advances of the recent data-driven paradigm shift in medicine and healthcare. Curr. Res. Biotechnol. 2024 , 7 , 100164. [ Google Scholar ] [ CrossRef ]
• Botvinick, M.; Ritter, S.; Wang, J.X.; Kurth-Nelson, Z.; Blundell, C.; Hassabis, D. Reinforcement Learning, Fast and Slow. Trends Cogn. Sci. 2019 , 23 , 408–422. [ Google Scholar ] [ CrossRef ]
• Jakhar, D.; Kaur, I. Artificial intelligence, machine learning and deep learning: Definitions and differences. Clin. Exp. Dermatol. 2020 , 45 , 131–132. [ Google Scholar ] [ CrossRef ]
• Piccialli, F.; Di Somma, V.; Giampaolo, F.; Cuomo, S.; Fortino, G. A survey on deep learning in medicine: Why, how and when? Inf. Fusion. 2021 , 66 , 111–137. [ Google Scholar ] [ CrossRef ]
• Dongare, A.D.; Kharde, R.R.; Kachare, A.D. Introduction to Artificial Neural Network. Certif. Int. J. Eng. Innov. Technol. (IJEIT) 2008 , 9001 , 2277–3754. [ Google Scholar ]
• Micheli-Tzanakou, E. Artificial Neural Networks: Definitions, Methods, Applications. In Supervised and Unsupervised Pattern Recognition ; CRC Press: Boca Raton, FL, USA, 2017; pp. 61–78. [ Google Scholar ]
• Zhou, S.K.; Rueckert, D.; Fichtinger, G. (Eds.) Handbook of Medical Image Computing and Computer Assisted Intervention ; Academic Press: Cambridge, MA, USA, 2020. [ Google Scholar ]
• Berezsky, O.; Liashchynskyi, P.; Pitsun, O.; Izonin, I. Synthesis of Convolutional Neural Network architectures for biomedical image classification. Biomed. Signal Process Control 2024 , 95 , 106325. [ Google Scholar ] [ CrossRef ]
• Piraianu, A.I.; Fulga, A.; Musat, C.L.; Ciobotaru, O.R.; Poalelungi, D.G.; Stamate, E.; Ciobotaru, O.; Fulga, I. Enhancing the Evidence with Algorithms: How Artificial Intelligence Is Transforming Forensic Medicine. Diagnostics 2023 , 13 , 2992. [ Google Scholar ] [ CrossRef ]
• Herman, R.; Meyers, H.P.; Smith, S.W.; Bertolone, D.T.; Leone, A.; Bermpeis, K.; Viscusi, M.M.; Belmonte, M.; Demolder, A.; Boza, V.; et al. International evaluation of an artificial intelligence-powered electrocardiogram model detecting acute coronary occlusion myocardial infarction. Eur. Heart J. Digit. Health 2023 , 5 , 123–133. [ Google Scholar ] [ CrossRef ]
• Nogimori, Y.; Sato, K.; Takamizawa, K.; Ogawa, Y.; Tanaka, Y.; Shiraga, K.; Masuda, H.; Matsui, H.; Kato, M.; Daimon, M.; et al. Prediction of adverse cardiovascular events in children using artificial intelligence-based electrocardiogram. Int. J. Cardiol. 2024 , 406 , 132019. [ Google Scholar ] [ CrossRef ]
• Hillis, J.; Bizzo, B.C.; Mercaldo, S.; Ghatak, A.; Macdonald, A.; Halle, M.; Schultz, A.; L’Italien, E.; Tam, V.; Awad, A.; et al. Detection of Hypertrophic Cardiomyopathy on Electrocardiogram Using Artificial Intelligence. J. Am. Coll. Cardiol. 2024 , 83 , 2609. [ Google Scholar ] [ CrossRef ]
• Haimovich, J.S.; Diamant, N.; Khurshid, S.; Di Achille, P.; Reeder, C.; Friedman, S.; Singh, P.; Spurlock, W.; Ellinor, P.T.; Philippakis, A.; et al. Artificial intelligence–enabled classification of hypertrophic heart diseases using electrocardiograms. Cardiovasc. Digit. Health J. 2023 , 4 , 48–59. [ Google Scholar ] [ CrossRef ]
• Harmon, D.M.; Mangold, K.; Baez Suarez, A.; Scott, C.G.; Murphree, D.H.; Malik, A.; Attia, Z.I.; Lopez-Jimenez, F.; Friedman, P.A.; Dispenzieri, A.; et al. Postdevelopment Performance and Validation of the Artificial Intelligence-Enhanced Electrocardiogram for Detection of Cardiac Amyloidosis. JACC Adv. 2023 , 2 , 100612. [ Google Scholar ] [ CrossRef ]
• Butler, L.; Karabayir, I.; Kitzman, D.W.; Alonso, A.; Tison, G.H.; Chen, L.Y.; Chang, P.P.; Clifford, G.; Soliman, E.Z.; Akbilgic, O. A generalizable electrocardiogram-based artificial intelligence model for 10-year heart failure risk prediction. Cardiovasc. Digit. Health J. 2023 , 4 , 183–190. [ Google Scholar ] [ CrossRef ]
• Awasthi, S.; Sachdeva, N.; Gupta, Y.; Anto, A.G.; Asfahan, S.; Abbou, R.; Bade, S.; Sood, S.; Hegstrom, L.; Vellanki, N.; et al. Identification and risk stratification of coronary disease by artificial intelligence-enabled ECG. eClinicalMedicine 2023 , 65 , 102259. [ Google Scholar ] [ CrossRef ]
• Lee, Y.H.; Hsieh, M.T.; Chang, C.C.; Tsai, Y.L.; Chou, R.H.; Lu, H.H.S.; Huang, P.-H. Improving detection of obstructive coronary artery disease with an artificial intelligence-enabled electrocardiogram algorithm. Atherosclerosis 2023 , 381 , 117238. [ Google Scholar ] [ CrossRef ]
• Valente Silva, B.; Marques, J.; Nobre Menezes, M.; Oliveira, A.L.; Pinto, F.J. Artificial intelligence-based diagnosis of acute pulmonary embolism: Development of a machine learning model using 12-lead electrocardiogram. Rev. Port. Cardiol. 2023 , 42 , 643–651. [ Google Scholar ] [ CrossRef ]
• Sau, A.; Ibrahim, S.; Kramer, D.B.; Waks, J.W.; Qureshi, N.; Koa-Wing, M.; Keene, D.; Malcolme-Lawes, L.; Lefroy, D.C.; Linton, N.W.; et al. Artificial intelligence–enabled electrocardiogram to distinguish atrioventricular re-entrant tachycardia from atrioventricular nodal re-entrant tachycardia. Cardiovasc. Digit. Health J. 2023 , 4 , 60–67. [ Google Scholar ] [ CrossRef ]
• Shimojo, M.; Inden, Y.; Yanagisawa, S.; Suzuki, N.; Tsurumi, N.; Watanabe, R.; Nakagomi, T.; Okajima, T.; Suga, K.; Tsuji, Y.; et al. A novel practical algorithm using machine learning to differentiate outflow tract ventricular arrhythmia origins. J. Cardiovasc. Electrophysiol. 2023 , 34 , 627–637. [ Google Scholar ] [ CrossRef ]
• Shiokawa, N.; Izumo, M.; Shimamura, T.; Kurosaka, Y.; Sato, Y.; Okamura, T.; Akashi, Y.J. Accuracy and Efficacy of Artificial Intelligence-Derived Automatic Measurements of Transthoracic Echocardiography in Routine Clinical Practice. J. Clin. Med. 2024 , 13 , 1861. [ Google Scholar ] [ CrossRef ]
• Sveric, K.M.; Ulbrich, S.; Dindane, Z.; Winkler, A.; Botan, R.; Mierke, J.; Trausch, A.; Heidrich, F.; Linke, A. Improved assessment of left ventricular ejection fraction using artificial intelligence in echocardiography: A comparative analysis with cardiac magnetic resonance imaging. Int. J. Cardiol. 2024 , 394 , 131383. [ Google Scholar ] [ CrossRef ]
• Slivnick, J.A.; Gessert, N.T.; Cotella, J.I.; Oliveira, L.; Pezzotti, N.; Eslami, P.; Sadeghi, A.; Wehle, S.; Prabhu, D.; Waechter-Stehle, I.; et al. Echocardiographic Detection of Regional Wall Motion Abnormalities Using Artificial Intelligence Compared to Human Readers. J. Am. Soc. Echocardiogr. 2024. [ CrossRef ]
• Kampaktsis, P.N.; Bohoran, T.A.; Lebehn, M.; McLaughlin, L.; Leb, J.; Liu, Z.; Moustakidis, S.; Siouras, A.; Singh, A.; Hahn, R.T.; et al. An attention-based deep learning method for right ventricular quantification using 2D echocardiography: Feasibility and accuracy. Echocardiography 2024 , 41 , e15719. [ Google Scholar ] [ CrossRef ]
• Murayama, M.; Sugimori, H.; Yoshimura, T.; Kaga, S.; Shima, H.; Tsuneta, S.; Mukai, A.; Nagai, Y.; Yokoyama, S.; Nishino, H.; et al. Deep learning to assess right ventricular ejection fraction from two-dimensional echocardiograms in precapillary pulmonary hypertension. Echocardiography 2024 , 41 , e15812. [ Google Scholar ] [ CrossRef ]
• Hsia, B.C.; Lai, A.; Singh, S.; Samtani, R.; Bienstock, S.; Liao, S.; Stern, E.; LaRocca, G.; Sanz, J.; Lerakis, S.; et al. Validation of American Society of Echocardiography Guideline-Recommended Parameters of Right Ventricular Dysfunction Using Artificial Intelligence Compared with Cardiac Magnetic Resonance Imaging. J. Am. Soc. Echocardiogr. 2023 , 36 , 967–977. [ Google Scholar ] [ CrossRef ]
• Anand, V.; Weston, A.D.; Scott, C.G.; Kane, G.C.; Pellikka, P.A.; Carter, R.E. Machine Learning for Diagnosis of Pulmonary Hypertension by Echocardiography. Mayo Clin. Proc. 2024 , 99 , 260–270. [ Google Scholar ] [ CrossRef ]
• Oikonomou, E.K.; Holste, G.; Yuan, N.; Coppi, A.; McNamara, R.L.; Haynes, N.A.; Vora, A.N.; Velazquez, E.J.; Li, F.; Menon, V.; et al. A Multimodal Video-Based AI Biomarker for Aortic Stenosis Development and Progression. JAMA Cardiol. 2024 , 9 , 534–544. [ Google Scholar ] [ CrossRef ]
• Krishna, H.; Desai, K.; Slostad, B.; Bhayani, S.; Arnold, J.H.; Ouwerkerk, W.; Hummel, Y.; Lam, C.S.; Ezekowitz, J.; Frost, M.; et al. Fully Automated Artificial Intelligence Assessment of Aortic Stenosis by Echocardiography. J. Am. Soc. Echocardiogr. 2023 , 36 , 769–777. [ Google Scholar ] [ CrossRef ]
• Guo, Y.; Xia, C.; Zhong, Y.; Wei, Y.; Zhu, H.; Ma, J.; Li, G.; Meng, X.; Yang, C.; Wang, X.; et al. Machine learning-enhanced echocardiography for screening coronary artery disease. Biomed. Eng. Online 2023 , 22 , 44. [ Google Scholar ] [ CrossRef ]
• Molenaar, M.A.; Bouma, B.J.; Asselbergs, F.W.; Verouden, N.J.; Selder, J.L.; Chamuleau, S.A.J.; Schuuring, M.J. Explainable machine learning using echocardiography to improve risk prediction in patients with chronic coronary syndrome. Eur. Heart J. Digit. Health 2024 , 5 , 170–182. [ Google Scholar ] [ CrossRef ]
• Lu, N.; Vaseli, H.; Mahdavi, M.; Taheri Dezaki, F.; Luong, C.; Yeung, D.; Gin, K.; Tsang, M.; Nair, P.; Jue, J.; et al. Automated Atrial Fibrillation Diagnosis by Echocardiography without ECG: Accuracy and Applications of a New Deep Learning Approach. Diseases 2024 , 12 , 35. [ Google Scholar ] [ CrossRef ]
• Brown, K.; Roshanitabrizi, P.; Rwebembera, J.; Okello, E.; Beaton, A.; Linguraru, M.G.; Sable, C.A. Using Artificial Intelligence for Rheumatic Heart Disease Detection by Echocardiography: Focus on Mitral Regurgitation. J. Am. Heart Assoc. 2024 , 13 , e031257. [ Google Scholar ] [ CrossRef ]
• Steffner, K.R.; Christensen, M.; Gill, G.; Bowdish, M.; Rhee, J.; Kumaresan, A.; He, B.; Zou, J.; Ouyang, D. Deep learning for transesophageal echocardiography view classification. Sci. Rep. 2024 , 14 , 11. [ Google Scholar ] [ CrossRef ]
• In Kim, Y.; Roh, J.H.; Kweon, J.; Kwon, H.; Chae, J.; Park, K.; Lee, J.-H.; Jeong, J.-O.; Kang, D.-Y.; Lee, P.H.; et al. Artificial intelligence-based quantitative coronary angiography of major vessels using deep-learning. Int. J. Cardiol. 2024 , 405 , 131945. [ Google Scholar ] [ CrossRef ]
• Rinehart, S.; Raible, S.J.; Ng, N.; Mullen, S.; Huey, W.; Rogers, C.; Pursnani, A. Utility of Artificial Intelligence Plaque Quantification: Results of the DECODE Study. J. Soc. Cardiovasc. Angiogr. Interv. 2024 , 3 , 101296. [ Google Scholar ] [ CrossRef ]
• Omori, H.; Matsuo, H.; Fujimoto, S.; Sobue, Y.; Nozaki, Y.; Nakazawa, G.; Takahashi, K.; Osawa, K.; Okubo, R.; Kaneko, U.; et al. Determination of lipid-rich plaques by artificial intelligence-enabled quantitative computed tomography using near-infrared spectroscopy as reference. Atherosclerosis 2023 , 386 , 117363. [ Google Scholar ] [ CrossRef ]
• Toggweiler, S.; Wyler von Ballmoos, M.C.; Moccetti, F.; Douverny, A.; Wolfrum, M.; Imamoglu, Z.; Mohler, A.; Gülan, U.; Kim, W.-K. A fully automated artificial intelligence-driven software for planning of transcatheter aortic valve replacement. Cardiovasc. Revascularization Med. 2024. [ CrossRef ]
• Salehi, M.; Maiter, A.; Strickland, S.; Aldabbagh, Z.; Karunasaagarar, K.; Thomas, R.; Lopez-Dee, T.; Capener, D.; Dwivedi, K.; Sharkey, M.; et al. Clinical assessment of an AI tool for measuring biventricular parameters on cardiac MR. Front. Cardiovasc. Med. 2024 , 11 , 1279298. [ Google Scholar ] [ CrossRef ]
• Ghanbari, F.; Joyce, T.; Lorenzoni, V.; Guaricci, A.I.; Pavon, A.G.; Fusini, L.; Andreini, D.; Rabbat, M.G.; Aquaro, G.D.; Abete, R.; et al. AI Cardiac MRI Scar Analysis Aids Prediction of Major Arrhythmic Events in the Multicenter DERIVATE Registry. Radiology 2023 , 307 , e222239. [ Google Scholar ] [ CrossRef ]
• Kaplan Berkaya, S.; Uysal, A.K.; Sora Gunal, E.; Ergin, S.; Gunal, S.; Gulmezoglu, M.B. A survey on ECG analysis. Biomed. Signal Process Control 2018 , 43 , 216–235. [ Google Scholar ] [ CrossRef ]
• Ogah, O.S.; Oladapo, O.O.; Adebiyi, A.A.; Adebayo, A.K.; Aje, A.; Ojji, D.B. Electrocardiographic left ventricular hypertrophy with strain pattern: Prevalence, mechanisms and prognostic implications. Cardiovasc. J. Afr. 2008 , 19 , 39. [ Google Scholar ]
• Bornstein, A.B.; Rao, S.S.; Marwaha, K. Left Ventricular Hypertrophy. [Updated 8 August 2023]. In StatPearls [Internet] ; StatPearls Publishing: Treasure Island, FL, USA, 2024. Available online: https://www.ncbi.nlm.nih.gov/books/NBK557534/ (accessed on 11 June 2024).
• Savarese, G.; Becher, P.M.; Lund, L.H.; Seferovic, P.; Rosano, G.M.C.; Coats, A.J.S. Global burden of heart failure: A comprehensive and updated review of epidemiology. Cardiovasc. Res. 2023 , 118 , 3272–3287. [ Google Scholar ] [ CrossRef ]
• Ojha, M.K.; Wadhwani, S.; Wadhwani, A.K.; Shukla, A. Automatic detection of arrhythmias from an ECG signal using an auto-encoder and SVM classifier. Phys. Eng. Sci. Med. 2022 , 45 , 665–674. [ Google Scholar ] [ CrossRef ]
• Ahmed, I.; Sasikumar, N. Echocardiography Imaging Techniques. [Updated 30 July 2023]. In StatPearls [Internet] ; StatPearls Publishing: Treasure Island, FL, USA, 2024. Available online: https://www.ncbi.nlm.nih.gov/books/NBK572130/ (accessed on 11 June 2024).
• Vidal-Perez, R.; Grapsa, J.; Bouzas-Mosquera, A.; Fontes-Carvalho, R.; Vazquez-Rodriguez, J.M. Current role and future perspectives of artificial intelligence in echocardiography. World J. Cardiol. 2023 , 15 , 284. [ Google Scholar ] [ CrossRef ]
• Carabello, B.A.; Paulus, W.J. Aortic stenosis. Lancet 2009 , 373 , 956–966. [ Google Scholar ] [ CrossRef ]
• Ring, L.; Shah, B.N.; Bhattacharyya, S.; Harkness, A.; Belham, M.; Oxborough, D.; Pearce, K.; Rana, B.S.; Augustine, D.X.; Robinson, S.; et al. Echocardiographic assessment of aortic stenosis: A practical guideline from the British Society of Echocardiography. Echo Res. Pract. 2021 , 8 , G19. [ Google Scholar ] [ CrossRef ]
• Stamate, E.; Piraianu, A.-I.; Ciobotaru, O.R.; Crassas, R.; Duca, O.; Fulga, A.; Grigore, I.; Vintila, V.; Fulga, I.; Ciobotaru, O.C. Revolutionizing Cardiology through Artificial Intelligence—Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment—A Comprehensive Review of the Past 5 Years. Diagnostics 2024 , 14 , 1103. [ Google Scholar ] [ CrossRef ]
• Suzuki, N.; Asano, T.; Nakazawa, G.; Aoki, J.; Tanabe, K.; Hibi, K.; Ikari, Y.; Kozuma, K. Clinical expert consensus document on quantitative coronary angiography from the Japanese Association of Cardiovascular Intervention and Therapeutics. Cardiovasc. Interv. Ther. 2020 , 35 , 105. [ Google Scholar ] [ CrossRef ]
• Ramjattan, N.A.; Lala, V.; Kousa, O.; Shams, P.; Makaryus, A.N. Coronary CT Angiography. [Updated 19 January 2024]. In StatPearls [Internet] ; StatPearls Publishing: Treasure Island, FL, USA, 2024. Available online: https://www.ncbi.nlm.nih.gov/books/NBK470279/ (accessed on 11 June 2024).
• Blanke, P.; Schoepf, U.J.; Leipsic, J.A. CT Transcatheter Aortic Valve Replacement. Radiology 2013 , 269 , 650–669. [ Google Scholar ] [ CrossRef ]
• Tseng, W.Y.I.; Su, M.Y.M.; Tseng, Y.H.E. Introduction to Cardiovascular Magnetic Resonance: Technical Principles and Clinical Applications. Acta Cardiol. Sin. 2016 , 32 , 129. [ Google Scholar ] [ CrossRef ]
• He, J.; Baxter, S.L.; Xu, J.; Xu, J.; Zhou, X.; Zhang, K. The practical implementation of artificial intelligence technologies in medicine. Nat. Med. 2019 , 25 , 30. [ Google Scholar ] [ CrossRef ]
• Kolla, L.; Parikh, R.B. Uses and limitations of artificial intelligence for oncology. Cancer 2024 , 130 , 2101–2107. [ Google Scholar ] [ CrossRef ]
• Oh, S.; Kim, J.H.; Choi, S.W.; Lee, H.J.; Hong, J.; Kwon, S.H. Physician Confidence in Artificial Intelligence: An Online Mobile Survey. J. Med. Internet Res. 2019 , 21 , e12422. [ Google Scholar ] [ CrossRef ]
• Patel, V.; Shah, M. Artificial intelligence and machine learning in drug discovery and development. Intell. Med. 2022 , 2 , 134–140. [ Google Scholar ] [ CrossRef ]
• Busnatu, Ș.; Niculescu, A.G.; Bolocan, A.; Petrescu, G.E.D.; Păduraru, D.N.; Năstasă, I.; Lupușoru, M.; Geantă, M.; Andronic, O.; Grumezescu, A.M.; et al. Clinical Applications of Artificial Intelligence—An Updated Overview. J. Clin. Med. 2022 , 11 , 2265. [ Google Scholar ] [ CrossRef ]
• Chan, B. Black-box assisted medical decisions: AI power vs. ethical physician care. Med. Health Care Philos. 2023 , 26 , 285. [ Google Scholar ] [ CrossRef ]
• Poon, A.I.F.; Sung, J.J.Y. Opening the black box of AI-Medicine. J. Gastroenterol. Hepatol. 2021 , 36 , 581–584. [ Google Scholar ] [ CrossRef ]
• Durán, J.M.; Jongsma, K.R. Who is afraid of black box algorithms? On the epistemological and ethical basis of trust in medical AI. J. Med. Ethics 2021 , 47 , 329–335. [ Google Scholar ] [ CrossRef ]
• Gerke, S.; Minssen, T.; Cohen, G. Ethical and legal challenges of artificial intelligence-driven healthcare. Artif. Intell. Healthc. 2020 , 295–336. [ Google Scholar ] [ CrossRef ]
• Tang, L.; Li, J.; Fantus, S. Medical artificial intelligence ethics: A systematic review of empirical studies. Digit. Health 2023 , 9 . [ Google Scholar ] [ CrossRef ]
• Sana, M.K.; Hussain, Z.M.; Maqsood, M.H.; Shah, P.A. Artificial intelligence in celiac disease. Comput. Biol. Med. 2020 , 125 , 103996. [ Google Scholar ] [ CrossRef ]
• Vinny, P.W.; Vishnu, V.Y.; Padma Srivastava, M.V. Artificial Intelligence shaping the future of neurology practice. Med. J. Armed Forces India 2021 , 77 , 276–282. [ Google Scholar ] [ CrossRef ]

Click here to enlarge figure

Share and Cite

Patrascanu, O.S.; Tutunaru, D.; Musat, C.L.; Dragostin, O.M.; Fulga, A.; Nechita, L.; Ciubara, A.B.; Piraianu, A.I.; Stamate, E.; Poalelungi, D.G.; et al. Future Horizons: The Potential Role of Artificial Intelligence in Cardiology. J. Pers. Med. 2024 , 14 , 656. https://doi.org/10.3390/jpm14060656

Patrascanu OS, Tutunaru D, Musat CL, Dragostin OM, Fulga A, Nechita L, Ciubara AB, Piraianu AI, Stamate E, Poalelungi DG, et al. Future Horizons: The Potential Role of Artificial Intelligence in Cardiology. Journal of Personalized Medicine . 2024; 14(6):656. https://doi.org/10.3390/jpm14060656

Patrascanu, Octavian Stefan, Dana Tutunaru, Carmina Liana Musat, Oana Maria Dragostin, Ana Fulga, Luiza Nechita, Alexandru Bogdan Ciubara, Alin Ionut Piraianu, Elena Stamate, Diana Gina Poalelungi, and et al. 2024. "Future Horizons: The Potential Role of Artificial Intelligence in Cardiology" Journal of Personalized Medicine 14, no. 6: 656. https://doi.org/10.3390/jpm14060656

Article Metrics

Article access statistics, further information, mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals