peer reviewed articles on diversity in education

REVIEW article

How to promote diversity and inclusion in educational settings: behavior change, climate surveys, and effective pro-diversity initiatives.

• Department of Psychology, University of Wisconsin-Madison, Madison, WI, United States

We review recent developments in the literature on diversity and inclusion in higher education settings. Diversity interventions increasingly focus on changing behaviors rather than mental constructs such as bias or attitudes. Additionally, there is now a greater emphasis on the evaluation of initiatives aimed at creating an inclusive climate. When trying to design an intervention to change behavior, it is advised to focus on a segment of the population (the “target audience”), to try to get people to adopt a small number of specific new behaviors (the “target behaviors”), and to address in the intervention the factors that affect the likelihood that members of the target audience will engage in the new target behaviors (the “barriers and benefits”). We report our recent work developing a climate survey that allows researchers and practitioners to identify these elements in a particular department or college. We then describe recent inclusion initiatives that have been shown to be effective in rigorous empirical studies. Taken together this paper shows that by implementing techniques based on research in the behavioral sciences it is possible to increase the sense of belonging, the success, and the graduation rate of minority students in STEM.

Introduction

Women, people of color, members of the LGBTQ + community, and members of other marginalized groups continue to be underrepresented in STEM fields ( National Science Foundation, 2020 ). Students from these groups are the target of both subtle and overt acts of discrimination, face negative stereotypes about their abilities, and experience disrespect and lack of inclusion by their instructors and peers ( Spencer and Castano, 2007 ; Wiggan, 2007 ; Cheryan et al., 2009 ). For example, students from marginalized groups are often assumed to be less intelligent and competent ( Moss-Racusin et al., 2014 ) and are often excluded when students form study groups or gather outside of class ( Slavin, 1990 ). Students from marginalized groups receive less challenging materials, worse feedback, and less time to respond to questions in class than their peers ( Beaman et al., 2006 ; Sadker et al., 2009 ). Additionally, the cultural mismatch between university norms and the cultural norms that students from marginalized groups were socialized in frequently leads to increased stress and negative emotions for these students ( Stephens et al., 2012 ).

Not surprisingly, students from marginalized groups are far more likely than high-status group members (e.g., White people, men) to report feeling as though they do not belong at universities ( Walton and Cohen, 2011 ). This is particularly problematic given that social belonging has been shown to be a key predictor of educational outcomes ( Dortch and Patel, 2017 ; Wolf et al., 2017 ; Murphy et al., 2020 ). Students who feel a greater sense of belonging are more likely to persist to graduation ( Strayhorn, 2012 ). Additionally, increased concerns about belonging can lead students to view common challenges—such as struggling to make friends or failing a test—as signs that they do not belong, promoting psychological disengagement and poorer educational outcomes ( Walton and Cohen, 2007 ). These challenges are exacerbated in STEM fields, which are typically dominated by members of high-status groups ( Rainey et al., 2018 ). Students from marginalized groups are particularly vulnerable to dropping out of STEM programs and the lack of a sense of community greatly contributes to this vulnerability ( O’Keefe, 2013 ).

It is clear then that the key to promoting academic success and retention of students from marginalized groups in STEM is creating an inclusive climate. In this article we will review recent developments within the diversity and inclusion literature about how to best promote inclusive behaviors and create an inclusive climate at colleges and universities. We will start out by describing recent shifts in the literature emphasizing the importance of changing behaviors rather than attitudes and the necessity to systematically evaluate diversity interventions. We will then review the key elements to designing effective interventions to promote diversity and inclusion. We will also talk about the use of focus groups and climate surveys to acquire the relevant background knowledge needed to design effective interventions. In the final section, we present recent initiatives that have successfully promoted diversity and inclusion in a variety of ways.

Recent Developments in Research on Diversity and Inclusion

A shift from reducing bias to promoting inclusive behavior.

Even though prejudice is communicated through behavior ( Carr et al., 2012 ), the traditional approach to prejudice reduction was to change explicit and implicit bias. The focus on bias was based on the assumption that changes in attitudes will subsequently lead to changes in behavior ( Dovidio et al., 2002 ). The universal acceptance of this assumption is surprising given the weak evidence for a link between attitudes and behavior. Explicit biases and attitudes more generally have been shown to predict behavior only weakly ( Wicker, 1969 ; Ajzen and Sheikh, 2013 ). Similarly, there is little to no connection between implicit bias and behavior ( Kurdi et al., 2019 ; Clayton et al., 2020 ). Implicit bias scores explain, at most, a very small proportion of the variability in intergroup behavior measured in lab settings, and this proportion is likely to be even smaller in more complex, real-world situations ( Oswald et al., 2013 ). Further, a change in implicit bias is not associated with a change in intergroup behavior. Lai et al. (2013) and Forscher et al. (2019) showed that while a variety of methods have been developed to change implicit bias, these methods produce trivial or nonexistent changes in intergroup behavior, and if they do, none of them last longer than 24 hours.

A growing body of research suggests that it is possible–and likely more effective–to focus on promoting inclusive behavior rather than improving individuals’ attitudes toward outgroup members. For example, Mousa (2020) randomly assigned Iraqi Christians displaced by the Islamic State of Iraq and Syria (ISIS) either to an all-Christian soccer team or to a team mixed with Muslims. Christians with Muslim teammates were more likely to vote for a Muslim from another team to receive a sportsmanship award, register for a mixed faith team next season, and train with other Muslim soccer players six months after the intervention. However, attitudes toward Muslims more broadly did not change. Similarly, Scacco and Warren (2018) examined if sustained intergroup contact in an educational setting between Christian and Muslim men in Kaduna, Nigeria led to increased harmony and reduced discrimination between the two groups. After the intervention, there were no reported changes in prejudicial attitudes for either groups, but Christians and Muslims who had high levels of intergroup contact engaged in fewer discriminatory behaviors than peers who had low levels of intergroup contact. These findings demonstrate that while promoting both positive intergroup attitudes and inclusive behavior is ideal, it is necessary to target inclusive behaviors directly rather than trying to change people’s biased attitudes with the assumption that such change will translate into a subsequent behavior change.

Greater Emphasis on Evaluation

Since the Civil Rights Act of 1964, researchers and practitioners have developed a variety of initiatives to combat racial prejudice in the United States (for reviews see Murrar et al., 2017 ; Paluck and Green, 2009 ; Paluck et al., 2021 ). Although these initiatives have been tested in individual studies, primarily in the lab, many of them have not undergone the rigorous scientific testing that is required to be able to conclude that they are effective in real-world settings ( Paluck and Green, 2009 ). Further, the evaluation studies frequently examined only the effects on self-report attitudes and not behavioral outcomes, which is problematic for reasons outlined in the previous paragraphs. In light of this deficit, there has been a recent shift in this field of research which now emphasizes the need for systemic evaluation of the effectiveness of diversity initiatives in the field ( Moss-Racusin et al., 2014 ).

Recent work examining the effectiveness of diversity initiatives has found mixed evidence for the idea that existing strategies reduce discrimination, create more inclusive environments, or increase the representation of marginalized groups ( Noon, 2018 ; FitzGerald et al., 2019 ; Dover et al., 2020 ). Most diversity training or implicit bias training workshops have been shown to be ineffective ( Bezrukova et al., 2016 ; Chang et al., 2019 ). Some interventions meant to promote diversity and inclusion actually achieve the opposite effect ( Dobbin and Kalev, 2018 ). For example, Dobbin et al., (2007) found that diversity training workshops had little to no effect on improving workplace diversity and some actually led to a decline in the number of Black women in management positions at companies. Similarly, Kulik et al. (2007) found that employees often respond to mandatory diversity training with anger and resistance and some report increased animosity toward members of marginalized groups afterward.

Designing Successful Behavioral Interventions

Behavior change interventions tend to be more effective if they involve a systematic, focused approach which consists of identifying and targeting specific behaviors, catering the intervention to a particular audience, and incorporating in the intervention relevant information about factors that affect how members of the target audience appraise the target behavior ( Campbell and Brauer, 2020 ). Below, we have outlined several methodological and theoretical considerations for practitioners whose goal is to develop a behavioral intervention to promote diversity and inclusion (see Figure 1 ).

FIGURE 1 . Key elements to consider designing a behavior change intervention (adapted from Campbell and Brauer, 2020 ).

Selecting a Target Behavior

Once a broad issue has been identified (e.g., promoting diversity and inclusion at a university department), it must be distilled into a measurable, actionable goal ( Smith, 2006 ). For example, one might focus on an outcome such as reducing the racial achievement gap. It is critical that the desired outcome is quantifiable, as that will allow one to determine whether a behavioral intervention has been a success.

The next step is to identify and select a desired behavior to be adopted (i.e., the target behavior). The goal is to choose a target behavior that will lead to the desired outcome if people actually perform it ( Lee and Kotler, 2019 ). Continuing with the previous example, a behavioral intervention with the goal of reducing the racial achievement gap may target behaviors such as encouraging White students to include students of color in their study groups and social events or motivate instructors to highlight to a greater extent the contributions of female scientists. Sometimes it is possible to promote multiple similar target behaviors in the same intervention.

To identify potential target behaviors it is usually advised to conduct background research (see next section of this paper). This research may involve semi-structured interviews or focus groups with members of marginalized groups. Climate surveys with closed and open-ended questions can be equally informative. The goal of the background research is to determine the behaviors that affect members of marginalized groups the most. It is crucial to know what behaviors they find offensive and disrespectful and thereby decrease their sense of belonging, and what behaviors make them feel included, welcomed, and cared for. Examples of target behaviors to promote inclusion are attending diversity-outreach events or consciously forming diverse work groups.

Once a list of potential target behaviors has been established, it is advised to choose one of them for the intervention. The choice can be guided by evaluating each potential target behavior along a number of relevant dimensions ( McKenzie-Mohr, 2011 ). One may consider, for example, the extent to which the effect of changing from the old behavior to the new target behavior will have a large effect (“impact”), how likely people are to adopt the target behavior (“probability”), and how many people currently do not yet engage in the target behavior (“market opportunity”). For instance, an intervention seeking to reduce discriminatory behaviors toward members of the LGBTQ + community in STEM contexts might consider focusing on encouraging students to learn what terms hurt the feelings of queer people and then abstain from using them, get the students to avoid gendered language, or promote joining a queer-straight alliance at their university. While a large number students joining a queer-straight alliance would have a big effect on the sense of belonging of members of the LGBTQ + community (high impact), it is unlikely many students will adopt this behavior if they are not already predisposed to do so (low probability). Similarly, it may be easy to get students to switch to gender neutral language (high probability), but if most students are already using this language then promoting this behavior will lead to only minor improvements (low market opportunity).

Ultimately the goal is to choose a single behavior (or a small set of interrelated behaviors) that will make the biggest difference for members of marginalized groups and then design an intervention that specifically encourages the adoption of this behavior ( Wymer, 2011 ).

Selecting a Target Audience

One of the most vital considerations when designing a behavioral intervention is the selection of a specific target audience ( Kotler et al., 2001 ). Different segments of the population are receptive to different messages, possess different motivations, and have different reasons for engaging or not engaging in the desirable behavior ( Walsh et al., 2010 ). Although all individuals in a specific setting are usually exposed to a given pro-diversity initiative (e.g., everyone in a specific department or college), the initiative is more likely to be effective if it is designed with a specific subset of the population in mind ( French et al., 2010 ).

The first step in determining a target audience is to segment the population into various groups along either demographic criteria (e.g., Whites, men), occupation (e.g., students, teaching assistants, faculty, staff), or psychological dimensions (e.g., highly egalitarian individuals, individuals with racist attitudes, folks in the middle). The background research described in the next section will help practitioners identify the groups that have the most negative impact on the climate in a department or college. One can find out from members of marginalized groups, for example, which groups treat them in the most offensive way or which kind of people have the most negative impact on their sense of belonging.

Although multiple groups may emerge as potential target audiences, it is generally advised to choose only one as the focus of the intervention. Similar to the process of selecting a target behavior, the choice of the target audience can be guided by considering a number of relevant dimensions: How large is the segment, and what percentage of the members of this segment currently do not yet engage in the target behavior (“size”)? To what extent are members of this segment able, willing, and ready to change their behavior (“readiness”)? How easy it is to identify the members of this segment and are there known distribution channels for persuasive messages (“reachability”)? Teaching assistants may be a group that can easily be instructed to adopt certain behaviors (high reachability), individuals with hostile feelings toward certain social groups may not be willing to behave inclusively (low readiness), and academic advisors may be a group that is too small and that students from marginalized backgrounds interact with too infrequently to be chosen as the target audience (small size).

Most effective behavior change interventions are designed with a single target audience in mind. That is, the communications and campaign materials are designed so that they are appealing and persuasive for the members of the chosen target audience. The objective should thus be to choose a single target audience that can be persuaded to adopt the target behavior and has a big impact on how included members of marginalized groups feel in the department or college.

Barriers and Benefits

It is critical to consider the factors that influence the likelihood that members of the target audience will engage in the desired target behavior, the so-called “barriers” and “benefits” ( Lefebvre, 2011 ). Barriers refer to anything that prevents an individual from engaging in a given behavior. Benefits are the positive outcomes an individual anticipates receiving as a result of engaging in the behavior. The ultimate goal is to design an intervention that makes salient the target audience’s perceived benefits of the new, desired target behavior and the perceived barriers toward engaging in the current, undesired behavior ( McKenzie-Mohr and Schultz, 2014 ).

Practitioners likely want to conduct background research to learn about the target audience’s motivations to engage in various behaviors. This can again be done with interviews, focus groups, or climate surveys, but this time the responses of members of the target audience, rather than the responses of members of marginalized groups, are most relevant. One should find out why members of the target audience currently do not perform the target behavior. Are there any logistic barriers (e.g., lack of opportunity) or psychological barriers (i.e., discomfort experienced around certain groups)? Are there any incorrect beliefs that underly the current behavior? The background research should also identify the positive consequences members of the target audience value and expect to experience when performing the target behavior. These consequences can then be highlighted in the intervention.

Both barriers and benefits can be abstract or concrete, internal or external, and real or perceived. For example, if an intervention seeks to encourage students from different backgrounds to be friendly to one another in the classroom members of the target audience may be apprehensive when interacting with outgroup members due to fear of saying something offensive (a barrier) but would interact more frequently with outgroup members if they believed that it would provide them an opportunity to make new friends (benefits). A well-designed behavioral intervention would then use this information to craft persuasive messages that directly address the target audience’s barriers and benefits. In this specific example, the intervention might involve providing people with tools to avoid offensive language and emphasize the potential to make new friends.

Elements That Increase the Persistence of a Behavioral Change

Sometimes people adopt a new behavior but then switch back to the old, undesired behavior after a few days or weeks. What can be done to increase the persistence of behavior change? One strategy that has proven to be particularly effective is to change the assumptions that people make about themselves and their environments ( Frey and Rogers, 2014 ; Walton and Wilson, 2018 ). For example, believing that one is not culturally competent will lead to interpreting difficult interactions with outgroup members as proof of this assumption. The more entrenched these beliefs become, the more difficult behaviors are to change. However, the human tendency to “make meaning” of oneself and one’s social situations can be harnessed for positive behavioral change. By altering the assumptions that lead to undesirable behaviors, it is possible to set in motion recursive cycles where a person’s new behavior leads to positive reactions in the environment, which in turn reinforces the self-representation that they are “the kind of person” who cares about this issue (e.g., diversity) and engages in these behaviors (e.g., inclusive behaviors). Consider an example from a different domain: Fostering a growth mindset where students start to believe they can improve through practice will change how they interpret successes and failures, thereby disrupting the negative feedback cycle that leads to poorer performance in school (see Yeager et al., 2019 ).

In addition, interventions that foster habit formation are more likely to increase the persistence of new behaviors ( Wood and Rünger, 2016 ). Interventions can promote habit formation by increasing the perceived difficulty of performing an undesirable behavior or by decreasing the perceived difficulty of doing the new target behavior. People will most often engage in behaviors that they perceive as being easy to do, regardless of whether or not the difference in difficulty is minimal. Additionally, providing easy to understand, recurring cues that encourage desirable behaviors and disrupt old, undesirable behaviors can help facilitate habit formation.

How to Conduct Relevant Background Research

There are a variety of ways how members of higher education institutions can identify the diversity-related issues that should be addressed in their department or college. The most frequently used methods are focus groups and climate surveys. We will discuss each of these methods below.

Focus groups are effective because a group member’s comment may cause other members to remember issues that they would not have thought of otherwise. It is easy to recruit students from marginalized groups by appealing to their departmental citizenship or by promising attractive prizes (e.g., two $100 gift certificates that will be given out to two randomly selected members of the focus group). It is generally advised to form groups of individuals sharing some social identity (i.e., African Americans, Latinxs, women in technical fields). Most individuals feel more comfortable voicing their concerns if the focus group facilitator also shares their social identity. Many universities have skilled focus group facilitators, but if necessary, it is possible to train research assistants by directing them to appropriate resources ( Krueger, 1994 ; https://fyi.extension.wisc.edu/programdevelopment/files/2016/04/Tipsheet5.pdf ).

Focus group members should be encouraged to talk about the situations in which they felt excluded, disrespected, or discriminated against. For example, focus group members might be asked questions such as “What exactly did the other person do or say? Where did the situation occur (in the classroom, during office hours)? Who was the other person (peer, instructor, staff)?” Focus group members should then be asked about the situations in which they felt included, respected, and cared for. Again, the goal should be to obtain precise information about the exact nature of the behaviors, the place in which they occurred, and person who engaged in the behaviors. It is useful to ask about the relative impact of these negative and positive behaviors. For example, one might ask “If you could eliminate one behavior here in this department which one would it be?” and “Among all the inclusive and respectful behaviors you just mentioned which one would increase your sense of belonging the most?”.

To assess the barriers and benefits of the potential target behaviors it can be useful to conduct focus groups with individuals who a priori do not come from any of the marginalized groups mentioned above. The facilitator can describe the negative behaviors (without labeling them as discriminatory) and ask whether the focus group members sometimes engage in them and if they do, why. One might ask about potential pathways to eliminate these undesired behaviors, e.g., “What would have to be different for you–or your peers–to no longer behave like that?”. The next step is to have a similar discussion about the positive target behavior: What prevents focus group members currently from engaging in this behavior? What could someone say or show to them so that they would engage in this behavior? If some members of the focus groups have recently started to do the positive behavior, what got them to change in the first place?

Focus groups are also useful to determine how able, willing, and ready to change their behavior members of different potential target audiences are. Several factors contribute to individuals’ “readiness” to change their behavior. These factors include openness to acting more inclusively ( Brauer et al., in press ), internal motivation to respond without prejudice ( Plant and Devine, 1998 ), lack of discomfort interacting with members of different social groups ( Stephan, 2014 ), and general enthusiasm for diversity ( Pittinsky et al., 2011 ). Facilitators can get at these factors by asking the members of the focus group about their motivation and perceived ability to engage in the target behavior.

Climate surveys are effective because they usually provide data from a larger and thus more representative sample in a given department or college. Various techniques exist to increase the response rate of respondents (e.g., Dykema et al., 2013 ). The exact content and length of a climate survey depend on the participant population and the frequency with which the survey is administered. The online supplemental material contains two examples developed by the Wisconsin Louis Stokes Alliance for Minority Participation (WiscAMP), one for graduate students of a university department and one for all undergraduate students on a campus. Other climate surveys used in higher education and numerous relevant references can be downloaded from this web address: http://psych.wisc.edu/Brauer/BrauerLab/index.php/campaign-materials/information-resources/

All climate surveys should measure demographic information, but in smaller units, anonymity may be an issue. Once gender identity is crossed with racial/ethnic identity and occupation (e.g., postdoc vs. assistant professor vs. full professor) it may no longer be possible to protect all respondents’ anonymity. The solution is to form a small number of relatively large categories such that it is unlikely that there will be fewer than five respondents when all these categories are crossed with each other. If the analyses reveal that certain groups of respondents are too small, then the presentation of the results should be adjusted. For example, the means can be broken down once by gender identity and once by race/ethnicity, but not by gender identity and race/ethnicity.

To address the anonymity issue, we recently conducted a climate survey in which we only asked two demographic questions: “Do you identify as a man, yes or no?” and “Do you identify as a member of a marginalized group (unrelated to gender identity), yes or no?” We justified the use of these questions in the survey by explaining that the gender identity question was asked in this way because research shows that individuals who identify as men are less often the target of sexual assault than those who do not identify as men. We also provided a brief definition of “marginalized groups.”

Climate surveys have two goals. They should provide an accurate reading of respondents’ perception of the social climate and they should suggest concrete action steps about initiatives to be implemented (see Table 1 for a list of constructs that are frequently measured in climate surveys). To achieve the first goal the climate survey should contain at least one question about the overall climate and several questions about specific feelings related to the social climate. In addition, the survey should assess sense of belonging, as well as mental and physical health. Most climate surveys also include items about respondents’ experiences of discrimination and their intention to remain in the institution (sometimes referred to as “persistence”). Finally, the climate survey may assess a variety of other constructs such as respondents’ perception of the institution’s commitment to diversity, their personal values related to diversity, their level of discomfort being around people from other social groups (sometimes referred to as “intergroup anxiety”) and self-reported inclusive behaviors.

TABLE 1 . List of constructs that are frequently measured in climate surveys.

To achieve the second goal–identification of concrete action steps about initiatives to be implemented–the climate survey needs to contain questions that help identify potential target behaviors, potential target audiences, and the barriers and benefits. It is helpful to ask respondents about the groups of individuals that have the most negative impact on their experience in the Department. It is further important to get information about the behaviors that should be discouraged (behaviors that negatively affect the well-being of individuals belonging to marginalized groups) and behaviors that should be promoted in the future (behaviors that make members of marginalized groups feel welcome and included). Once these behaviors have been identified, which will likely be the case after the climate survey has been implemented once or twice in a given Department, it is even possible to include items that measure the barriers and benefits for these behaviors.

As will be described in the next section, one of the most effective ways to promote an inclusive climate is to make salient that inclusion is a social norm. People’s perceptions of social norms are determined in part by what their peers think and do, and it is thus important for a climate survey to assess how common inclusive beliefs and behaviors are (the so-called “descriptive norms”). The above-mentioned items measuring personal values related to diversity partially achieve this purpose. In addition, consider including in the climate survey items that measure respondents’ support for their department’s pro-diversity initiatives, their enjoyment of diversity, their self-reported inclusive behaviors, and their perceptions of the proportion of peers who behave in an inclusive, non-discriminatory way. The survey shown in the online Supplemental Material contains additional items that assess respondent’s perceptions of the extent to which it is “descriptively normative” to be inclusive. It can be highly effective to create persuasive messages in which the average response to these items is reported. For example, if respondents from marginalized groups answered that a numerical majority of their peers engage in inclusive behaviors and abstain from engaging in discriminatory behaviors, then obviously inclusion is a social norm. As will be explained in more detail in the next section, such “social norms messages” have been shown to promote the occurrence of inclusive behaviors and to promote a welcoming social climate, as long as is it acknowledged that acts of bigotry and exclusion still occur and it is communicated that the department or college will continue its diversity efforts until members of marginalized groups feel just as welcome and included as members of nonmarginalized groups.

Overview of Recently Developed Initiatives to Promote Inclusion

A few new approaches to promoting inclusion stand out among the rest. Rather than taking a traditional approach of reducing biased attitudes or raising awareness about persistent prejudice, many of these new initiatives focus on changing behavior. We will discuss in detail two types of interventions, one involving social norms messaging and the other promoting intergroup contact. We will also briefly describe the “pride and prejudice” approach to inclusion in academia. While only some of these initiatives have been specifically tested as ways to improve inclusion in STEM settings, all of them can easily be applied in these settings as they show promise for increasing inclusion in academic contexts.

Social Norms Messaging

Social norms influence behavior in a way that is consistent with desirable normative behavior ( McDonald and Crandall, 2015 ). Social norms messaging–persuasive messages about social norms–has recently emerged as a promising method for promoting inclusion ( Murrar et al., 2020 ). There are two main types of social norms, descriptive (i.e., what behaviors are common among a group of people) and injunctive (i.e., what is approved of among a group of people; Cialdini et al., 1990 ). Interventions that utilize messages about descriptive social norms have been used for many years and have been proven successful in a variety of areas (e.g., energy conservation, binge drinking among college students; Frey and Rogers, 2014 ; Lewis and Neighbors, 2006 ; Miller and Prentice, 2016 ). Such interventions influence behavior by changing or correcting individuals’ perceptions of their peers’ behavior, which is particularly powerful because people rely on each other and their environment for guidance on how to behave ( Rhodes et al., 2020 ).

Prejudice is often blamed on conformity to social norms ( Crandall et al., 2002 ). However, researchers have started to employ social norms messaging as a way to improve intergroup outcomes. For example, Murrar and colleagues (2020) developed two interventions that targeted peoples’ perceptions of their peers’ pro-diversity attitudes and inclusive behaviors (i.e., descriptive norms) and tested them within college classrooms. One intervention involved placing posters inside classrooms that communicated that most students at the university embrace diversity and welcome people from all backgrounds into the campus community. The other intervention consisted of a short video that portrayed interviews with students who expressed pro-diversity attitudes and intentions to behave inclusively. The video also showed interviews with diversity and inclusion experts who reported that the blatant acts of discrimination, which undoubtedly occur on campus and affect the well-being of students from marginalized groups, are perpetrated by a numerical minority of students. The interventions led to an increase in inclusive behaviors in all students, an enhanced sense of belonging among students from marginalized groups, and a reduction in the achievement gap (see Figure 2 ). Note that Murrar and colleagues’ Experiment 6 specifically examined the effectiveness of the intervention in STEM courses.

FIGURE 2 . Effect of condition on outcomes of interest for students from marginalized groups in experiment 5 of Murrar et al., 2020 . Note: The authors compared their social norms intervention to a no-exposure control group and an intervention highlighting bias.

Another intervention strategy that successfully utilized social norms messaging and improved the well-being of college students from marginalized groups was developed and tested by Brauer et al. (in press) . Using the steps to designing successful behavior interventions described earlier, these authors identified the target behavior (inclusive classroom behavior), target audience (White university students), barriers (perceptions of peer inclusive behaviors and lack of motivation to behave inclusively) and benefits (importance of working and communicating well with a diverse group of people for others and oneself) to design a theoretically informed intervention strategy: a one-page document to be included in course syllabi. The document included not only social norms messaging about students’ inclusive behaviors (descriptive norms), but also statements by the university leadership endorsing diversity (highlighting injunctive norms, Rhodes et al., 2020 ), a short text about the benefits of learning to behave inclusively (inspired by utility value interventions; Harackiewicz et al., 2016 ) and concrete behavioral recommendations (inspired by SMART goals; Wade, 2009 ). This approach of applying multiple theories in an intervention creates “theoretical synergy,” which refers to the situation where the elements of a multifaceted intervention mutually reinforce each other and thus become particularly effective ( Paluck et al., 2021 ).

Posters, videos, and syllabi documents are just a few ways through which social norms messaging can be implemented in classrooms to promote inclusive behaviors and improve the classroom climate for students belonging to marginalized groups. Social norms messaging can also be considered a cheap, easy, and flexible way for instructors to shape students’ norm perceptions of a classroom early on and establish expectations for inclusive behavior. When inclusive norms are established early, students are more likely to abide by them.

Intergroup Contact

The intergroup contact hypothesis, first proposed by Allport (1954) , has been the basis for many prejudice reduction strategies. The theory suggests that contact between members of different groups can cause prejudice reduction if there is equal status between the groups and they are in pursuit of common goals. Intergroup contact has rarely been tested as a means to promote inclusion in STEM settings, but some recent experiments involving interventions that utilize intergroup contact have shown promise in their ability to promote inclusion and reduce the occurrence of discriminatory behavior.

Described earlier in this paper, Mousa (2020) , Scacco and Warren (2018) are examples for how intergroup contact can promote inclusion in academic and non-academic settings. Similarly, Lowe (2021) randomly assigned men from different castes in India to be cricket teammates and compete against other teams. Lowe examined one to three weeks after the end of the cricket league whether intergroup contact experienced through being on a mixed-caste sports team and having opponents from different castes would affect willingness to interact with people from other castes, ingroup favoritism, and efficiency and trust in trading goods that had monetary value. Whereas collaborative contact improved the three outcomes, adversarial contact (i.e., contact through being opponents to different caste members) resulted in the opposite effects.

Lowe (2021) , Mousa (2020) , Scacco and Warren (2018) intergroup contact interventions show the importance of providing long-term intergroup interactions when trying to reduce discriminatory behavior and promote inclusive behavior. In particular, if the interactions involve being on the same teams and sharing common goals, engagement in inclusive behaviors and decision-making will be a likely outcome. Note that none of these interventions altered people’s attitudes. Attitude change is not a precondition for behavior change to occur. Classroom instructors in STEM can leverage insights from the research on intergroup contact by incorporating numerous opportunities for intergroup interaction in the classroom as well as in assignments and projects throughout the course. One easy way to achieve this goal is to form project groups randomly rather than allowing students to form groups themselves.

Pride and Prejudice

A new strategy for promoting inclusion in academia is the “Pride and Prejudice” approach, which has been created to address the complexity of marginalized identities ( Brannon and Lin, 2020 ). “Pride” refers to the acknowledgment of the history and culture of students from marginalized groups (e.g., classes, groups, and spaces dedicated to marginalized groups), whereas “prejudice” refers to initiatives that address the discrimination experienced by students from these groups. The key idea of this approach is that identity is a source for both pride and prejudice for those belonging to marginalized groups. Both supporting marginalized groups and addressing instances of prejudice are pathways to inclusion in academic settings.

Support for the “Pride and Prejudice” approach comes from Brannon and Lin (2020) analysis of demands made by students from 80 United States colleges and universities compiled in 2016 (see thedemands.org ) following a series of racial discrimination protests regarding what changes they wanted to see on their campuses ( Hartocollis and Bidgood, 2015 ). Their analysis revealed that most demands referenced pride experiences and prejudice experiences. Brannon and Lin also analyzed longitudinal data to assess for pride and prejudice experiences among college students in 27 colleges and universities and the relationships of these experiences with several intergroup outcomes. The results showed that pride and prejudice experiences impact students’ sense of belonging via ingroup and outgroup closeness. The findings suggest that to promote inclusion in academia, it may be best to create settings that support and celebrate the cultures of marginalized groups in addition to having practices in place to mitigate prejudice and discrimination toward marginalized groups.

A variety of strategies have been developed to reduce the achievement gap (e.g., self-affirmation interventions, promoting growth-mindsets, etc…). However, many of these strategies are meant to help students from marginalized students succeed in an environment that is not inclusive. Instead of placing the burden on students from marginalized groups (i.e., teaching them how to deal with the exclusion and discrimination), researchers and practitioners should shift their focus to creating inclusive academic environments. The research discussed in this article provides a framework for developing successful interventions to promote diversity and inclusion. Such an approach may hold the key to improving the experiences of individuals from marginalized groups by targeting the behaviors that can make them feel more recognized, respected, welcomed, and valued. In the long run this will be the most effective way to raise the success and graduation of students from marginalized groups in STEM.

Author Contributions

GM, NI, and MB participated in the writing and revision of the paper. MB approved the paper for submission.

Conflict of Interest

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.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/feduc.2021.668250/full#supplementary-material

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Keywords: higher educaction, STEM–science technology engineering mathematics, diversity, inclusion, behavior change, intervention

Citation: Moreu G, Isenberg N and Brauer M (2021) How to Promote Diversity and Inclusion in Educational Settings: Behavior Change, Climate Surveys, and Effective Pro-Diversity Initiatives. Front. Educ. 6:668250. doi: 10.3389/feduc.2021.668250

Received: 15 February 2021; Accepted: 23 June 2021; Published: 08 July 2021.

Reviewed by:

Copyright © 2021 Moreu, Isenberg and Brauer. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Markus Brauer, [email protected]

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Research Article

Understanding the Influence of Race/Ethnicity, Gender, and Class on Inequalities in Academic and Non-Academic Outcomes among Eighth-Grade Students: Findings from an Intersectionality Approach

* E-mail: [email protected]

Affiliation Centre on Dynamics of Ethnicity, Department of Social Statistics, University of Manchester, Manchester, United Kingdom

Affiliation Australian National University, Acton, Australia

• Laia Bécares, 
• Naomi Priest

• Published: October 27, 2015
• https://doi.org/10.1371/journal.pone.0141363
• Reader Comments

Socioeconomic, racial/ethnic, and gender inequalities in academic achievement have been widely reported in the US, but how these three axes of inequality intersect to determine academic and non-academic outcomes among school-aged children is not well understood. Using data from the US Early Childhood Longitudinal Study—Kindergarten (ECLS-K; N = 10,115), we apply an intersectionality approach to examine inequalities across eighth-grade outcomes at the intersection of six racial/ethnic and gender groups (Latino girls and boys, Black girls and boys, and White girls and boys) and four classes of socioeconomic advantage/disadvantage. Results of mixture models show large inequalities in socioemotional outcomes (internalizing behavior, locus of control, and self-concept) across classes of advantage/disadvantage. Within classes of advantage/disadvantage, racial/ethnic and gender inequalities are predominantly found in the most advantaged class, where Black boys and girls, and Latina girls, underperform White boys in academic assessments, but not in socioemotional outcomes. In these latter outcomes, Black boys and girls perform better than White boys. Latino boys show small differences as compared to White boys, mainly in science assessments. The contrasting outcomes between racial/ethnic and gender minorities in self-assessment and socioemotional outcomes, as compared to standardized assessments, highlight the detrimental effect that intersecting racial/ethnic and gender discrimination have in patterning academic outcomes that predict success in adult life. Interventions to eliminate achievement gaps cannot fully succeed as long as social stratification caused by gender and racial discrimination is not addressed.

Citation: Bécares L, Priest N (2015) Understanding the Influence of Race/Ethnicity, Gender, and Class on Inequalities in Academic and Non-Academic Outcomes among Eighth-Grade Students: Findings from an Intersectionality Approach. PLoS ONE 10(10): e0141363. https://doi.org/10.1371/journal.pone.0141363

Editor: Emmanuel Manalo, Kyoto University, JAPAN

Received: June 10, 2015; Accepted: October 6, 2015; Published: October 27, 2015

Copyright: © 2015 Bécares, Priest. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Data Availability: All ECLS-K Kindergarten-Eighth Grade Public-use File are available from the National Center for Education Statistics website ( https://nces.ed.gov/ecls/dataproducts.asp#K-8 ).

Funding: This work was funded by an ESRC grant (ES/K001582/1) and a Hallsworth Research Fellowship to LB.

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

Introduction

The US racial/ethnic academic achievement gap is a well-documented social inequality [ 1 ]. National assessments for science, mathematics, and reading show that White students score higher on average than all other racial/ethnic groups, particularly when compared to Black and Hispanic students [ 2 , 3 ]. Explanations for these gaps tend to focus on the influence of socioeconomic resources, neighborhood and school characteristics, and family composition in patterning socioeconomic inequalities, and on the racialized nature of socioeconomic inequalities as key drivers of racial/ethnic academic achievement gaps [ 4 – 10 ]. Substantial evidence documents that indicators of socioeconomic status, such as free or reduced-price school lunch, are highly predictive of academic outcomes [ 2 , 3 ]. However, the relative contribution of family, neighborhood and school level socioeconomic inequalities to racial/ethnic academic inequalities continues to be debated, with evidence suggesting none of these factors fully explain racial/ethnic academic achievement gaps, particularly as students move through elementary school [ 11 ]. Attitudinal outcomes have been proposed by some as one explanatory factor for racial/ethnic inequalities in academic achievement [ 12 ], but differences in educational attitudes and aspirations across groups do not fully reflect inequalities in academic assessment. For example, while students of poorer socioeconomic status have lower educational aspirations than more advantaged students [ 13 ], racial/ethnic minority students report higher educational aspirations than White students, particularly after accounting for socioeconomic characteristics [ 14 – 16 ]. Similarly, while socio-emotional development is considered highly predictive of academic achievement in school students, some racial/ethnic minority children report better socio-emotional outcomes than their White peers on some indicators, although findings are inconsistent [ 17 – 22 ].

In addition to inequalities in academic achievement, racial/ethnic and socioeconomic inequalities also exist across measures of socio-emotional development [ 23 – 26 ]. And as with academic achievement, although socioeconomic factors are highly predictive of socio-emotional outcomes, they do not completely explain racial/ethnic inequalities in school-related outcomes not focused on standardized assessments [ 11 ].

Further complexity in understanding how academic and non-academic outcomes are patterned by socioeconomic factors, and how this contributes to racial/ethnic inequalities, is added by the multi-dimensional nature of socioeconomic status. Socioeconomic status is widely recognized as comprising diverse factors that operate across different levels (e.g. individual, household, neighborhood), and influence outcomes through different causal pathways [ 27 ]. The lack of interchangeability between measures of socioeconomic status within and between levels (e.g. income, education, occupation, wealth, neighborhood socioeconomic characteristics, or past socioeconomic circumstances) is also well established, as is the non-equivalence of measures between racial/ethnic groups [ 27 ]. For example, large inequalities have been reported across racial/ethnic groups within the same educational level, and inequalities in wealth have been shown across racial/ethnic that have similar income. It is therefore imperative that studies consider these multiple dimensions of socioeconomic status so that critical social gradients across the entire socioeconomic spectrum are not missed [ 27 ], and racial/ethnic inequalities within levels of socioeconomic status are adequately documented. It is also important that differences in school outcomes are considered across levels of socioeconomic status within and between racial/ethnic groups, so that the influence of specific socioeconomic factors on outcomes within specific racial/ethnic groups can be studied [ 28 ]. However, while these analytic approaches have been identified as research priorities in order to enhance our understanding of the complex ways in which socioeconomic status and race/ethnicity intersect to influence school outcomes, research that operationalizes these recommendations across academic and non-academic outcomes of school children is scant.

In addition to the complexity that arises from race/ethnicity, socioeconomic status, and intersections between them, different patterns in academic and non-academic outcomes by gender have also received longstanding attention. Comparisons across gender show that, on average, boys have higher scores in mathematics and science, whereas girls have higher scores in reading [ 2 , 3 , 29 ]. In contrast to explanations for socioeconomic inequalities, gender differences have been mainly attributed to social conditioning and stereotyping within families, schools, communities, and the wider society [ 30 – 35 ]. These socialization and stereotyping processes are also highly relevant determining factors in explaining racial/ethnic academic and non-academic inequalities [ 35 , 36 ], as are processes of racial discrimination and stigmatization [ 37 , 38 ]. Gender differences in academic outcomes have been documented as differently patterned across racial/ethnic groups and across levels of socioeconomic status. For example, gender inequalities in math and science are largest among White and Latino students, and smallest among Asian American and African American students [ 39 – 43 ], while gender gaps in test scores are more pronounced among socioeconomically disadvantaged children [ 44 , 45 ]. In terms of attitudes towards math and sciences, gender differences in attitudes towards math are largest among Latino students, but gender differences in attitudes towards science are largest among White students [ 39 , 40 ]. Gender differences in socio-developmental outcomes and in non-cognitive academic outcomes, across race/ethnicity and socio-economic status, have received far less attention; studies that consider multiple academic and non-academic outcomes among school aged children across race/ethnicity, socioeconomic status and gender are limited in the US and internationally.

Understanding how different academic and non-academic outcomes are differently patterned by race/ethnicity, socio-economic status, and gender, including within and between group differences, is an important research area that may assist in understanding the potential causal pathways and explanations for observed inequalities, and in identifying key population groups and points at which interventions should be targeted to address inequalities in particular outcomes [ 28 , 46 ]. Not only is such knowledge critical for population level policy and/or local level action within affected communities, but failing to detect potential factors for interventions and potential solutions is argued as reinforcing perceptions of the unmodifiable nature of inequality and injustice [ 46 ].

Notwithstanding the importance of documenting patterns of inequality in relation to a particular social identity (e.g. race/ethnicity, gender, class), there is increasing acknowledgement within both theoretical and empirical research of the need to move beyond analyzing single categories to consider simultaneous interactions between different aspects of social identity, and the impact of systems and processes of oppression and domination (e.g., racism, classism, sexism) that operate at the micro and macro level [ 47 , 48 ]. Such intersectional approaches challenge practices that isolate and prioritize a single social position, and emphasize the potential of varied inter-relationships of social identities and interacting social processes in the production of inequities [ 49 – 51 ]. To date, exploration of how social identities interact in an intersectional way to influence outcomes has largely been theoretical and qualitative in nature. Explanations offered for interactions between privileged and marginalized identities, and associated outcomes, include family and teacher socialization of gender performance (e.g. math and science as male domains, verbal and emotional skills as female), as well as racialized stereotypes and expectations from teachers and wider society regarding racial/ethnic minorities that are also gendered (e.g. Black males as violent prone and aggressive, Asian females as submissive) [ 52 – 57 ]. That is, social processes that socialize and pattern opportunities and outcomes are both racialized and gendered, with racism and sexism operating in intersecting ways to influence the development and achievements of children and youth [ 58 – 60 ]. Socioeconomic status adds a third important dimension to these processes, with individuals of the same race/ethnicity and gender having access to vastly different resources and opportunities across levels of socioeconomic status. Moreover, access to resources as well as socialization experiences and expectations differ considerably by race and gender within the same level of socio-economic status. Thus, neither gender nor race nor socio-economic status alone can fully explain the interacting social processes influencing outcomes for youth [ 27 , 28 ]. Disentangling such interactions is therefore an important research priority in order to inform intervention to address inequalities at a population level and within local communities.

In the realm of quantitative approaches to the study of inequality, studies often examine separate social identities independently to assess which of these axes of stratification is most prominent, and for the most part do not consider claims that the varied dimensions of social stratification are often juxtaposed [ 56 , 61 ]. A pressing need remains for quantitative research to consider how multiple forms of social stratification are interrelated, and how they combine interactively, not just additively, to influence outcomes [ 46 ]. Doing so enables analyses that consider in greater detail the representation of the embodied positions of individuals, particularly issues of multiple marginalization as well as the co-occurrence of some form of privilege with marginalization [ 46 ]. It is important to note that the languages of statistical interaction and of intersectionality need to be carefully distinguished (e.g. intersectional additivity or additive assumptions, versus additive scale and cross-product interaction terms) to avoid misinterpretation of findings, and to ensure appropriate application of statistical interaction to enable the description of outcome measures for groups of individuals at each cross-stratified intersection [ 46 ]. Ultimately this will provide more nuanced and realistic understandings of the determinants of inequality in order to inform intervention strategies.

This study fills these gaps in the literature by examining inequalities across several eighth grade academic and non-academic outcomes at the intersection of race/ethnicity, gender, and socioeconomic status. It aims to do this by: identifying classes of socioeconomic advantage/disadvantage from kindergarten to eighth grade; then ascertaining whether membership into classes of socioeconomic advantage/disadvantage differ for racial/ethnic and gender groups; and finally, by contrasting academic and non-academic outcomes at the intersection of race/ethnicity, gender and socioeconomic advantage/disadvantage. Intersecting identities of race/ethnicity, gender, and socioeconomic characteristics are compared to the reference group of White boys in the most advantaged socioeconomic category, as these are the three identities (male, White, socioeconomically privileged) that experience the least marginalization when compared to racial/ethnic and gender minority groups in disadvantaged socioeconomic positions.

This study used data on singleton children from the Early Childhood Longitudinal Study—Kindergarten (ECLS-K). The ECLS-K employed a multistage probability sample design to select a nationally representative sample of children attending kindergarten in 1998–99. In the base year the primary sampling units (PSUs) were geographic areas consisting of counties or groups of counties. The second-stage units were schools within sampled PSUs. The third- and final-stage units were children within schools [ 62 ]. Analyses were conducted on data collected from direct child assessments, as well as information provided by parents and school administrators.

Ethics Statement

This article is based on the secondary analysis of anonymized and de-identified Public-Use Data Files available to researchers via the Inter-University Consortium for Political and Social Research (ICPSR). Human participants were not directly involved in the research reported in this article; therefore, no institutional review board approval was sought.

Outcome Variables.

Eight outcome variables, all assessed in eighth grade, were selected to examine the study aims: two measures relating to non-cognitive academic skills (perceived interest/competence in reading, and in math); three measures capturing socioemotional development (internalizing behavior, locus of control, self-concept); and three measures of cognitive skills (math, reading and science assessment scores).

For the eighth-grade data collection, children completed the 16-item Self Description Questionnaire (SDQ) II [ 63 ], where they provided self-assessments of their academic skills by rating their perceived competence and interest in English and mathematics. The SDQ also asked children to report on problem behaviors with which they might struggle. Three subscales were produced from the SDQ items: The SDQ Perceived Interest/Competence in Reading, including four items on grades in English and the child’s interest in and enjoyment of reading. The SDQ Perceived Interest/Competence in Math, including four items on mathematics grades and the child’s interest in and enjoyment of mathematics. And the SDQ Internalizing Behavior subscale, which includes eight items on internalizing problem behaviors such as feeling sad, lonely, ashamed of mistakes, frustrated, and worrying about school and friendships [ 62 ].

The Self-Concept and Locus of Control scales ask children about their self-perceptions and the amount of control they have over their own lives. These scales, adopted from the National Education Longitudinal Study of 1988, asked children to indicate the degree to which they agreed with 13 statements (seven items in the Self-Concept scale, and six items in the Locus of Control Scale) about themselves, including “I feel good about myself,” “I don’t have enough control over the direction my life is taking,” and “At times I think I am no good at all.” Responses ranged from “strongly agree” to “strongly disagree.” Some items were reversed coded so that higher scores indicate more positive self-concept and a greater perception of control over one’s own life. The seven items in the Self-Concept scale, and the six items in the Locus of Control were standardized separately to a mean of zero and a standard deviation of 1. The scores of each scale are an average of the standardized scores [ 62 ].

Academic achievement in reading, mathematics and science was measured with the eighth-grade direct cognitive assessment battery [ 62 ].

Children were given separate routing assessment forms to determine the level (high/low) of their reading, mathematics, and science assessments. The two-stage cognitive assessment approach was used to maximize the accuracy of measurement and reduce administration time by using the child’s responses from a brief first-stage routing form to select the appropriate second-stage level form. First, children read items in a booklet and recorded their responses on an answer form. These answer forms were then scored by the test administrator. Based on the score of the respective routing forms, the test administrator then assigned a high or low second-stage level form of the reading and mathematics assessments. For the second-stage level tests, children read items in the assessment booklet and recorded their responses in the same assessment booklet. The routing tests and the second-stage tests were timed for 80 minutes [ 62 ]. The present analyses use the standardized scores (T-scores), allowing relative comparisons of children against their peers.

Individual and Contextual Disadvantage Variables.

Latent Class Analysis, described in greater detail below, was used to classify students into classes of individual and contextual advantage or disadvantage. Nine constructs, measuring characteristics at the individual-, school-, and neighborhood-level, were captured using 42 dichotomous variables measured across the different waves of the ECLS-K.

Individual-level variables captured household composition, material disadvantage, and parental expectations of the children’s success. Measures included whether the child lived in a single-parent household at kindergarten, first, third, fifth and eighth grades; whether the household was below the poverty threshold level at kindergarten, fifth and eighth grades; food insecurity at kindergarten, first, second and third grades; and parental expectations of the child’s academic achievement (categorized as up to high school and more than high school) at kindergarten, first, third, fifth and eighth grades. An indicator of whether parents had moved since the previous interview (measured at kindergarten, first, third, fifth and eighth grades) was included to capture stability in the children’s life. A household-level composite index of socioeconomic status, derived by the National Center for Education Statistics, was also included at kindergarten, first, third, fifth and eighth grades. This measure captured the father/male guardian’s education and occupation, the mother/female guardian’s education and occupation, and the household income. Higher scores reflect higher levels of educational attainment, occupational prestige, and income. In the present analyses, the socioeconomic composite index was categorized into quintiles and further divided into the lowest first and second quintiles, versus the third, fourth and fifth quintiles.

Two variables measured the school-level environment: percentage of students eligible for free school meals, and percentage of students from a racial/ethnic background other than White non-Hispanic. These two variables were dichotomized as more than or equal to 50% of students belonging to each category. Both variables were measured in the kindergarten, first, third, fifth and eighth grade data collections.

To capture the neighborhood environment, a variable was included which measured the level of safety of the neighborhood in kindergarten, first, third, fifth and eighth grades. Parents were asked “How safe is it for children to play outside during the day in your neighborhood?” with responses ranging from 1, not at all safe, to 3, very safe. For the present analyses, response categories were recoded into 1 “not at all and somewhat safe,” and 0 “very safe.”

Predictor Variables.

The race/ethnicity and gender of the children were assessed during the parent interview. In order to empirically measure the intersection between race/ethnicity and gender in the classes of disadvantage, a set of six dummy variables were created that combined racial/ethnic and gender categories into White boys, White girls, Black boys, Black girls, Latino boys, and Latina girls.

Statistical Analyses

This study used the manual 3-step approach in mixture modeling with auxiliary variables [ 64 , 65 ] to independently evaluate the relationship between the predictor auxiliary variables (the combined race/ethnicity and gender groups), the latent class variable of advantage/disadvantage, and the outcome (non-cognitive skills, socioemotional development, cognitive assessments). This is a data-driven, mixture modelling technique which uses indicator variables (in this case the variables described under Individual and Contextual Disadvantage Variables section) to identify a number of latent classes. It also includes auxiliary information in the form of covariates (the race/ethnicity and gender combinations described under Predictor Variables) and distal outcomes (the eight outcome variables), to better explore the relationships between the characteristics that make up the latent classes, the predictors of class membership, and the associated consequences of membership into each class.

The first step in the 3-step procedure is to estimate the measurement part of the joint model (i.e., the latent class model) by creating the latent classes without adding covariates. Latent class analyses first evaluated the fit of a 2-class model, and systematically increased the number of classes in subsequent models until the addition of latent classes did not further improve model fit. For each model, replication of the best log-likelihood was verified to avoid local maxima. To determine the optimal number of classes, models were compared across several model fit criteria. First, the sample-size adjusted Bayesian Information Criterion (BIC) [ 66 ] was evaluated; lower relative BIC values indicate improved model fit. Given that the BIC criterion tends to favor models with fewer latent classes [ 67 ], the Lo, Mendell, and Rubin likelihood ratio test (LMR-LRT) statistic [ 68 ] was also considered. The LMR-LRT can be used in mixture modeling to compare the fit of the specified class solution ( k -class model) to a model with fewer classes ( k -1 class model). A non-significant chi-square value suggests that a model with one fewer class is preferred. Entropy statistics, which measure the separation of the classes based on the posterior class membership probabilities, were also examined; entropy values approaching 1 indicate clear separation between classes [ 69 ].

After determining the latent class model in step 1, the second step of the analyses used the latent class posterior distribution to generate a nominal variable N , which represented the most likely class [ 64 ]. During the third step, the measurement error for N was accounted for while the model was estimated with the outcomes and predictor auxiliary variables [ 64 ]. The last step of the analysis examined whether race/ethnic and gender categories predict class membership, and whether class membership predicts the outcomes of interest.

All analyses were conducted using MPlus v. 7.11 [ 70 ], and used longitudinal weights to account for differential probabilities of selection at each sampling stage and to adjust for the effects of non-response. A robust standard error estimator was used in MPlus to account for the clustering of observations in the ECLS-K.

Four distinct classes of advantage/disadvantage were identified in the latent class analysis (see Table 1 ).

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https://doi.org/10.1371/journal.pone.0141363.t001

Class characteristics are shown in Table A in S1 File . Trajectories of advantage and disadvantage were stable across ECLS-K waves, so that none of the classes identified changed in individual and contextual characteristics across time. The largest proportion of the sample (47%; Class 3: Individually and Contextually Wealthy) lived in individual and contextual privilege, with very low proportions of children in socioeconomic deprived contexts. A class representing the opposite characteristics (children living in individually- and contextually-deprived circumstances) was also identified in the analyses (19%; Class 1: Individually and Contextually Disadvantaged). Class 1 had the highest proportion of children living in socioeconomic deprivation, attending schools with more than 50% racial/ethnic minority students, and living in unsafe neighborhoods, but did not have a high proportion of children with the lowest parental expectations. Class 4 (19%; Individually Disadvantaged, Contextually Wealthy) had the highest proportion of children with the lowest parental expectations (parents reporting across waves that they expected children to achieve up to a high school education). Class 4 (Individually Disadvantaged, Contextually Wealthy) also had high proportions of children living in individual-level socioeconomic deprivation, but had low proportions of children attending a school with over 50% of children eligible for free school meals. It also had relatively low proportions of children living in unsafe neighborhoods and low proportions of children attending diverse schools, forming a class with a mixture of individual-level deprivation, and contextual-level advantage. The last class was composed of children who lived in individually-wealthy environments, but who also lived in unsafe neighborhoods and attended diverse schools where more than 50% of pupils were eligible for free school meals (13%; Class 2: Individually Wealthy, Contextually Disadvantaged; see Table A in S1 File ).

The combined intersecting racial/ethnic and gender characteristics yielded six groups consisting of White boys (n = 2998), White girls (n = 2899), Black boys (n = 553), Black girls (n = 560), Latino boys (n = 961), and Latina girls (n = 949). All pairs containing at least one minority status of either race/ethnicity or gender (e.g., Black boys, Black girls, Latino boys, Latina girls) were more likely than White boys to be assigned to the more disadvantaged classes, as compared to being assigned to Class 3, the least disadvantaged (see Table B in S1 File ).

Racial/Ethnic and Gender Differences in Eighth-Grade Academic Outcomes

Table 2 shows broad patterns of intersecting racial/ethnic and gender inequalities in academic outcomes, although interesting differences emerge across racial/ethnic and gender groups. Whereas Black boys achieved lower scores than White boys across all classes on the math, reading and science assessments, this was not the case for Latino boys, who only underperformed White boys on the science assessment within the most privileged class (Class 3: Individually and Contextually Wealthy). Latina girls, in contrast, outperformed White boys on reading scores within Class 4 (Individually Disadvantaged, Contextually Wealthy), but scored lower than White boys on science and math assessments, although only when in the two most privileged classes (Class 3 and 4). For Black girls the effect of class membership was not as pronounced, and they had lower science and math scores than White boys across all but one instance.

https://doi.org/10.1371/journal.pone.0141363.t002

In general, the largest inequalities in academic outcomes across racial/ethnic and gender groups appeared in the most privileged classes. For example, results show no differences in math scores across racial/ethnic and gender categories within Class 4, the most disadvantaged class, but in all other classes that contain an element of advantage, and particularly in Class 3 (Individually and Contextually Wealthy), there are large gaps in math scores across racial/ethnic and gender groups, when compared to White boys. These patterns of heightened inequality in the most advantaged classes are similar for reading and science scores (see Table 2 ).

Racial/Ethnic and Gender Differences in Eighth-Grade Non-Academic Outcomes

Interestingly, racialized and gendered patterns of inequality observed in academic outcomes were not as stark in non-cognitive academic outcomes (see Table 3 ).

https://doi.org/10.1371/journal.pone.0141363.t003

Racial/ethnic and gender differences were small across socioemotional outcomes, and in fact, White boys were outperformed on several outcomes. Black boys scored lower than White boys on internalizing behavior and higher on self-concept within Classes 2 (Individually Wealthy, Contextually Disadvantaged) and 4 (Individually Disadvantaged, Contextually Wealthy), and Black girls scored higher than White boys on self-concept within Classes 2 and 3 (Individually Wealthy, Contextually Disadvantaged, and Individually and Contextually Wealthy, respectively). White and Latina girls, but not Black girls, scored higher than White boys on internalizing behavior (within Classes 3 and 4 for White girls, and within Classes 1 and 3 for Latina girls; see Table 3 ).

As with academic outcomes, most racial/ethnic and gender differences also emerged within the most privileged classes, and particularly in Class 3 (Individually and Contextually Wealthy), although in the case of perceived interest/competence in reading, White and Latina girls performed better than White boys. White girls also reported higher perceived interest/competence in reading than White boys in Class 4: Individually Disadvantaged, Contextually Wealthy.

This study set out to examine inequalities across several eighth grade academic and non-academic outcomes at the intersection of race/ethnicity, gender, and socioeconomic status. It first identified four classes of longstanding individual- and contextual-level disadvantage; then determined membership to these classes depending on racial/ethnic and gender groups; and finally compared non-cognitive skills, academic assessment scores, and socioemotional outcomes across intersecting gender, racial/ethnic and socioeconomic social positions.

Results show the clear influence of race/ethnicity in determining membership to the most disadvantaged classes. Across gender dichotomies, Black students were more likely than White boys to be assigned to all classes of disadvantage as compared to the most advantaged class, and this was particularly strong for the most disadvantaged class, which included elements of both individual- and contextual-level disadvantage. Latino boys and girls were also more likely than White boys to be assigned to all the disadvantaged classes, but the strength of the association was much smaller than for Black students. Whereas membership into classes of disadvantage appears to be more a result of structural inequalities strongly driven by race/ethnicity, the salience of gender is apparent in the distribution of academic assessment outcomes within classes of disadvantage. Results show a gendered pattern of math, reading and science assessments, particularly in the most privileged class, where girls from all ethnic/racial groups (although mostly from Black and Latino racial/ethnic groups) underperform White boys in math and science, and where Black boys score lower, and White girls higher, than White boys in reading.

With the exception of educational assessments, gender and racial/ethnic inequalities within classes are either not very pronounced or in the opposite direction (e.g. racial/ethnic and gender minorities outperform White males), but differences in outcomes across classes are stark. The strength of the association between race/ethnicity and class membership, and the reduced racial/ethnic and gender inequalities within classes of advantage and disadvantage, attest to the importance of socioeconomic status and wealth in explaining racial/ethnic inequalities; should individual and contextual disadvantage be comparable across racial/ethnic groups, racial/ethnic inequalities would be substantially reduced. This being said, most within-class differences were observed in the most privileged classes, showing that benefits brought about by affluence and advantage are not equal across racial/ethnic and gender groups. The measures of advantage and disadvantage captured in this study relate to characteristics afforded by parental resources, implying an intergenerational transmission of disadvantage, regardless of the presence of absolute adversity in childhood. This pattern of differential returns of affluence has been shown in other studies, which report that White teenagers benefit more from the presence of affluent neighbors than do Black teenagers [ 71 ]. Among adult populations, studies show that across several health outcomes, highly educated Black adults fare worse than White adults with the lowest education [ 72 ]. Intersectional approaches such as the one applied in this study reveal how power within gendered and racialized institutional settings operates to undermine access to and use of resources that would otherwise be available to individuals of advantaged classes [ 72 ]. The present study further contributes to this literature by documenting how, in a key stage of the life course, similar levels of advantage, but not disadvantage, lead to different academic outcomes across racial/ethnic and gender groups. These findings suggest that, should socioeconomic inequalities be addressed, and levels of advantage were similar across racial/ethnic and gender groups, systems of oppression that pattern the racialization and socialization of children into racial/ethnic and gender roles in society would still ensure that inequalities in academic outcomes existed across racial/ethnic and gender categories. In other words, racism and sexism have a direct effect on academic and non-academic outcomes among 8 th graders, independent of the effect of socioeconomic disadvantage on these outcomes. An important limitation of the current study is that although it uses a comprehensive measure of advantage/disadvantage, including elements of deprivation and affluence at the family, school and neighborhood levels through time, it failed to capture these two key causal determinants of racial/ethnic and gender inequality: experiences of racial and gender discrimination.

Despite this limitation, it is important to note that socioeconomic inequalities in the US are driven by racial and gender bias and discrimination at structural and individual levels, with race and gender discrimination exerting a strong influence on academic and non-academic inequalities. Racial discrimination, prevalent in the US and in other industrialized nations [ 38 , 73 ] determines differential life opportunities and resources across racial/ethnic groups, and is a crucial determinant of racial/ethnic inequalities in health and development throughout life and across generations [ 37 , 38 ]. In the context of this study’s primary outcomes within school settings, racism and racial discrimination experienced by both the parents and the children are likely to contribute towards explaining observed racial/ethnic inequalities in outcomes within classes of disadvantage. Gender discrimination—another system of oppression—is apparent in this study in relation to academic subjects socially considered as typically male or female orientated. For example, results show no difference between Black girls and White boys from the most advantaged class in terms of perceived interest and competence in math but, in this same class, Black girls score much lower than White boys in the math assessment. This difference, not explained by intrinsic or socioeconomic differences, can be contextualized as a consequence of experienced intersecting racial and gender discrimination. The consequences of the intersection between two marginalized identities are found throughout the results of this study when comparing across broad categorizations of race/ethnicity and gender, and in more detailed conceptualizations of minority status. Growing up Black, Latino or White in the US is not the same for boys and girls, and growing up as a boy or a girl in America does not lead to the same outcomes and opportunities for Black, Latino and White children as they become adults. With this study’s approach of intersectionality one can observe the complexity of how gender and race/ethnicity intersect to create unique academic and non-academic outcomes. This includes the contrasting results found for Black and Latino boys, when compared to White boys, which show very few examples of poorer outcomes among Latino boys, but several instances among Black boys. Results also show different racialization for Black and Latina girls. Latina girls, but not Black girls, report higher internalizing behavior than White boys, whereas Black girls, but not Latina girls, report higher self-concept than White boys. Black boys also report higher self-concept and lower internalizing behavior than White boys, findings that mirror research on self-esteem among Black adolescents [ 74 , 75 ]. In cognitive assessments, intersecting racial/ethnic and gender differences emerge across classes of disadvantage. For example, Black girls in all four classes score lower on science scores than White boys, but only Latina girls in the most advantaged class score lower than White boys. Although one can observe differences in the racialization of Black and Latino boys and girls across classes of disadvantage, findings about broad differences across Latino children compared to Black and White children should be interpreted with caution. The Latino ethnic group is a large, heterogeneous group, representing 16.7% of the total US population [ 76 ]. The Latino population is composed of a variety of different sub-groups with diverse national origins and migration histories [ 77 ], which has led to differences in sociodemographic characteristics and lived experiences of ethnicity and minority status among the various groups. Differences across Latino sub-groups are widely documented, and pooled analyses such as those reported here are masking differences across Latino sub-groups, and providing biased comparisons between Latino children, and Black and White children.

Poorer performance of girls and racial/ethnic minority students in science and math assessments (but not in self-perceived competence and interest) might result from stereotype threat, whereby negative stereotypes of a group influence their member’s performance [ 78 ]. Stereotype threat posits that awareness of a social stereotype that reflects negatively on one's social group can negatively affect the performance of group members [ 35 ]. Reduced performance only occurs in a threatening situation (e.g., a test) where individuals are aware of the stereotype. Studies show that early adolescence is a time when youth become aware of and begin to endorse traditional gender and racial/ethnic stereotypes [ 79 ]. Findings among youth parallel findings among adult populations, which show that adult men are generally perceived to be more competent than women, but that these perceptions do not necessarily hold for Black men [ 80 ]. These stereotypes have strong implications for interpersonal interactions and for the wider structuring of systemic racial/ethnic and gender inequalities. An example of the consequences of negative racial/ethnic and gender stereotypes as children grow up is the well-documented racial/ethnic and gender pay gap: women earn less than men [ 81 ], and racial/ethnic minority women and men earn less than White men [ 82 ].

In addition to the focus on intersectionality, a strength of this study is its person-centered methodological approach, which incorporates measures of advantage and disadvantage across individual and contextual levels through nine years of children’s socialization. Children live within multiple contexts, with risk factors at the family, school, and neighborhood level contributing to their development and wellbeing. Individual risk factors seldom operate in isolation [ 83 ], and they are often strongly associated both within and across levels [ 84 ]. All risk factors captured in the latent class analyses have been independently associated with increased risk for academic problems [ 10 , 71 , 85 , 86 ], and given that combinations of risk factors that cut across multiple domains explain the association between early risk and later outcomes better than any isolated risk factor [ 83 , 84 ], the incorporation of person-centered and intersectionality approaches to the study of racial/ethnic, gender, and socioeconomic inequalities across school outcomes provides new insight into how children in marginalized social groups are socialized in the early life course.

Conclusions

The contrasting outcomes between racial/ethnic and gender minorities in self-assessment and socioemotional outcomes, as compared to standardized assessments, provide support for the detrimental effect that intersecting racial/ethnic and gender discrimination have in patterning academic outcomes that predict success in adult life. Interventions to eliminate achievement gaps cannot fully succeed as long as social stratification caused by gender and racial discrimination is not addressed [ 87 , 88 ].

Supporting Information

S1 file. supporting tables..

Table A: Class characteristics. Table B: Associations between race/ethnicity and gender groups and assigned class membership (membership to Classes 1, 2 or 4 as compared to Class 3: Individually and Contextually Wealthy).

https://doi.org/10.1371/journal.pone.0141363.s001

Acknowledgments

This work was funded by an ESRC grant (ES/K001582/1) and a Hallsworth Research Fellowship to LB. Most of this work was conducted while LB was a visiting scholar at the Institute for Social Research, University of Michigan. She would like to thank them for hosting her visit and for the support provided.

Author Contributions

Conceived and designed the experiments: LB. Performed the experiments: LB. Analyzed the data: LB. Wrote the paper: LB NP.

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Teachers’ perspectives on students’ cultural diversity: a systematic literature review.

1. Introduction

2. materials and methods, 2.1. developing a review protocol, 2.1.1. search strategy, 2.1.2. data management, 2.2. data collection process, 2.3. risk of bias, 2.4. articles identified and selected, 3.1. study characteristics: settings, sample, dimensions of cultural diversity, 3.2. teachers’ perspectives on students’ diversity and inclusion, 3.3. strategies and practices developed by teachers, 3.4. teachers’ professional development related to students’ diversity and inclusion, 4. discussion, 4.1. teachers’ perspectives on student diversity and emerging inclusive practices, 4.2. culturally responsive teaching (crt), 4.3. intercultural education (ie), 5. conclusions, implications, limitations and future directions, supplementary materials, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Semião, D.; Mogarro, M.J.; Pinto, F.B.; Martins, M.J.D.; Santos, N.; Sousa, O.; Marchão, A.; Freire, I.P.; Lord, L.; Tinoca, L. Teachers’ Perspectives on Students’ Cultural Diversity: A Systematic Literature Review. Educ. Sci. 2023 , 13 , 1215. https://doi.org/10.3390/educsci13121215

Semião D, Mogarro MJ, Pinto FB, Martins MJD, Santos N, Sousa O, Marchão A, Freire IP, Lord L, Tinoca L. Teachers’ Perspectives on Students’ Cultural Diversity: A Systematic Literature Review. Education Sciences . 2023; 13(12):1215. https://doi.org/10.3390/educsci13121215

Semião, Daniela, Maria João Mogarro, Filipe Brás Pinto, Maria José D. Martins, Nelson Santos, Otilia Sousa, Amélia Marchão, Isabel Pimenta Freire, Lucio Lord, and Luís Tinoca. 2023. "Teachers’ Perspectives on Students’ Cultural Diversity: A Systematic Literature Review" Education Sciences 13, no. 12: 1215. https://doi.org/10.3390/educsci13121215

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• Mol Biol Cell
• v.31(25); 2020 Dec 1

Diversity through equity and inclusion: The responsibility belongs to all of us

James A. Olzmann

a Department of Molecular and Cell Biology, Department of Nutritional Sciences and Toxicology, and the Miller Institute for Basic Research in Science, University of California, Berkeley, Berkeley, CA 94720, and Chan Zuckerberg Biohub, San Francisco, CA 94158

Despite the recognized benefits of diversity and the decades of programs targeted at increasing diversity in science, technology, engineering, mathematics, and medicine, the underrepresentation of historically excluded groups continues due to persisting systemic inequalities. It is imperative that we reassess our current recruitment strategies and reimagine our campus and workplace environments to provide an inclusive and equitable culture that is free of institutional barriers, affording equal opportunities for each individual to succeed, thrive, and be their whole self. For too long this vision has been the fight of a heroic few, but it must become the fight of all in order to achieve true change. I am working toward, and look forward to, a future where contributing to diversity, equity, and inclusion is fully integrated into the core mission of our institutions and is an expectation for all of us.

“It is not our differences that divide us. It is our inability to recognize, accept, and celebrate those differences.” —Audre Lorde

I am honored and grateful to receive the Günter Blobel Early Career Award from the American Society for Cell Biology (ASCB). As a graduate student, I was fortunate enough to receive a travel award through the ASCB Minorities Affairs Committee that allowed me to attend the 2005 ASCB annual meeting. I recall my first meeting as both a daunting and exhilarating experience, and I excitedly attended talks from my science heroes. Over the years, the excitement of the annual meeting has never faded for me, and it now feels much like a reunion of friends and colleagues. I have also come to appreciate that ASCB is much more than just the annual meeting—it is a community of amazing cell biologists and it is our community! Everything has come full circle and I am privileged to be a member of the ASCB Minorities Affairs Committee, striving to pay it forward to students from diverse backgrounds and to cultivate an inclusive community where we all feel that we belong and are welcomed.

During a typical year, I would take this opportunity to discuss my path to become a cell biologist and how I became fascinated by lipid droplets, offer some tips for success in science and research, and perhaps wax poetic about the power of collaboration and mentorship. However, I think we can all agree that 2020 is not a typical year. Our world continues to reel from the impact of the COVID-19 pandemic and we are in the midst of the Black Lives Matter movement, the fire for this an anti-racism revolution rekindled by the needless and heartbreaking deaths of George Floyd, Breonna Taylor, and so many others. These events, which are just the most recent examples of all too common racially motivated violence, shine a light on our reality born out of a legacy of racism that permeates all aspects of our society. It is an understatement to say that our scientific community is not exempt from these systemic injustices, biases, and inequalities. To gain some small insight into the scope of the problem, we only need to look towards the #blackintheivory and #blackinivory hashtags on Twitter and recently published stories ( Simmons, 2020 ) that chronicle the lived experiences of our black colleagues—the microaggressions, implicit and explicit bias, tokenism, etc. It is with these current events as the backdrop that I focus this essay on the need for systemic change and the importance of achieving diversity through equity and inclusion in science, technology, engineering, mathematics, and medicine (STEMM).

WHAT IS DIVERSITY AND WHY SHOULD WE CARE ABOUT IT?

Diversity refers to differences within a group ( Gibbs, 2014 ), which can include, but are not limited to, differences in race, ethnicity, disability, nationality, socioeconomic stratum, gender, gender identity, and sexual orientation. Numerous studies agree that historically excluded and marginalized groups such as Blacks/African Americans, Latinx, American Indians, Alaska Natives, Native Hawaiians and other Pacific Islanders, women, and persons with disabilities continue to be underrepresented in STEMM ( National Academies of Sciences, Engineering, and Medicine, 2019 ; National Center for Science and Engineering Statistics, 2019 ). For example, in 2017, in the fields of science and engineering, women received 41% of research doctorates despite composing 51.5% of the population, and persons excluded because of their ethnicity or race (PEERs) received 11% of research doctorates despite composing 27% of the population ( National Center for Science and Engineering Statistics, 2019 ; Asai, 2020 ). Furthermore, there continues to be a lack of diversity in departmental faculty, editorial and scientific advisory boards, academic and industry leadership positions, award recipients, conference speaker lists, and the list goes on.

Why is diversity important? A common argument for diversity is a business model, that a variety of opinions and perspectives leads to more creative problem-solving and innovation ( National Institutes of Health, 2019 ). In addition, due to the changing demographics in the United States and the increase in historically excluded groups, diversity enables a field to better utilize the full talent pool. Perhaps this is the most compelling argument for some audiences, particularly those driven by achieving maximum market success. However, the moral argument is just as important, and it is often not given adequate weight. We should not simply value the increase in success brought by diversity, we should value an equitable and just system that provides equal access to opportunities, recognizes talent is distributed across all groups independent of identity, and acknowledges each of us are human beings deserving of dignity.

RECRUITMENT AND RETENTION: SUCCESSES AND FAILURES

Despite decades of programs aimed at increasing diversity, underrepresentation and exclusion remain issues at all levels of academia ( Gibbs et al. , 2016 ; NIH, 2019 ). Excuses are easy to find, and often assumptions that have been thoroughly debunked are raised as explanations for exclusion, such as that there are insufficient numbers of qualified candidates or that PEERs have less interest in scientific research ( Poodry and Asai, 2018 ). These excuses avoid blame and are the easy way out. The path that scientists from well-represented groups and in positions of power must take is difficult because it requires facing the reality of why underrepresentation persists, acknowledging biases and contributing to a system that perpetuates inequities, and implementing innovative solutions to overcome the problem. The problem is not insurmountable, but it requires making a choice to do the work required to solve it.

Although the rate of progress continues to be glacial, there have been increases in representation of historically excluded groups at the bachelor’s and doctoral degree levels. We should celebrate these hard-won successes! Some of these successes are due to terrific programs directed at the recruitment and persistence of students at the undergraduate level ( Estrada et al. , 2016 ), such as the University of California (UC) Berkeley Biology Scholars program ( Matsui, 2018 ), the University of Maryland Baltimore Meyerhoff Scholars program ( Maton et al. , 2016 ), and the Louisiana State University hierarchical mentoring program ( Wilson et al. , 2012 ). Successful programs such as these should be valued, provided with long-term financial support by the campus instead of unpredictable extramural sources, and used as models for the construction of similar programs at other universities ( Sto Domingo et al. , 2019 ). Widespread undergraduate summer research programs have also been successful in providing research experiences to students from diverse backgrounds ( Lopatto, 2004 , 2007 ; Seymour et al. , 2004 ; Ghee et al. , 2016 ), but it is important to emphasize that these programs are not a substitute for addressing institutional barriers and for building an inclusive culture. Historically black colleges and universities (HBCUs) and high-Hispanic-enrollment institutions (HHEs) continue to play vital roles, training a large portion of Black or African American, Hispanic, and Latinx students who go on to doctoral training in science and engineering fields ( National Center for Science and Engineering Statistics, 2019 ). The much-needed evolution of graduate program admissions strategies to be equitable and inclusive may also lead to increases in the recruitment of students from diverse backgrounds. For example, the Graduate Record Examination (GRE) is a standardized test widely employed by universities in the United States for graduate admissions, despite data indicating that it is a poor predictor of success in graduate school and is a barrier to the admission of historically excluded groups, particularly when cutoff scores for admission are employed ( Miller and Stassun, 2014 ; Petersen et al. , 2018 ). Many graduate programs have removed the GRE from the criteria considered for admission, and some of these programs can be found on Twitter by searching for #GRExit and #GREexit hashtags. Instead of an overreliance on standardized tests with poor predictive value, we need to implement holistic assessments that examine both academic aptitude and other competencies that are central to success as a scientist, such as perseverance, adaptability, creativity, and potential. Grades and test scores are never a determining factor when I recruit new members to my lab, and I have argued against the undue weight given to these criteria in admission to graduate programs. I find that a passion for science and “distance traveled” conveyed through personal statements and conversations are much better predictors of success as a scientist. This hiring strategy has allowed me to recruit an amazing and diverse group of scientists from all walks of life, and I could not be prouder of the members of my lab and their accomplishments.

While representation has increased at the bachelor’s and doctoral degree levels (though clearly not enough), we have largely failed to increase the representation of historically excluded groups within the professoriate and within independent NIH-funded investigators ( Heggeness et al. , 2016 ; Li and Koedel, 2017 ; Meyers et al. , 2018 ; National Center for Science and Engineering Statistics, 2019 ). Why is this? Controlling for many factors, recent studies find that programs and policies focused primarily on increasing the supply of talented PEERs (i.e., increasing the “pipeline”) will not make an adequate impact on diversity at the faculty level ( Gibbs et al. , 2014 , 2016 ). In addition, while PEERs exhibit higher contributions to scientific novelty, their contributions are more likely to be discounted and less likely to lead to faculty positions ( Hofstra et al. , 2020 ). These studies highlight the importance of examining discriminatory institutional barriers (e.g., research evaluation and hiring practices) and addressing inclusive and equitable cultures (or the lack thereof) that exert differential pressures on social identity, career selection, and persistence. I am excited to see the recent emergence of several innovative programs to improve diversity at the faculty level. Some examples include the Maximizing Opportunities for Scientific and Academic Independent Careers (MOSAIC) (K99/R00 and UE5) and the Howard Hughes Medical Institute Hanna Gray Fellowship programs. Not only do these awards provide financial support during postdoctoral training and the initial years as independent faculty, but they also offer opportunities for training and development of professional skills that are imperative to the success of early faculty. The MOSAIC UE5 program also provides support to organizations (e.g., the ASCB) that impart mentorship, networking, and training to the MOSAIC K99/R00 scholars, and requires investigators and administrators from the scholars’ home institutions to engage in mentoring/diversity, equity, and inclusion (DEI) training and planning for systemic change at their universities. Thus, these programs aim to move beyond the deficit-based, “fix the victim” model and impart systemic change in institutional culture and policies. The impact that these programs will have on representation of historically excluded groups within the professoriate remains to be seen, but I am encouraged by these efforts.

We may not all be able to participate in these recruitment and retention programs, but those of us already in hiring positions can immediately assess our current approaches to faculty hiring and make changes to embrace best-in-practice methods that facilitate the equitable recruitment of scientists from diverse backgrounds ( Bhalla, 2019 ). Some key improvements in faculty recruitment strategies include using active advertising approaches such as soliciting applications directly from PEER scientists, requiring and valuing DEI statements, using rubrics, and employing broad recruitments and cluster hires ( Bhalla, 2019 ). I applaud departments that recognize the problem and are experimenting with new strategies to reduce bias in recruitment and to increase equity and diversity. At UC Berkeley, a recent life science cluster search heralded changes to how faculty searches are performed and put excellence in DEI on par with excellence in research, based upon the understanding that the two are not mutually exclusive and both are essential to the campus community. Search committee members evaluated each candidate’s understanding of the issues, record of engagement, and plans for advancing DEI as a faculty member and a portion of the chalk talk during the on-campus interview was used to discuss the candidate’s plans to contribute to DEI efforts.

Critical to the success of these recruitment efforts to diversifying the student body and professoriate is the retention and support of recruited individuals ( Bhalla, 2019 ; Termini and Pang, 2020 ). For example, the transition to faculty as well as tenure and promotion remain major barriers. Transparency in the tenure process and faculty mentoring committees are one way to help support faculty in navigating this challenging and often convoluted process. It should also be taken into consideration that faculty from historically excluded groups face bias in teaching evaluations as well as publication and funding success ( Heggeness et al. , 2016 ; Helmer et al. , 2017 ; Kuehn, 2017 ; Fan et al. , 2019 ; Peterson et al. , 2019 ; Witteman et al. , 2019 ). In addition, care must be taken to not overburden faculty members. Often in an effort to achieve diversity, historically excluded individuals are called upon for a higher amount of service than other faculty members. Expectations for service should be equivalent. If faculty are contributing at a higher level to DEI efforts, this should be valued and their contribution to other aspects of service and teaching adjusted, though not at the expense of opportunities critical to advancement. Some institutions, such as Pomona College and UC Los Angeles, are leading the way in formalizing tenure and promotion requirements that include evaluation of contributions to DEI in teaching, scholarship, and service ( Jaschik, 2016 ; UCLA, 2019 ). This is certainly an exception. When I was hired, I was told that “you can be the best teacher in the world, but that is not going to get you tenure,” clearly establishing research as the sole priority. I agree that research excellence is absolutely required, but I do not think that this needs to be mutually exclusive with excellence in DEI and teaching. Our institutions have a long way to go to achieve an inclusive environment, and it will not happen without education and work.

WHAT CAN WE DO AND HOW CAN WE MAKE A DIFFERENCE? ACKNOWLEDGE, LISTEN, EDUCATE, ACT

Recent events have again shone a light upon the ugly truth of racism that permeates our society, but there is genuine interest from our colleagues and students who want to get involved and make an impact. This is a critical time to have conversations on how best to harness this energy to achieve maximal results. For all of us, it is important to acknowledge the problem of exclusion, to listen to a wide range of voices in our field (e.g., the new “Voices” series of essays in Molecular Biology of the Cell; Welch, 2020 ), to educate ourselves, and to participate in ongoing efforts to promote DEI. Let’s move beyond the often empty pledges on social media, and make the effort to cultivate real change in our communities. Some actions that we can take are as follows:

• Seek out training and education about DEI. Learn about microaggressions, microaffirmations, stereotype threat, imposter syndrome, tokenism, and cultural competency in teaching and mentorship. There may be opportunities to gain such training on campus through campus divisions of equity and inclusion, ASCB Minorities Affairs Committee programs, workshops, and presentations ( Segarra et al. , 2017 , 2020 ), and diversity-focused conferences, such as SACNAS and ABRCMS.
• Require annual training in aspects of inclusion, belonging, diversity, and cultural awareness for all faculty.
• Promote and amplify individuals from historically excluded groups by inviting them to speak during seminar series and conferences and by nominating them for awards.
• Ensure that there is diversity in panels, committees, seminars, conferences, editorial boards, and leadership positions.
• Demand that university leadership value DEI efforts with program funding and as part of the appointment and promotion criteria for all faculty.
• Get involved in existing programs on campus and contribute to the development of new programs. Learn about the programs that have succeeded on other campuses and adopt successful paradigms.
• Have conversations about DEI. If possible, participate in moderated workshops on DEI that question our assumptions and force creative thought regarding new solutions.
• Involve students in decision making and value their opinions. There is often more diversity at the student level than at the faculty level.
• Employ evolving best-in-practice procedures for recruitment and retention of students, staff, and faculty.
• Redefine what excellence and merit mean for students and faculty to include contributions to DEI.
• Enact new, evidence-based, sustainable approaches to improve DEI that have measurable outcomes that can be assessed and improved upon over time. Do not be afraid to make changes and do not be paralyzed by the fear of making a mistake.
• Promote and embrace inclusive teaching methods, such as including readings and discussions of discoveries by scientists from historically excluded groups.

FINAL PERSPECTIVE

I would like to end on a hopeful note. Current events provide momentum to an ongoing movement to make systemic changes to achieve diversity, equity, inclusion, and justice. I am heartened by the amazing and tireless individuals at the forefront of this fight against systemic inequalities and racism. I am also encouraged by the progress that has been made. As a biracial American born to first-generation parents of Filipino and German ancestry, I can look back and see that not so long ago there were states where it would have been illegal for my parents to be married. That was changed just over 50 years ago by Loving v. Virginia, which ruled that state laws forbidding interracial marriage were unconstitutional. We should remember our history and keep fighting for the equitable and just future that we deserve. For too long this has been the fight of a few; it must now become the fight of all of us.

Acknowledgments

Thank you to my many mentors over the years, both formal and informal. A special thank you to my postdoctoral mentor, Ron Kopito, for all of his continued support. I am truly grateful for the amazing colleagues at UC Berkeley, especially the terrific members of my lab. You support me and push me to be the best version of myself. Thank you to Michael Boyce, Stephanie Carlson, Milton To, Aaron Streets, Matthew Olzmann, and Elena Olzmann for discussion and critical reading of this perspective essay. A shout-out to my fellow members of the ASCB Minorities Affairs Committee and all the members of the cell biology community who are fighting for representation, equity, inclusion, and justice. You know who you are, and I appreciate you and what you do!

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Culturally Diverse Student Engagement in Online Higher Education: A Review

• Original Paper
• Published: 02 July 2024

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• Lisa M. Tereshko   ORCID: orcid.org/0000-0002-3932-5131 1 ,
• Mary Jane Weiss 1 ,
• Samantha Cross 2 &
• Linda Neang 2  

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Increased acceptability and implementation of online instruction in higher education has increased the diversity of students that are being taught within a course. Online courses are more likely to include students from varying geographic regions and countries, as well as students of various races, cultures, and ethnicities. This study reviews conceptual and empirical peer-reviewed articles to assess existing strategies to increase the engagement of culturally diverse students in higher education. The search was conducted on PsycINFO and ERIC databases to find articles that were: published in peer-reviewed journals prior to January 2023, included students of a higher education institution where courses were taught at least partially online, reported students’ cultures and linked directly them to student engagement, and were available in English. Thirty-one articles fit the inclusion criteria and were analyzed, by two independent raters, across the measures of research methodology, participant demographics, course format, dependent and independent variables, identified cultural barriers, outcomes, and recommendations. Implications for educators are reviewed and included strategies that involve: timing and pacing manipulations, modifications to course flexibility, attention to language use, strategies for accessing help, increasing material accessibility, providing cultural training, implementing the use of tutors/mentors, strengthening peer collaboration, and increasing compassion.

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Tereshko, L.M., Weiss, M.J., Cross, S. et al. Culturally Diverse Student Engagement in Online Higher Education: A Review. J Behav Educ (2024). https://doi.org/10.1007/s10864-024-09554-8

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Please note you do not have access to teaching notes, the power of feedback in teacher education.

International Journal for Lesson and Learning Studies

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Article publication date: 3 July 2024

This qualitative study extends jigsaw lesson study (JLS) by focusing specifically on the impact of feedback on teacher candidates’ (TCs') professional knowledge and instructional growth in the teacher-educator classroom.

Design/methodology/approach

For this study, JLS took place in two different methods courses and followed the lesson study (LS) framework using the small group rotations of JLS. In each course, the JLS small group teams taught another team before receiving feedback and revising their lessons. Then they would teach another group. After each iteration, teams debriefed and reviewed the feedback to revise their lessons and prepare for reteaching. Following the JLS process, TCs reflected on the impact of feedback in a post-survey that was analyzed, coded and aligned with their lesson iterations and revisions.

Both integrated language arts (ILA) and math TCs reported that receiving peer feedback improved their lessons, instructional materials, revisions and student engagement. Through collaboration, TCs valued peer dialog, multiple perspectives and TCs learned to provide and receive constructive feedback professionally. Overall, feedback and collaboration helped strengthen their lesson planning as they considered multiple perspectives. Feedback helped TCs consider differentiation and the diversity of learners as well as student engagement while building their professional knowledge.

Originality/value

Although a previous study has shown an impact of JLS in ILA teacher-education courses with a broader scope in mathematics courses, this study focused on the JLS process in two teacher-education courses. Furthermore, current research tends to focus on the LS process, but this study focused specifically on TCs’ perceptions of the impact of feedback of their professional and instructional growth.

• Lesson study
• Collaboration
• Teacher education
• Teacher candidates
• Jigsaw lesson study
• Professional knowledge
• Lesson revision
• Instruction

Weaver, J.C. , Matangula, T.C. and Matney, G. (2024), "The power of feedback in teacher education", International Journal for Lesson and Learning Studies , Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJLLS-01-2024-0001

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• Published: 04 July 2024

Caregiver recruitment strategies for interventions designed to optimize transitions from hospital to home: lessons from a randomized trial

• Allison M. Gustavson   ORCID: orcid.org/0000-0002-6401-1230 1 , 2 ,
• Molly J. Horstman 3 , 4 ,
• Jodie A. Cogswell 5 ,
• Diane E. Holland 5 ,
• Catherine E. Vanderboom 5 ,
• Jay Mandrekar 6 ,
• William S. Harmsen 6 ,
• Brystana G. Kaufman 7 , 8 , 9 ,
• Cory Ingram 10 &
• Joan M. Griffin 5 , 11  

Trials volume  25 , Article number:  454 ( 2024 ) Cite this article

Metrics details

Challenges to recruitment of family caregivers exist and are amplified when consent must occur in the context of chaotic healthcare circumstances, such as the transition from hospital to home. The onset of the COVID-19 pandemic during our randomized controlled trial provided an opportunity for a natural experiment exploring and examining different consent processes for caregiver recruitment. The purpose of this publication is to describe different recruitment processes (in-person versus virtual) and compare diversity in recruitment rates in the context of a care recipient’s hospitalization. We found rates of family caregiver recruitment for in-person versus virtual were 28% and 23%, respectively ( p  = 0.01). Differences existed across groups with family caregivers recruited virtually being more likely to be younger, white, have greater than high school education, and not be a spouse or significant other to the care recipient, such as a child. Future work is still needed to identify the modality and timing of family caregiver recruitment to maximize rates and enhance the representativeness of the population for equitable impact.

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Introduction

The increasing recognition and value of family caregivers (FCGs) during the vulnerable transition from hospital to home has positioned FCGs as essential stakeholders in research. The dynamic and uncertain context surrounding transitions of care from hospital to home often results in new tasks placed on the FCG. Capturing the FCG experience, voice, and perception is critical to designing, implementing, and scaling effective interventions that promote positive outcomes at patient, caregiver, clinician, and system levels.

Intervening to support FCGs is a potential solution to stabilizing transition plans and reducing caregiver burden and stress. However, challenges exist to recruiting and retaining caregivers in research [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ]. Patient electronic health records (EHRs) are often used to identify potential research participants. However, EHRs rarely include the FCG’s name, relationship to patient, and contact information, nor do they include the type and level of caregiving support needed [ 12 ]. Screening patient EHR data for patient characteristics that are associated with high caregiver need is another option; [ 10 ] however this process is time and resource intensive, precluding its use for larger studies. Traditional recruitment methods first identify potential participants, mail opt-out letters, and then wait 10 days before initiating contact. This approach may not be ideal for studies on care transitions because the compressed hospitalization time does not provide flexibility for contacting FCGs or allow extra time for FCGs to weigh decisions about participation. The first 1–2 weeks at home pose the highest risk for adverse events (e.g., rehospitalization, falls) [ 13 , 14 , 15 , 16 , 17 ]. Therefore, recruiting after hospitalization is not ideal for examining the effects of interventions designed to capture and facilitate the transition from hospital to home. These recruitment challenges further complicate efforts to recruit diverse FCGs (considering a mix of age, sex, ethnicity, etc.), threatening the internal and external validity of FCG interventions.

With the COVID-19 pandemic, we experienced additional recruitment challenges firsthand while conducting the technology-enhanced Transitional Palliative Care for Family Caregivers trial (TPC), a study evaluating the effect of a novel video intervention designed to support rural FCGs caring for palliative care patients during the transition from hospital to home. The purpose of this publication is to describe our different recruitment processes (in-person versus virtual) and compare diversity in recruitment rates between processes in the context of a care transition from hospital to home.

Overview of TPC intervention and trial

The TPC trial protocol is outlined in detail elsewhere [ 17 ] (trial registration: NCT03339271, November 8, 2017). Briefly, the purpose of the randomized controlled trial was to test the efficacy and cost-effectiveness of a video-based, nurse-led intervention in improving transitions for critically ill patients with life-limiting illnesses by targeting FCG health and well-being. The intervention involved teaching, guiding, and counseling FCGs to enhance caregiving knowledge and skills, while also meeting the FCG’s own health needs. Participants were FGCs living in rural areas who were recruited while the patient (hereafter referred to as care recipient) was hospitalized between 2018 and 2022 [ 17 ]. FCGs were randomly assigned to an attention control condition or the intervention. The attention control group received monthly phone calls from a team member to collect cost data. This approach was utilized to reduce attrition and account for nonspecific effects of the intervention that may occur due to any interaction with the research team [ 17 ]. The intervention began while the care recipient was hospitalized and continued for 8 weeks after hospital discharge. The Mayo Clinic Institutional Review Board (IRB) approved this study (# 17–005188). We used the CONSORT checklist when writing our report [ 18 ].

TPC caregiver recruitment strategies

FCGs were recruited from four hospitals in the same health system in the upper Midwest. FCGs were broadly defined as persons who self-identified as an unpaid, informal caregiver for someone with unmet medical or care needs. Recruitment was timed to occur after a palliative care consult but prior to patient discharge from the hospital. The COVID-19 pandemic prompted a shift in recruitment strategies from in-person to virtual. In-person recruitment included a face-to-face interaction with paper consent, whereas virtual recruitment was conducted via telephone with electronic consent.

To recruit for the trial, we screened the EHR Palliative Medicine calendar daily to identify care recipients admitted for inpatient services and who received a Palliative Medicine consult, but had not yet been discharged from the hospital. Every care recipient was initially screened via chart review in the EHR to determine eligibility and identify if a FCG was named in the EHR. Care recipients, and thereby their FCGs, were excluded if they were < 21 years, had a left ventricular assist device, used home infusion pain pumps, or had documented chronic pain or addictive behaviors in their problem list. Following the initial eligibility screen of care recipients, FCGs were contacted, and the FCG eligibility was confirmed. To be included, FCGs had to provide care outside of the hospital, be ≥ 21 years old, and live in a rural or medically underserved setting (population of 50,000 and under) in Minnesota, Wisconsin, or Iowa. FCGs interested in participating needed to consent prior to the care recipient’s discharge from the hospital.

Prior to the COVID-19 pandemic, in-person recruitment and consent at the hospital was standard. A study coordinator would call the FCG to confirm eligibility, provide a short description of the study, and ask if the FCG could meet with them in-person when the FCG visited the care recipient at the hospital. If the FCG agreed, the study coordinator would arrange a time to meet the FCG at the hospital, give them a study brochure, review the study details outlined in the brochure, answer questions, and then obtain written consent from the FCG. If the FCG wanted to first think about the study, the study coordinator would leave a recruitment packet containing a consent and study brochure with the FCG, follow up within 2 days by phone or in person, and then collect paper consent forms in-person at the hospital.

With the onset of the COVID-19 pandemic hospital policies on March 18, 2020, virtual recruitment became standard. With visitor restrictions imposed, we pivoted our recruitment strategy to avoid any in-person contact and had a 2-week hiatus from recruitment while we revised our processes. We telephoned potential FCG participants to confirm eligibility, but then continued with recruitment activities by phone. The virtual process included a verbal overview of study details—instead of a brochure, time to answer questions, and a verbal review of the consent document. If the FCG agreed to consent to study participation, the study coordinator would enter their information into a Participant Tracking System (Ptrax) and send a secure link by electronic email for an electronic consent form (e-consent). The study coordinator was available to guide a potential participant through the electronic signature process upon request. The FCG was enrolled in the study once the electronic signature was received.

Data collection and analyses

All data were captured in the REDCap [ 19 ] database hosted at the Mayo Clinic. Recruitment and retention data was collected by the study coordinator. Demographic data was collected from baseline study surveys or the care recipient’s EHR. FCG burden was assessed electronically prior to care recipient discharge using the 15-item (7-point scale with − 3 to + 3 ratings) Bakas Caregiving Outcomes Scale-Revised (BCOS-R), with higher scores representing better FCG outcomes [ 20 ]. The ratings were recoded to 1–7 to determine a positive value that would be used in the analysis. The care recipient’s risk for mortality at the time of discharge was assessed using the Charlson Comorbidity Index, with scores derived from the International Classification of Diseases (ICD)-10 codes available in the care recipient’s EHR. Scores range from 0 to 39, with scores > 5 are considered to have high comorbidity [ 21 ]. Discrete baseline characteristics were compared between the two recruitment modalities using a chi-square or Fisher’s exact test, as appropriate. Ordered self-report health status was compared using a Wilcoxon rank sum test and continuous variables were compared using a two-sample t -test. The comparison of recruitment rates between modalities was made using a chi square test. The alpha-level was set at 0.05 for statistical significance. All analyses were conducted using SAS version 9.4 (SAS Inc., Cary, NC).

TPC caregiver recruitment rates

Figure  1 depicts the participant flow diagram by method of recruitment. Eight thousand five hundred ninety-six patients were screened and 1699 FCGs contacted (82% successful contact rate out of the 2065 eligible). Four hundred and twenty-nine FCGs (201 in-person; 228 virtual) provided consent to participate in the TPC trial. For in-person recruitment, 28% of FCGs contacted were recruited compared to 23% of FCGs contacted with virtual recruitment ( p  = 0.01). Twenty-three percent ( n  = 42) of FCGs recruited in-person later withdrew from the study compared to 21% ( n  = 41) of FCGs recruited virtually.

Participant flow diagram for TPC by method of recruitment 

TPC caregiver characteristics by recruitment modality

Table 1 presents FCG characteristics by recruitment modality. Compared to those recruited in-person, those recruited virtually were significantly younger (mean ± SD: 55.5 ± 11.8 vs. 61.4 ± 13.1 years; p  < 0.0001) and more likely to be white (95.7% vs. 92.2%, p  = 0.03). The group recruited virtually was significantly more likely to have vocational or college education (89.1% vs. 77.5%, p  < 0.1) and work full-time (50.3% vs. 34.4%), yet they were less likely to be spouses (50.8% vs. 71.7%, p  < 0.001) or live in the same home as the care recipient (65.7% vs. 82.1%, p  < 0.001). More participants recruited virtually provided caregiving for others in addition to the care recipient, compared to the group recruited in-person (40.3% vs 24.2%; p  = 0.004). However, fewer participants recruited virtually compared to in-person stated they received caregiving help from others (70.1% vs. 80.6%, p  = 0.04). Characteristics of the care recipient (in-hospital mortality/medical complexity and—related—caregiver burden) were not significantly different between groups ( p  = 0.23). Participants did not differ significantly between recruitment methods by other factors possibly related to rates of recruitment such as self-reported health ( p  = 0.32). The mean duration from eligibility (receiving a palliative care consult) to hospital discharge and from the time of consent to the care recipient’s hospital discharge were not statistically different between groups ( p  > 0.26).

We found rates of FCG recruitment higher for in-person compared to virtual recruitment for a randomized controlled trial conducted to test the effectiveness of an intervention to support FCGs during the transition of a critically ill care recipient from hospital to home. FCGs recruited virtually were more likely to be younger, white, have vocational or college level education and were less likely to live in the same residence as the care recipient, be spouses with the care recipient, provide care to another person in addition to the care recipient, and receive less caregiving help from others. This study adds to the literature by describing the reach and participant diversity when utilizing two FCG recruitment strategies in healthcare settings.

The modality in which recruitment and consent occur is an important contextual factor when recruiting FCGs. The wider variability in days from caregiver consent to care recipient hospital discharge and days from care recipient eligibility to hospital discharge in the virtually recruited group may have been an indicator of longer stays due to COVID-19 restrictions (e.g., lack of post-acute services, longer hospital stays). Alternatively, with the virtual recruitment process, we may have been able to connect with caregivers more quickly by not including and coordinating an in-person visit. A potential challenge to virtual recruitment and, subsequently, electronic consent is the reliance on the participant having access to email and the internet [ 22 ]. We found that internet connectivity was not a significant issue in this rural sample once recruited, but we are unsure if virtual methods introduced new biases due to virtual access. Age may play a role in receptivity to and enrollment of FCGs through virtual methods requiring internet/email and digital literacy [ 23 , 24 ]. In the context of a hospitalization and our trial, we note that the differences in recruitment rates by approach may be due to limitations on when we could recruit and when caregivers might be available. For example, FCGs who were younger may not have been available pre-pandemic during business hours, whereas an older FCG may have been more available at bedside for in-person recruitment. These observations and perspectives support the need for multi-modal recruitment strategies that meet the needs, preferences, and capabilities of FCGs to participate in recruitment processes for caregiving studies. In our case, the COVID-19 pandemic provided a natural experiment to observe changes in recruitment rates when switching—by necessity—from in-person to virtual processes. In-person recruitment is a resource-intensive approach that requires travel time on the part of participants and research personnel. However, in-person does offer the opportunity for rapport [ 22 ]. Conversely, virtual recruitment and electronic consent allow flexibility in time and place for both researchers and potential participants. While the difference in recruitment rates between approaches was statistically different, there is a balance to be struck between the costs associated with in-person recruitment and the potential to contact more FCGs with virtual recruitment, but with a higher refusal rate. Research is needed to compare costs in recruitment strategies to identify the ideal return on investment in caregiver studies.

Our challenges with recruitment are shared with others across multiple settings and patient populations with prevailing issues in FCG identification, low recruitment rates, and high numbers of withdrawals from participation [ 3 , 5 , 7 , 8 , 9 , 25 , 26 ]. Opt-out recruitment approaches are common in healthcare delivery settings because of the ability to harness the EHR to screen for enrollment and identify contact information of potential FCGs [ 10 ]. Ma and colleagues employed an EHR-driven process to identify unpaid FCGs of Veterans and found that of the 2134 Veterans who received opt-out letters and were called, 64% answered, and—of those—60% had an unpaid FCG [ 10 ]. However, opt-out letters—while a common and successful strategy [ 3 , 10 ] —are not feasible in the setting of a brief hospital stay. Provider outreach is possible, but likely yields low recruitment due to time constraints on the provider’s part, and difficulty integrating both outreach and communication to the research team into clinical workflow [ 3 , 27 ].

Finally, we shared similar challenges to other researchers in reaching and recruiting racially or ethnically marginalized populations [ 3 , 5 , 7 , 8 , 9 , 25 ]. Although more FCG participants in the in-person group compared to the virtual group represented greater racial diversity, most of the overall sample were white. The small proportion of Black, Asian, and Hispanic participants limited our power to detect differences in recruitment across racial groups. In addition, age may explain the other differences observed between FCGs by recruitment method, such as employment status, relationship to care recipient, self-reported health, and caregiving for an additional person. However, as an ancillary study, we are not powered to look at these comparisons. The modality and timing of recruitment likely play important roles in who is approached, consented, and enrolled. Virtual recruitment may reduce implicit bias that can hinder diversity in trial enrollment [ 28 ]. Importantly, we recruited from a health care system which has the advantage of using EHR data to assist in recruitment identification, but may bias participants who seek and have access to the healthcare system. Structural racism and mistrust in the healthcare systems may lead to missed opportunities to recruit a diverse pool of participants that is necessary to identify culturally sensitive adaptations to the intervention and intervention delivery [ 29 , 30 , 31 ].

Our trial spanned the early pandemic in 2020 to 2023 and, thus, our recruitment rates likely varied between the onset of the pandemic and the later stages as the world attempted to return to normal. Beyond the modality of recruitment, the pandemic likely changed people’s perceptions about participation in clinical trials; [ 32 ] as an ancillary study, our data is unable to discern between changes in recruitment due to modality and changes due to the experiences during the pandemic. The COVID-19 pandemic unmasked the persistent challenges in recruiting a diverse sample, regardless of in-person or virtual approaches. There is an opportunity to learn from the use of two different methods in a single trial to identify inequities and advocate for a multi-modal approach to recruitment. Further research is needed to better understand impacts of recruitment methods on equitable enrollment.

Conclusions and lessons learned

Health care systems continue to recognize the essential role of FCGs in the health and well-being of care recipients and the potential health risks posed to the FCG through this role [ 6 , 33 , 34 , 35 ]. However, challenges to FCG recruitment in the healthcare delivery setting exist and detrimentally impact the advancement of research in this area. To feasibly recruit FCGs during a hospitalization period, we found that utilizing multiple approaches to obtain consent can be a solution to optimizing recruitment and diversity in caregiving research. However, this may depend on the resources available within the research study. Future work is needed to identify the modality, timing, and cost-effectiveness of FCG recruitment to maximize recruitment rates and enhance the representativeness of the population for equitable impact. A qualitative study may be helpful prior to trial commencement with the caveat that this is a challenging population to recruit for any study. Post-intervention interviews with participants may yield insight into barriers as well as co-production of solutions to recruiting caregivers in the context of acute and post-acute care. Ideally, a multi-modal approach to recruitment would occur with the added flexibility of research staff to recruit outside of business hours. The hospitalization timeline is unpredictable, which garners the need for flexibility and adaptable approaches to fit the needs and capacity of researchers and FCGs alike.

Availability of data and materials

Data will be made available on reasonable request.

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Acknowledgements

We are grateful for the work of Emily Hudson, MS in designing the figure and assisting with the formatting of the manuscript. We thank Cassidy Weeks, MS, student at the University of Colorado Doctorate in Physical Therapy Program, for her work in formatting the manuscript and preparing for submission.

The study cited was supported by the National Institutes of Health, National Institute of Nursing Research (R01NR016433). Dr. Gustavson is supported by the Minneapolis Veterans Affairs Center of Innovation, Center for Care Delivery and Outcomes Research (CIN 13–406); the Agency for Healthcare Research and Quality (AHRQ) and Patient-Centered Outcomes Research Institute (PCORI), grant K12HS026379; and the National Institutes of Health’s National Center for Advancing Translational Sciences, grant KL2TR002492. Dr. Horstman is supported by a Career Development Award (1IK2HX003163-01A2) from the United States Department of Veterans Affairs Health Services Research & Development Service of the VA Office of Research and Development. The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs or the United States Government.

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Allison M. Gustavson

Department of Medicine, University of Minnesota, Minneapolis, MN, 55455, USA

Center for Innovations in Quality, Effectiveness, and Safety, Michael E. DeBakey VA Medical Center, Houston, TX, 77030, USA

Molly J. Horstman

Department of Medicine, Baylor College of Medicine, Houston, TX, 77030, USA

Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery Research, Rochester, MN, 55905, USA

Jodie A. Cogswell, Diane E. Holland, Catherine E. Vanderboom & Joan M. Griffin

Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, 55905, USA

Jay Mandrekar & William S. Harmsen

Department of Population Health Sciences, Duke University, Durham, NC, USA

Brystana G. Kaufman

Duke-Margolis Institute for Health Policy, Duke University, Durham, NC, USA

Center of Innovation to Accelerate Discovery and Practice Transformation, Durham VA Health Care System, Durham, NC, USA

Department of Community Internal Medicine, Geriatrics, and Palliative Care, Mayo Clinic, Rochester, MN, 55905, USA

Cory Ingram

Division of Health Care Delivery Research (HCDR), Mayo Clinic, Rochester, MN, 55905, USA

Joan M. Griffin

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Contributions

AMG contributed to conceptualization of the publication aims, methodology, validation, formal analysis, and writing the original draft as well as reviewing & editing.

MJH contributed to conceptualization of the publication aims, methodology, and writing the original draft as well as reviewing & editing.

JAC contributed to validation, data curation, project administration, and writing the original draft as well as reviewing & editing.

DEH contributed to funding acquisition, conceptualization, methodology, investigation, project administration, validation, formal analysis, and writing the original draft as well as reviewing & editing.

CEV contributed to funding acquisition, conceptualization, methodology, investigation, project administration, validation, formal analysis, and writing the original draft as well as reviewing & editing.

JM contributed to methodology, data curation, validation, formal analysis, and writing the original draft as well as reviewing & editing.

WSH contributed to methodology, data curation, validation, formal analysis, and writing the original draft as well as reviewing & editing.

BGK contributed to conceptualization of the publication aims, methodology, and writing the original draft as well as reviewing & editing.

CI contributed to conceptualization of the publication aims, methodology, and writing the original draft as well as reviewing & editing.JMG contributed to funding acquisition, supervision, conceptualization, methodology, investigation, project administration, validation, formal analysis, and writing the original draft as well as reviewing & editing.

Corresponding author

Correspondence to Joan M. Griffin .

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The Mayo Clinic Institutional Review Board (IRB) approved this study (# 17–005188).

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The authors declare that they have no competing interests.

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Gustavson, A.M., Horstman, M.J., Cogswell, J.A. et al. Caregiver recruitment strategies for interventions designed to optimize transitions from hospital to home: lessons from a randomized trial. Trials 25 , 454 (2024). https://doi.org/10.1186/s13063-024-08288-2

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Received : 06 March 2024

Accepted : 24 June 2024

Published : 04 July 2024

DOI : https://doi.org/10.1186/s13063-024-08288-2

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