Microaggression and discrimination exposure on young adult anxiety, depression, and sleep.

Data for the present analyses were drawn from the 2022 Spring administration of the American College Health Association-National College Health Assessment III (ACHA-NCHA; Lederer & Hoban 2022 ). The survey is collected twice annually from students of higher education institutions in the U.S. and assesses mental, physical, and sexual health. In the Spring 2022 wave, 69,131 students participated across 129 institutions. Institutional participation rates ranged from three percent to 48% ( M =13.8, SD =7.29). Surveys were administered online from January through June 2022. The analytic sample for the present analyses included N =48,606 young adults aged 18–30 years ( M =20.35, SD =2.02) who were enrolled in undergraduate, degree-granting programs and who had data available on all measures described below. Students completed the Kessler Psychological Distress Scale-6 (K6; Kessler et al., 2002 ) which measures non-specific psychological distress via the frequency of six symptoms (e.g., “nervous,” “hopeless”) over the past 30 days. Responses were on a five-point Likert scale (0= none of the time ; 1= a little of the time ; 2= some of the time ; 3= most of the time ; 4= all of the time ). The present study focused on identifying young adults at risk of experiencing elevated psychological distress at least most of the time as this threshold is similarly used in clinical settings to determine diagnosis. Anxiety symptoms were drawn from the K6, operationalized as reported frequency of feeling “nervous” and “restless or fidgety” over the past 30 days. To compute the dichotomous outcome variable, raw scores for the two items were summed, and ranged from 0 to 8 ( M =4.12, SD =1.97). Students scoring ≥6 (on average experiencing symptoms at least most of the time over the past 30 days) were classified as reporting significant anxiety symptoms. Students reporting a score <6 were the reference. Depressive symptoms were drawn from the K6 and operationalized as the reported frequency of feeling “hopeless,” “so sad nothing could cheer you up,” “that everything was an effort,” and “worthless” over the past 30 days. Raw scores for the four items were summed, and ranged from 0 to 16 ( M =4.91, SD =3.95). Students scoring ≥12 were identified as reporting significant depressive symptoms. Students scoring <12 were the reference . Respondents completed four items assessing the frequency with which they (1) “woke up too early in the morning and couldn’t get back to sleep,” (2) “had an extremely hard time falling asleep,” (3) “felt tired or sleepy during the day,” and (4) “got enough sleep so that they felt rested” over the past seven days (1= 0 days , 8= 7 days ). The items are similar to those in other standardized measures of sleep (e.g., Medical Outcomes Study-Sleep Scale; Shahid et al., 2012 ), although certain indicators of sleep disturbance, such as morning shortness of breath or headaches, were not assessed. Using the four available items, a global composite score was computed. Item four was reverse-coded, and the average of the four items was dichotomized using the median-split method ( M =4.12, Med =4, SD =1.38) to identify respondents who reported relatively high levels of sleep disturbance. Respondents scoring >4 (above the median) were coded as reporting high sleep disturbance; scores ≤4 (at or below the median) were the reference . Respondents were asked about any problems or challenges with microaggression in the last 12 months (yes/no), defined as “a subtle but offensive comment or action directed at a minority or other non-dominant group, whether intentional or unintentional, that reinforces a stereotype.” We henceforth refer to problematic exposure to microaggression as “microaggression exposure.” Respondents reported any problems or challenges with discrimination in the last 12 months (yes/no), defined as “the unjust or prejudicial treatment of a person based on the group, class, or category to which the person is perceived to belong.” We refer to problematic exposure to discrimination as “discrimination exposure.” Covariates were selected based on prior research examining mental health among young adults among the present sample (e.g., Rastogi et al., 2023 ), as well as microaggression and discrimination exposure ( Bostwick et al., 2014 ; Zeiders et al., 2018 ). Students aged 18 to 24 years were identified as emerging adults (coded “1”). Those aged 25 to 30 were identified as young adults (reference group). Students answered three questions: “What sex were you assigned at birth”? (1= Female , 2= Male , 3= Intersex ). “Do you identify as transgender?” (1= No , 2= Yes ). Last, “Which term do you use to describe your gender identity?” (1= Woman or female, 2= Man or male , 3= Trans woman , 4= Trans man , 5= Genderqueer, 6= Agender , 7= Genderfluid , 8= Intersex , 9= Non-binary , and 10= My identity is not listed above [please specify] ). Cisgender men (reference group) were those assigned male at birth, not transgender, self-identifying as “man or male.” Cisgender women were those assigned female at birth, not transgender, self-identified as “woman or female.” Transgender/nonbinary students included those selecting a transgender or nonbinary gender identity (including intersex). Students were asked “What term best describes your sexual orientation?” and provided the following options: 1= Straight/heterosexual , 2= Bisexual, 3= Gay , 4= Lesbian , 5= Pansexual , 6= Queer, 7= Questioning , and 8= My identity is not listed above (please specify ). Students who identified as gay or lesbian were considered homosexual (i.e., same-gender attracted). Students identifying as bisexual or pansexual were considered multi-gender attracted. Students indicating any other identity were coded “different sexual orientation” (reference=straight/heterosexual). Respondents were asked, “How do you usually describe yourself?” and presented with the following options (check all that apply): 1= American Indian or Native Alaskan , 2= Asian or Asian American , 3= Black or African American , 4= Hispanic or Latino/a/x , 5= Middle Eastern/North African (MENA) or Arab Origin , 6= Native Hawaiian or Other Pacific Islander Native , 7= White , 8= Biracial or Multiracial , 9= My identity is not listed above (please specify ). Those selecting multiple options were re-coded as Multiracial. Respondents selecting option nine were re-coded as belonging to a “different race/ethnicity,” as were respondents who identified as “Native Hawaiian or Other Pacific Islander Native,” due to the small sample size ( n =66). White students were the reference group. Given previous research underscoring the associations between weight and mental health ( Emmer et al., 2020 ; McLaren et al., 2008 ), self-reported weight and height were collected. BMI was calculated by dividing students’ weight (kilograms) by the square of their height (meters): (kg) / [height (m)] 2 . BMI 24.9 was considered high. Activity level is known to predict improved mental health ( Cahuas et al., 2020 ; Kredlow et al., 2015 ). Students estimated the time in minutes spent over the past week engaged in moderate or vigorous aerobic activity, and the number of days in the previous week they engaged in muscle strengthening activity (0= 0 days ; 7= 7 days ). Students completing at least two days of muscle strengthening activity per week and at least 150 minutes of moderate aerobic activity met the U.S. Department of Health and Human Services guidelines (2018) for physical activity and were coded “1.” Students who did not meet the guidelines served as the reference group (coded “0”). Respondents identified previous diagnoses among a list of 104 chronic health conditions (e.g., allergies, cancer, endometriosis). Prevalent mental health conditions (anxiety, depression, post-traumatic stress disorder) were identified based on previous research ( Cénat et al., 2021 ; Smoller, 2016 ). Students with at least one of these conditions were identified as having a prior mental health diagnosis (reference=no diagnosis). Analyses were conducted in M plus version 8.8 ( Muthén & Muthén, 1998–2018) . Respondents with missing data were excluded ( n =3047); listwise deletion was used, as missingness did not exceed 10%. In the full analytic sample, a multilevel binary logistic regression model (students nested within schools) was estimated to examine odds of anxiety symptoms in association with self-reported microaggression and discrimination challenges among U.S. college undergraduates (Model 1). Model 2 examined odds of depressive symptoms, and Model 3 examined odds of sleep disturbance. Post hoc regression analyses tested whether the association of microaggression with health differed from the association of discrimination (e.g., is problematic discrimination exposure a stronger predictor of anxiety symptoms than microaggression exposure?). Subsequent racial subgroup analyses were conducted, wherein Models 1, 2, and 3 were estimated in White, Asian American, Black/African American, and Hispanic or Latino/a/x students to determine the extent to which findings among the full analytic sample were consistent across groups. Post hoc regression analyses were conducted again to compare the associations of microaggression and discrimination exposure.

Sample sociodemographic characteristics and prevalence rates are displayed in Table 1 . In total, 16.8% of college students reported problems or challenges with microaggression in the past 12 months; 10.4% reported problems or challenges with discrimination over the same timeframe. One in five (20.2%) reported at least one exposure; of these, 37.4% reported both exposures. Descriptives disaggregated by racial subgroup ( Table 2 ) demonstrated significant differences in rates of self-reported problematic microaggression exposure, Χ 2 (3,41914)=1888.29, p <.001. Asian American, Black/African American, and Hispanic or Latino/a/x students reported significantly higher rates of microaggression exposure than White students. The same pattern of results was observed for discrimination exposure, Χ 2 (3,41914)=1402.63, p <.001. Among the three largest marginalized racial/ethnic groups (Asian American, Black/African American, Hispanic or Latino/a/x), Black/African American students reported the highest rates of microaggression exposure, followed by Asian Americans, while Hispanic or Latino/a/x students reported the lowest rates, Χ 2 (2,10495)=160.37, p <.001. The same pattern was observed for discrimination, Χ 2 (2,10495)=77.21, p <.001. Only effects of the primary predictors of interest are discussed ( Table 3 ). Microaggression exposure was associated with increased likelihood of reporting anxiety symptoms most of the time over the past 30 days ( OR =1.42, 99% CI [1.30, 1.50]). Discrimination exposure was similarly associated with increased likelihood of anxiety symptoms ( OR =1.46, 99% CI [1.33, 1.61]). Post hoc regression analyses revealed the association between microaggression exposure and anxiety symptoms did not significantly differ from the association between discrimination exposure and anxiety symptoms, p >.05. With respect to depressive symptoms, discrimination exposure appeared to have a greater association with the likelihood of distress ( OR =1.59, 99% CI [1.38, 1.84]) relative to microaggression ( OR =1.24, 99% CI [1.09, 1.41]). Post hoc analyses demonstrated a significant difference between microaggression exposure and discrimination exposure on depressive symptoms, p <.01, wherein the association between discrimination and odds of depressive symptoms was stronger than the association with microaggression. Similarly, discrimination exposure was more strongly associated with high self-reported sleep disturbance ( OR =1.54, 99% CI [1.43, 1.67]) than microaggression exposure ( OR =1.27, 99% CI [1.17, 1.39]). The post hoc analyses demonstrated a significantly greater association between discrimination and sleep disturbance than microaggression exposure and sleep disturbance, p <.001. In sum, discrimination was more strongly associated with depressive symptoms and sleep disturbance than microaggression. In contrast, the two exposures appeared to be similar in their associations with anxiety symptoms. The results of the binary logistic regressions examining associations among White, Asian American, Black/African American, and Hispanic or Latino/a/x students are displayed in Tables 4 (anxiety symptoms), 5 (depressive symptoms), and 6 (sleep disturbance). Among White respondents, microaggression ( OR =1.41, 99% CI [1.25, 1.59]) and discrimination exposure ( OR =1.43, 99% CI [1.24, 1.65] each predicted increased likelihood of experiencing anxiety symptoms most of the time over the past 30 days. Similar trends were observed for Asian Americans (microaggression: OR =1.49, 99% CI [1.22, 1.82]; discrimination: OR =1.59, 99% CI [1.26, 2.00]). Among Black/African American respondents, microaggression exposure predicted increased likelihood of anxiety symptoms ( OR =1.41, 99% CI [1.09, 1.83]), although to a lesser magnitude than discrimination exposure ( OR =1.71, 99% CI [1.24, 2.36]). Notably, only microaggression exposure was associated with anxiety symptoms among Hispanic or Latino/a/x respondents ( OR =1.66, 99% CI [1.24, 2.21]). Post hoc regression analyses revealed no significant differences between microaggression and discrimination exposure in the magnitude of the associations with anxiety symptoms for any racial group. Discrimination exposure was associated with increased likelihood of reporting depressive symptoms most of the time over the past 30 days among White ( OR =1.59, 99% CI [1.30, 1.94]), Asian American ( OR =1.56, 99% CI [1.12, 2.38]), and Black/African American ( OR =1.59, 99% CI [1.06, 2.38]) college students. Neither microaggression exposure nor discrimination exposure predicted depressive symptoms in Hispanic or Latino/a/x students. Post hoc regression analyses demonstrated no difference between microaggression and discrimination exposure for any racial group. Microaggression exposure predicted increased likelihood of sleep disturbance over the past seven days among White respondents ( OR =1.34, 99% CI [1.19, 1.51]), as did discrimination exposure ( OR =1.51, 99% CI [1.32, 1.72]). Only discrimination exposure predicted sleep disturbance in Asian American ( OR =1.75, 99% CI [1.39, 2.20]), Black/African American ( OR =1.79, 99% CI [1.34, 2.39]), and Hispanic or Latino/a/x respondents ( OR =1.35, 99% CI [1.03, 1.76]). Post hoc regression analyses demonstrated a marginally significant difference between microaggression and discrimination exposure on sleep for Asian Americans ( p <.05) and a significant difference for Black/African Americans ( p <.01).

The present study examined the differential associations of problematic microaggression and discrimination exposure on young adult anxiety symptoms, depressive symptoms, and sleep disturbance. To our knowledge, this is among the first studies to test two different types of identity-based exposures on different forms of distress ( Seelman et al., 2020 ), while also examining differences across dominant and marginalized racial groups ( F. Lui et al., 2022 ; P. P. Lui, 2020 ). Young adults in our sample reported higher rates of microaggression exposure than discrimination exposure, highlighting the urgency of addressing its unique effects on mental health. With increasing attention paid to social determinants of health ( World Health Organization, 2010 ), the findings are timely and present meaningful insights for research, policy, education, and clinical practice. In the full sample, 20% of college students reported at least one problematic identity-based exposure over the past year; racially marginalized young adults reported significantly greater exposure than Whites, consistent with Forrest-Bank and Jenson (2015) . Both microaggression and discrimination exposure predicted stronger likelihood of anxiety symptoms, depressive symptoms, and sleep disturbance. Interestingly, discrimination was at least twice as strongly associated with depressive symptoms and sleep disturbance than microaggression. This is consistent with meta-analytic evidence demonstrating a stronger association of microaggression with internalizing symptoms relative to externalizing symptoms and somatic complaints ( P. P. Lui & Quezada, 2019 ). Our findings highlight that serious or overt instances of discrimination are deeply consequential for young adult health, more so than microaggression. Discrimination can be experienced as a clear attack on one’s identity which may result in lower feelings of self-worth and depressed mood. In contrast, exposure to microaggression – ambiguous or subtle actions that (in)advertently confirm stereotypes – may prompt the target to doubt or question their own experiences, and may therefore be more closely linked to a sense of uncertainty and vigilance (i.e., anxiety; Brosschot et al., 2006 ) than depression and poor sleep ( Domaradzka & Fajkowska, 2018 ). With these results, we offer preliminary evidence that the mechanism underlying the association between microaggression exposure and mental health may differ from the mechanism driving the effects of discrimination. We posit that microaggressive experiences contribute to a sense of uncertainty and discomfort in one’s environment, which in turn leads to feeling mentally and physically nervous or on edge, symptoms that characterize many anxiety disorders. This can contribute to long-term negative health consequences via heightened hypothalamic-pituitary-adrenal (HPA) axis activation that contributes to chronic feelings of stress and physiologic impacts such as cardiovascular risk ( Berger & Sarnyai, 2015 ; Busse et al., 2017 ). Even microaggression – a subtle experience – may drive racial weathering ( Forde et al., 2019 ), the daily “wear-and-tear” of small but cumulative instances of prejudice upon well-being. Problems with discrimination were significantly more strongly associated with depressive symptoms (e.g., low affect, low motivation, poor sleep quality) than microaggression. As discrimination is typically overt and communicates hostility or disgust toward a marginalized group, the target may internalize the message that their identity is wrong or unacceptable, resulting in depressive symptoms ( Beck & Dozois, 2014 ; Dozois & Rnic, 2015 ). Indeed, prior data shows negative core beliefs about one’s marginalized identities predict depressive symptoms (e.g., worthlessness, hopelessness) in African American adults ( James, 2017 ). Discrimination may also be related to depressed mood via a sense of loss for being “held back” from opportunity or disenfranchised due to structural oppression (i.e., racial melancholia; ( Eng & Han, 2000 ; Grinage, 2019 ). Thus, the exposure can induce multi-level impacts on mental health. Intriguingly, problematic discrimination exposure was also more strongly associated with sleep disturbances than microaggression. Research suggests discrimination impacts sleep via perseverative thinking (e.g., worry, rumination). In a sample of African American young adults, Hoggard and Hill (2018) demonstrated perseveration as a mediator of the link between discrimination and sleep; Farber and colleagues (2021) demonstrated the same in racially marginalized college students. Meta-analytic evidence shows perseverative cognition can prolong HPA axis activation ( Ottaviani et al., 2016 ); HPA axis hyperactivity is then linked with sleep disturbance ( Buckley & Schatzberg, 2005 ). While there is an established literature on the effects of discrimination on sleep ( Slopen et al., 2016 ), the evidence regarding microaggression exposure and sleep is more limited ( Davenport et al., 2021 ; Ong et al., 2017 ). Our findings contribute nuance to this growing literature; microaggression was only half as likely to predict sleep disturbance relative to discrimination. The more subtle experiences may be easier to minimize or discount, therefore inducing less perseveration. Future research should further unpack any specific cognitive features (e.g., perseveration, internalized negative core beliefs) implicated in the process of coping after a marginalizing experience. The qualitative nuances of the exposure may also influence pathways to mental health. Keith et al. (2017) and F. Lui et al. (2022) each identify specific classes or “phenotypes” of discrimination and microaggression exposure among African American and racially marginalized adults, respectively; it is possible that mechanisms to mental and physical well-being vary on the basis of class membership and the specific form of dehumanization experienced (e.g., institutional discrimination versus interpersonal discrimination). We contribute evidence of variations in the associations between identity-based victimization exposures and health across groups. With respect to anxiety, we found that both microaggression and discrimination exposure predicted increased likelihood of symptoms in White, Asian American, and Black/African American respondents; among Hispanic or Latino/a/x respondents, only microaggression exposure predicted anxiety symptoms. With respect to depression, we demonstrated an association between discrimination and depressive symptoms in White and Asian respondents but not in Black/African American and Hispanic or Latino/a/x respondents. Last, both microaggression and discrimination exposure were associated with sleep disturbances in White respondents; only discrimination was associated with sleep disturbances in Asian American, Black/African American, and Hispanic or Latino/a/x respondents. Surprisingly, identity-based exposures were more strongly associated with poor health for Whites than racially marginalized students. This may reflect a decreased sensitization to these exposures, or blunted stress response ( Lovallo et al., 2012 ), in marginalized groups. Black adolescents and young adults have been found to display blunted HPA axis reactivity to a social stress test relative to Whites ( Chong et al., 2008 ; Guo et al., 2017 ), and racially marginalized young adults who report greater sensitivity to experiences of microaggression demonstrate a greater blunting of the physiological stress response than non-minoritized peers ( Majeno et al., 2021 ). Future research must identify mechanisms underlying the associations – or lack thereof. Disrupted stress reactivity is related to greater depressive symptoms and lifespan cardiovascular, metabolic, and inflammatory risk ( Luecken et al., 2013 ); and the process appears to accelerate with age ( Chiang et al., 2022 ). It is therefore urgent to identify and promote means to halt or restore altered biological reactivity in individuals affected by structural oppression. The aim should be to provide opportunities for individuals to process the incident rather than avoiding or minimizing it, which may inadvertently promote worry and perseveration. Of course, we also highlight the imperative to eliminate structural oppression, and strongly caution against reducing societal issues to individual differences in psychopathology and/or resilience. Consistent with past research ( Robertson et al., 2021 ; Wittgens et al., 2022 ), in the full sample, marginalized gender (cisgender women, transgender/nonbinary students) and sexual orientation (gay/lesbian, bisexual/pansexual, different sexual orientation) were associated with greater likelihood of health problems (i.e., anxiety symptoms, depressive symptoms, and sleep disturbance). Individuals with marginalized identities likely experience elevated exposure to adversity and stress, which in turn drives increased symptomatology ( Calabrese et al., 2015 ; Frost & Meyer, 2023 ). These associations were less consistent among the racial subgroup analyses, which may be due to the insufficient statistical power given the limited sample size of respondents with multiple marginalized identities (e.g., Black transgender students). The findings highlight the importance of research utilizing an intersectional approach ( Crenshaw, 1994 ), to understand the qualitative differences in experience and risk facing individuals with multiple marginalized identities. All analyses accounted for prior diagnosis of anxiety, depression, and/or post-traumatic stress disorder. Those with even one diagnosis were more likely to report poor health than respondents with no diagnosis. There may be consequences of the identity-based exposures over and above the prior diagnosis history. This suggests individuals with poor mental health are not merely “more likely to perceive problematic exposures.” The exposures in fact uniquely contribute to poor health. Low and high BMI were associated with increased mental health symptomatology, reflecting associations between weight stigma and well-being ( Emmer et al., 2020 ). Importantly, we demonstrate that individuals of lower weight than the societal norm also demonstrate negative mental health, similar to individuals of higher weight. The primary protective factor that emerged in the analyses was physical activity level, with young adults who met federal guidelines for physical activity demonstrating lower likelihood of symptomatology than those who did not meet federal guidelines ( Ghrouz et al., 2019 ; VanKim & Nelson, 2013 ). Thus, even in the context of identity-based stress, physical activity may promote psychological well-being. A significant limitation of the study is the lack of standardized, symptom-based measures of anxiety, depression, and sleep, such as the Generalized Anxiety Disorder-7 (GAD-7; Spitzer et al., 2006 ), Patient Health Questionnaire-9 (PHQ-9; Kroenke et al., 2009 ), and Medical Outcomes Study-Sleep Scale (MOS-SS; Shahid et al., 2012 ). The present data source included only the Kessler-6 (K6; Kessler et al., 2002 ), and four items assessing sleep. We categorized the individual items of the K6 based on known symptoms of anxiety or depressive disorders. Nevertheless, the proportion of respondents indicating clinically significant depressive symptoms was low, and the null findings across the racial subgroups may reflect the insufficient statistical power. The four items used to assess sleep disturbance overlap with those assessed in the MOS-SS; however, specific manifestations of sleep disturbance were not assessed (e.g., morning shortness of breath). The measures of identity-based exposures did not assess the marginalized identity being targeted (e.g., race, gender, sexual orientation); our findings suggest that the harmful effects may extend across a variety of marginalized identities. However, future research should replicate the analyses using validated measures of disorder-specific symptomatology, including traumatic stress symptoms (e.g., social avoidance, dissociation) given increasing research on discrimination and racial trauma ( Williams et al., 2018 ). As with most survey research, our work is also limited by sampling and response bias; for instance, racially marginalized young adults and cisgender men were underrepresented in the present sample relative to the population of all U.S. college students ( National Center for Education Statistics, 2024 ). Because the sample is not population-representative, we are limited in our ability to generalize the findings to all college students. It is possible that students with pre-existing health concerns are more likely to participate in a health-focused survey than those in good health. Racially marginalized students who have positive experiences with campus authority figures and who experience a sense of belonging may also be more likely to participate than those with mistrust (perhaps due to significant experiences of bias/discrimination) of the campus community. Another limitation is that the data are cross-sectional, limiting causal inference. Third variables may predict young adults’ challenges with identity-based victimization as well as their negative mental health. Also, microaggression and discrimination exposure were each assessed using a single item. The item did not directly assess whether or not young adults had been targeted by these incidents, per se, but rather whether or not they perceived “problems or challenges” with such incidents – which may even include vicarious or witnessed discrimination and microaggression. There was also no data available regarding the perpetrator of the incident (e.g., relationship to target, gender, race) – which may moderate the associations between discrimination and health. Future research should utilize validated scales to assess discrimination and microaggression exposure, as well as unpack the specific aspects of the exposure that may shape well-being (e.g., perpetrator, frequency, perceived severity, direct or vicarious incident; ( Ibrahim et al., 2024 ). Last, it is also important to further unpack qualitative and quantitative differences across racial groups, and disaggregate groups such as Asian American (e.g., East Asian, Southeast Asian, South Asian), Black/African American (e.g., African American, African Caribbean, African immigrants) and Hispanic or Latino/a/x (e.g., Mexican, Central American, South American). Given that racial discrimination is not the only form of stigma or oppression in the U.S., it would be relevant and timely to disaggregate results by other aspects of identity (e.g., gender, sexual orientation, disability). This would enable us to understand whether mechanisms vary by target identity, and if there are unique risks for multiply-marginalized individuals (e.g., sexual minority people of color). To address these issues, it is important to practice oversampling of marginalized or numerical minority groups who are often excluded from research; even population-representative samples may be underpowered to test the nuances in lived experience across diverse marginalized communities ( Kalton, 2009 ; Vaughan, 2017 ). First, clinicians screening patients for anxiety or depression may want to assess whether individuals have experienced microaggression or discrimination exposure. Over half the U.S. population possesses at least one marginalized identity (50.8% female; U.S. Department of Commerce, 2021 ); a non-negligible portion of the psychological distress may be due to social determinants. Second, when treating individuals with exposure to identity-based victimization, clinicians should validate the lived experiences of marginalization and foster adaptive coping skills. For example, the cognitive restructuring component of cognitive behavioral therapy (CBT) requests patients to question the validity of their fears and may exacerbate psychological distress associated with microaggression ( Kelly, 2019 ; Metzger et al., 2021 ), which by nature may increase distress when individuals question the legitimacy of such problematic experiences. Practitioners can contextualize psychological distress and treatment within a larger framework of cultivating resilience and resistance against structural oppression and assist clients in developing adaptive coping skills to use in everyday life in response to discriminatory experiences. Clinical approaches such as acceptance and commitment therapy (ACT) can assist clients in building coping skills in which experiences like discrimination occur regularly. ACT teaches clients to live aligned with their values (i.e., constructs that are important to individuals; ( Banks et al., 2021 ; Martinez et al., 2022 ). This can increase psychological wellbeing in the context of harmful experiences such as discrimination. There is a need for disaggregated research on identity-based victimizations and health. Asian Americans have long been stereotyped as a “Model Minority” faring just as well as – or better than – White Americans ( Wong & Halgin, 2006 )and excluded from health and health disparities research ( Đoàn et al., 2019 ). Yet, we evidence “racial weathering” in Black/African American, Hispanic or Latino/a/x, and Asian American young adults. Future research is needed to unpack group-specific risk and protective factors. It is also important to examine these pathways among Native American, Middle Eastern/North African, Pacific Islander, and Multiracial individuals, for whom we were underpowered to conduct racial subgroup analyses. Last, longitudinal research should be undertaken to understand the cumulative effects of microaggression and discrimination exposure upon mental health over time ( Del Toro & Hughes, 2020 ).