Black Women: The Intersection of Race and Gender as a Source of Mental Health Vulnerability in Academia

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Kimberlé Crenshaw’s theory of intersectionality posits that systems of oppression, such as racism and sexism, interact to produce unique forms of disadvantage for individuals who hold multiple marginalized identities. This study examined depressive symptoms in Brazil’s academic community, focusing on the intersection of race and gender. Methods Members of the Brazilian academic community completed an online survey that included sociodemographic questions (such as race and gender) and the Patient Health Questionnaire-9 (PHQ-9). Depressive symptoms were assessed using this standardized psychometric instrument in a sample of 3,857 participants. Results The results showed that Black women had significantly higher depression scores (57.1% above the cut-off for a probable diagnosis of depression) than did White women (45.3%), Black men (35.4%), and White men (32.9%). ANCOVA confirmed significant effects of gender, race, and their interaction, with Black women having the highest mean PHQ-9 scores. Logistic regression revealed that Black women were twice as likely as White men to meet the criteria for a probable diagnosis of depression. The findings suggest that systemic vulnerabilities for Black women, compounded by racism and sexism, also persist in academic settings. In fact, academic settings present structural barriers, such as underrepresentation and racialized stress, that can exacerbate mental health disparities. Conclusion This study highlights the urgent need for targeted mental health policies that address intersectional inequalities in academia, with particular attention to the experiences of Black women. Black Women Mental health Depressive symptoms Academic Community Gender Race Figures Figure 1 Figure 2 Figure 3 1. Introduction Major depressive disorder (MDD) is currently among the leading mental health concerns worldwide. It affects an estimated 4.0% of the global population, including 5.7% of adults, 4.6% of men, 6.9% of women, and 5.9% of individuals over the age of 60 [ 1 ]. Overall, approximately 280 million people worldwide live with depression [ 2 , 3 ]. Data from the Brazilian National Health Survey (PNS) [ 4 ] revealed that 10.2% of respondents aged 18 and older experienced moderate to severe depressive symptoms, affecting more than 16 million people. During a depressive episode, individuals may experience persistently low mood characterized by sadness, irritability, or emptiness, often accompanied by a loss of interest or pleasure in previously enjoyed activities. Common symptoms include poor concentration, excessive guilt or low self-worth, hopelessness about the future, suicidal thoughts, disrupted sleep, changes in appetite or weight, and low energy or fatigue [ 1 ]. Beyond its clinical manifestations, depression disproportionately affects populations depending on intersecting social determinants such as gender and race. Kimberlé Crenshaw’s theory of intersectionality [ 5 ] posits that systems of oppression, such as racism and sexism, interact to produce unique forms of disadvantage for individuals who hold multiple marginalized identities. Rather than experiencing racism and sexism as separate, additive forces, Black women are subject to a compounded form of discrimination that cannot be reduced to the sum of its parts. This framework emphasizes that analysing race and gender in isolation may obscure the unique psychosocial stressors affecting Black women, thereby supporting the hypothesis that they may exhibit increased vulnerability to depressive symptoms. Evidence from the United States provides empirical support for this perspective, as Hargrove et al. [ 6 ] found that women reported more depressive symptoms than did men within the same racial and ethnic groups, while Black and Hispanic women presented higher symptom levels than did White women and Black and Hispanic men reported more symptoms than did their White counterparts. Additional evidence underscores the central role of racism in shaping mental health outcomes. A meta-analysis demonstrated a robust association between perceived racism and adverse mental health, with consistent links to self-reported depression and anxiety [ 7 ]. Specifically, the findings suggest that the relationships between perceived racism and self-reported depression and anxiety are highly robust, underscoring the critical role that experiences of racism may play in mental health outcomes [ 7 ]. Despite the relevance of this topic, research on the intersection of gender and race and its implications for mental health remains scarce, particularly in relation to depressive symptoms [ 8 , 9 ]. In the academic context, studies that directly compare depressive symptoms between men and women and between White and Black individuals while explicitly addressing this intersection are noticeably lacking. Within academic settings, evidence suggests that higher education can exert both protective and detrimental effects on mental health. Although higher education is generally associated with protective outcomes [ 10 ], these benefits tend to be more pronounced among specific groups, particularly individuals from socioeconomically disadvantaged backgrounds [ 11 ], and they may be undermined by experiences of racial discrimination [ 12 ]. In addition, the academic environment may reproduce both sexism and structural racism, thereby exacerbating mental health challenges among minoritized groups [ 13 , 14 ]. A growing body of evidence indicates that mental health in academic settings is substantially compromised, with a high prevalence of depressive symptoms [ 15 , 16 ]. Moreover, Black students underutilize mental health services and are often reluctant to seek help, associating help-seeking with negative processes [ 17 ]. In summary, in this study, we employed an explicitly intersectional approach that directly compared men and women as well as Black and White participants, which may yield critical insights into differential vulnerability across gender and racial groups within academic settings. Such an analysis has the potential to elucidate patterns of compounded disadvantage and identify populations at heightened risk for adverse mental health outcomes. To the best of our knowledge, this study is the first to examine depressive symptoms by simultaneously considering gender and race in an academic context. The findings are expected to inform the development of public policies that promote greater support for women in academia, with special attention to the unique challenges faced by Black women. 2. Materials and Methods 2.1 Study Design and Recruitment The experimental design of this study is part of the PSIcovidA project, a study developed to investigate the impact of the pandemic on the mental health of the Brazilian academic community. Data collection was conducted between March 10, 2022, and June 10, 2022. By the conclusion of the study, Brazil was still experiencing the effects of the COVID-19 pandemic, with an accumulated total of 32.8 million confirmed cases and 673,000 deaths. During the final week of data collection, 402,000 additional cases and 1,696 new deaths were recorded. Overall, 19 million vaccine doses had been administered to the Brazilian population [18]. Universities had already resumed their activities, including in-person classes, with the mandatory use of protective face masks. Nationwide, social distancing measures had been progressively relaxed, and the commercial and service sectors were operating under regulations requiring physical distancing and the use of face masks [19]. Data were collected using the snowball methodology, where participants were encouraged to share the survey with their network of contacts [20]. The study was communicated to the main trade unions representing higher education professionals and student unions so that they could share the link to the form with their members. The study was also disseminated through the official project website (www.psicovida.org) and social media (@projetopsicovida) to reach out to new participants. The experimental protocol and informed consent form were approved by the Research Ethics Committee of the Fluminense Federal University (CEP-UFF) and the National Commission for Research Ethics (CONEP), under the Ethical Appreciation Certificate (CAAE) 52739721.0.0000.5243. All procedures were conducted in accordance with the ethical standards of the 1964 Declaration of Helsinki and its later amendments. Written informed consent was obtained from all participants prior to their inclusion in the study. 2.2 Participants Initially, a total of 4,465 participants accessed the online survey. The inclusion criterion required participants to be members of the academic community from a university or research institute, such as professors/faculty members, administrative staff, graduate students, or undergraduate students. The exclusion criteria included individuals who were not part of the academic community from a university or research institute (n = 373) and those with duplicate responses (n = 39). For the statistical analysis of gender and race, individuals who did not disclose their race or gender (n = 45) and those belonging to groups with low sample representation (Asian, Indigenous, and non-binary) (n = 151) were not included. Although groups with small sample sizes were not included in the statistical analyses, their descriptive data are presented in Table 1. The final sample consisted of 3,857 participants. Figure 1 provides a flowchart detailing the steps taken to obtain the final sample. 2.3 Psychometric Instruments 2.3.1 Sociodemographic Questionnaire This instrument was specifically created for this research, with questions regarding gender, age, race, academic position and prior mental disorders. The classification of race follows the official Brazilian census and the Brazilian Institute of Geography and Statistics (IBGE). The questionnaire used the following categories: White, Black (dark-skin Black people), Brown (light-skin Black people), Asian, and Indigenous. The study aimed to understand mental health vulnerability among men and women as well as White and Black individuals. Therefore, Black and Brown participants were grouped into the “Black” category for statistical analysis, in line with the IBGE’s understanding [21] and theories that consider both groups as victims of social marginalization and racism [22]. The participants were also invited to answer a question regarding any previous diagnosis of a mental disorder. 2.3.2 Patient Health Questionnaire-9 (PHQ-9) [23, 24] PHQ-9 scale data were used to assess the likelihood of a depression diagnosis according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-V). The PHQ-9 consists of nine questions addressing major depressive symptoms, as defined in the DSM-IV, and it also evaluates symptom severity through a Likert-type scale, yielding total scores between 0 and 27 points. Each question refers to the frequency of symptoms experienced over the previous two weeks and is scored from 0 (“not at all”) to 3 (“nearly every day”) [23]. The PHQ-9 scale was validated for a Brazilian population sample [24]. Following the recommendation of Santos et al. [24], we used a cut-off score of 9 or higher in the sum of the responses to indicate high levels of depressive symptomatology in the Brazilian population, representing a probable diagnosis of depression. The Cronbach’s alpha for the PHQ-9 scale in this study was 0.916. 2.4 Procedures The participants in this study accessed the web survey by clicking on a provided link, which directed them to the Google Forms platform hosting the research instrument and directions. Initially, a brief introduction outlined the overall objectives of the study and asked whether the respondents were part of the academic community. If the response was affirmative, the participants were directed to review the informed consent terms. Those who indicated that they were not part of the academic community were redirected to a page with information on improving mental health. After providing consent, participants were asked to complete a sociodemographic questionnaire, followed by the PHQ-9 questionnaire, which assessed their depressive symptoms. Participants spent approximately 20 minutes completing all the questionnaires. After submitting their responses, they were provided with supportive information on managing mental health and a list of professional contacts for psychological support. 2.5 Statistical Analysis Descriptive statistical analyses were performed to evaluate the profile of the research participants, calculating the proportion of the sample in relation to the following variables: age, gender, race, transsexuality, prior diagnosis of mental health disorders, and probable diagnosis of depression (PHQ-9 ≥ 9). The characteristics of this sample are described in Table 1. To examine the influence of gender and race on depressive symptoms, the study sample was categorized into four groups: White men, Black men, White women, and Black women. Depressive symptoms served as the dependent variable in the analysis of covariance (ANCOVA), with “gender” and “race” as the independent variables. Age and prior psychiatric disorders were incorporated as control variables in the analysis. Statistical differences among the groups were assessed using the Tukey post hoc test. Following this analysis, a logistic regression model was constructed to examine whether gender and race were associated with a probable diagnosis of depression. The participants were classified as either having or not having a probable depression diagnosis based on a cut-off score of ≥ 9, which served as the dependent variable in the logistic regression model. Age and prior psychiatric disorders were once again incorporated as control variables in the analysis. Statistical analyses were conducted via SPSS 25 and Stata 12.0 software. The significance level adopted for all analyses was p < 0.05. 3. Results 3.1 Sample Characteristics The final sample consisted of 3,857 participants. Regarding gender, 2,606 (67.6%) identified as female, and 1,251 (32.4%) identified as male. With respect to race, 2,571 (66.7%) identified as White, and 1,286 (33.3%) identified as Black (25.6% as Brown and 7.8% as Black). Five (0.1%) participants identified as transgender, 3,828 (99.3%) did not, and 24 (0.6%) preferred not to declare. A total of 2,506 (65.0%) participants had no previous diagnosis of mental disorders, while 1,351 (35.0%) reported having a previous diagnosis of mental disorders (Table 1 ). Table 1 also presents the initial sample, which includes groups that were excluded from the statistical analysis because of small sample sizes (Asian, Indigenous, and non-binary individuals), as well as participants who declined to disclose their gender or racial identity. Table 1 Sample characteristics. Initial Sample (n = 4053) n (%) PHQ-9 Score Mean (SD) Final Sample (n = 3857) n (%) PHQ-9 Score Mean (SD) Age - years 18–29 years 1199 (29.6%) 12.1 (7.2) 1142 (29.6%) 12.0 (7.3) 30–39 years 1129 (27.8%) 9.4 (6.9) 1091 (28.3%) 9.3 (6.9) 40–49 years 872 (21.5%) 8.1 (6.7) 825 (21.4%) 8.1 (6.6) 50–59 years 567 (14.0%) 6.0 (6.0) 531 (13.8%) 5.9 (5.9) 60 or more years 286 (7.1%) 4.5 (5.3) 268 (6.9%) 4.4 (5.3) Gender Women 2693 (66,4%) 9.9 (7.2) 2606 (67.6%) 9.9 (7.2) Men 1315 (32.5%) 7.3 (6.7) 1251 (32.4%) 7.2 (6.6) Non-binary 26 (0.7%) 13.9 (7.4) - - Did not declare gender 19 (0.4%) 11.7 (7.3) - - Transgender No 3998 (98,6%) 9.1 (7.1) 3828 (99.3%) 9.0 (7.1) Yes 17 (0.4%) 14.8 (5.8) 5 (0.1%) 15.8 (4.5) Did not declare transsexuality 38 (0.9%) 9.6 (7.0) 24 (0.6%) 7.8 (7.1) Race White 2598 (64.1%) 8.6 (7.0) 2571 (66.7%) 8.5 (7.0) Brown 994 (24,5%) 9.9 (7.3) 987 (25.6%) 9.9 (7.2) Black 300 (7.4%) 10.9 (7.4) 299 (7.8%) 10.9 (7.4) Indigenous 10 (0.2%) 10.6 (7.4) - - Asian 35 (0.9%) 6.7 (5.7) - - Did not declare race 116 (2.9%) 9.8 (8.1) - - Gender*race White men 839 (21.7%) 7.0 (6.6) 839 (21.7%) 7.0 (6.6) Black men 415 (10.7%) 7.7 (6.7) 412 (10.7%) 7.7 (6.7) White women 1732 (44.8%) 9.3 (7.0) 1732 (44.9%) 9.3 (7.0) Black women 881 (2.8%) 11.3 (7.3) 874 (22.7%) 11.3 (7.3) Academic position Undergraduate students 901 (22.2%) 12.6 (7.4) 854 (22.1%) 12.5 (7.4) Graduate students 837 (20.6%) 10.4 (7.0) 858 (22.3%) 10.4 (7.0) University teachers 1571 (38.8%) 6.7 (6.3) 1427 (37.0%) 6.7 (6.2) Administrative staff 744 (18.4%) 8.1 (6.7) 718 (18.6%) 8.1 (6.6) Previous mental disorders No 2557 (63.1%) 7.3 (6.4) 2506 (65.0%) 7.3 (6.4) Yes 1496 (36.9%) 12.3 (7.2) 1351 (35.0%) 12.3 (7.2) The average score on the PHQ-9 was 9.04 (SD = 7.1) across the entire sample. Using the cut-off point suggested by Santos et al. [ 24 ], with a score of 9 or higher indicating a probable diagnosis of depression, a total of 1,705 (44.2%) participants scored above the cut-off. By intersecting gender and race, four groups were analysed: White men, Black men, White women, and Black women. The average PHQ-9 scores for these groups are presented in Table 1 . When a cut-off point of 9 or higher for a probable diagnosis of depression was used, 32.9% of White men, 35.4% of Black men, 45.3% of White women, and 57.1% of Black women scored above the cut-off (Fig. 2 ). Analysis of covariance (ANCOVA) was conducted to examine the effects of gender (women and men), race (White and Black), and the interaction between these factors on depressive symptoms, with age and a prior diagnosis of a psychiatric disorder included as control variables. The results revealed a significant main effect of gender (F(1,3853) = 72.577, p < 0.001, Ƞ² = 0.015), race (F(1,3853) = 9.669, p < 0.001, Ƞ² = 0.002), and the interaction between gender and race (F(1,3853) = 5.526, p = 0.019, Ƞ² = 0.001), even when controlling for age and previous psychiatric diagnoses. Post hoc analysis revealed that Black women scored significantly higher than all other groups (p < 0.001 for all comparisons), while White women scored significantly higher than both Black and White men (p = 0.002 and p < 0.001, respectively, for each comparison). There were no significant differences between Black and White men (p = 0.965; Fig. 3 /Table 2 ). Table 2 Post hoc analyses examining the interaction between gender and race on mean PHQ-9 scores, controlling for age and prior mental disorders. Gender*Race Mean Difference (PHQ-9) Standart error (SEM) t Tukey test P value Black Women White Women Black Men White Men Black Men White Women White Men Black Men White Men 2.692 2.512 1.258 1.434 1.254 0.180 0.308 0.377 0.262 0.265 0.345 0.378 8.742 6.665 4.797 5.406 3.630 0.475 < .001* < .001* < .001* < .001* 0.002* 0.965 Model adjusted for age and previous mental disorders. *p < 0.005 Additionally, we conducted a multivariable logistic regression analysis to identify the likelihood of scoring above the PHQ-9 cut-off (≥ 9, indicating a high probability of a depression diagnosis; Santos et al. 2013) among the different gender and race groups, using White men as the reference group. Age and a prior diagnosis of a psychiatric disorder were included as control variables. Black men did not differ significantly from White men in terms of the likelihood of a probable depression diagnosis (p = 0.849). However, White women were 1.47 times more likely to be in the high probability of a depression diagnosis group compared to White men (p < 0.001), while Black women were more than twice as likely to be in the high probability of a depression diagnosis group compared to White men (p < 0.001; Table 3 ). Table 3 Multivariable logistic regression showing the likelihood of a probable diagnosis of depression among different race and gender intersection groups. Multivariable model controlling for age and previous mental disorders. Gender*race Odds Ratio z 95% CI Wald test P value White men Black men White women Black women (reference) 1.026 0.190 [-0,240-0,291] 0.849 1.469 4.028 [0,198-0,572] < .001* 2.048 6.600 [0,504-0,929] < .001* Multivariable model controlling for age and previous mental disorders. CI = confidence interval. *p < 0.001. 4. Discussion The aim of this study was to assess the impact of the intersection between race and gender on depressive symptoms within the university community. Our results indicated that Black women exhibit significantly higher levels of depressive symptoms than do all other groups studied. In contrast, White women report fewer depressive symptoms than Black women but significantly more symptoms than either White or Black men. There was no significant difference between White and Black men in terms of depressive symptoms. With respect to potential depression diagnoses, more than half of the Black women in the study (57%) scored above the cut-off point of 9, indicating a higher likelihood of depression. Indeed, logistic regression analysis using White men as the reference group revealed that Black women were twice as likely to meet the criteria for a probable depression diagnosis compared to White men. Taken together, these findings suggest that even within academic settings, the intersection of race and gender constitutes a set of interrelated vulnerability factors that reinforce one another, making Black women more susceptible to elevated levels of depressive symptoms. These results are consistent with Kimberlé Crenshaw’s theory of intersectionality [ 5 ], which posits that overlapping systems of oppression, such as racism and sexism, create unique and compounded disadvantages. In the present study, the detection of a significant interaction underscores the added value of assessing these intersections rather than analysing race and gender as independent variables. The substantially greater burden observed among Black women provides empirical evidence that their experience is qualitatively distinct, shaped by the simultaneous and inseparable forces of race and gender. A growing body of research has sought to explain the greater vulnerability to mental health problems among Black women. Among the various dimensions of gendered racial microaggressions, the stereotype of the “angry Black woman” has shown the strongest association with depressive symptoms [ 25 ]. Other pervasive stereotypes can also be profoundly detrimental. For example, the “strong Black woman” schema [ 26 ] often leads to the perception that Black women do not need or deserve support [ 9 , 27 ]. Furthermore, the combined effects of gendered racism and the “strong Black woman” stereotype may discourage help-seeking behaviors, thereby contributing to the higher prevalence of depressive symptoms observed in this group [ 28 ]. However, to the best of our knowledge, no study has directly compared depressive symptoms among men and women, both White and Black, within the academic community. It might be hypothesized that higher educational attainment and being in a prestigious, successful, and socially ascendant environment, such as academia, could have a protective effect on the mental health of Black women who manage to reach these spaces. However, our findings do not support this hypothesis. On the contrary, Black women remain more vulnerable to depressive symptoms despite being in an academic environment. Sue et al. [ 29 ] showed that universities themselves are sites of environmental racial microaggressions that convey exclusion, such as buildings named exclusively after White men, symbolically reinforcing the message that Black people “do not belong”. Similarly, Mowatt et al. [ 30 ] argued that Black women face a paradox of invisibility and hypervisibility: marginalized in academic recognition yet hyperexposed as racialized and gendered bodies subject to stereotyping and surveillance. This echoes the testimonies in Settles et al. [ 31 ], where Black faculty described being treated as tokens, that is, individuals who, because of their minority status, become hypervisible and subject to heightened scrutiny while simultaneously being rendered invisible through the dismissal of their contributions. These dynamics are not merely symbolic; indeed, they function as chronic psychosocial stressors that erode well-being. Taken together, our findings support the idea that academic spaces may constitute what Settles et al. [ 31 ] describe as a “paradox of (in)visibility,” where Black women’s presence is simultaneously overexposed and undervalued. This paradox amplifies minority stress, reinforcing feelings of isolation and intensifying vulnerability to depression. By empirically demonstrating that Black women in academia are twice as likely as White men to meet the criteria for probable depression, our study extends this theoretical framework to the Brazilian context. Historically, Black women have been underrepresented in the academic community, especially in higher education leadership positions [ 32 , 33 , 34 ]. Between 1997 and 2006, 85% of all vice-chancellors in the United Kingdom were men; no Black women were represented, and there was only one Black male vice-chancellor until 2011 [ 32 , 33 ]. The lack of racial diversity affects all of higher education in the UK. In the 2021–2022 academic year, Black individuals represented less than 1% of senior positions, while 88% were held by White individuals [ 35 , 36 ]. These figures contrast sharply with demographic trends in the general population, where the proportion of Black people has been increasing, reaching 4% in 2021 [ 35 , 36 ]. In Brazil, there is a lack of data on race in the academic community. It was only in 2009, with the National Plan for the Promotion of Racial Equality, that “colour/race” was included in student data collection forms at all levels of public and private education. This information is highly relevant for the development of educational policies, which include the transfer of resources from the federal government [ 37 ]. Even with the expansion of the Black population in higher education, this growth is not reflected in career advancement, particularly in access to the highest positions of power and leadership [ 38 ]. According to data from the National Institute for Educational Studies and Research (INEP), Black students represent 38% of undergraduate students, but only 21% of university professors are Black [ 39 ]. Even in higher-prestige university positions, Black women still face significant inequality in terms of their academic careers. A study conducted by Parent in Science [ 40 ] analysed the distribution of professors’ productivity grants (PQ) in Brazil awarded by the National Council for Scientific and Technological Development (CNPq), focusing on gender and race. The results revealed that only 5.6% of all productivity scholarships are allocated to Black women [ 40 ]. PQ grants are awarded to university professors across all fields of knowledge who stand out for their academic work, with the aim of recognizing and supporting their scientific output. The criteria for awarding these grants include scientific production, participation in the supervision of human resources, and effective contributions to the research field [ 41 ]. PQ grants have different levels, and as the grant level and value increase, the representation of Black women decreases, highlighting an effect known as the “scissors effect”. At the PQ-Sr level, the highest level possible, there are no self-declared Black people and only 6.2% self-declared Brown people [ 40 , 42 ]. Over the past twenty years, affirmative action policies have facilitated Black and Indigenous students’ access to federal universities across Brazil [ 43 , 44 , 45 ]. The implementation of these policies appears to have significantly increased representation in highly competitive and prestigious universities. Moreover, universities that adopted strictly race-based criteria also experienced increased enrolment of students from socioeconomically disadvantaged backgrounds [ 46 ]. However, additional policies to mitigate this socioeconomic and racial inequality are still necessary. Otherwise, this underrepresentation may foster feelings of isolation and a lack of belonging, which could negatively impact their mental health. Black academics are forced to lead double lives or change their cultural codes, as the university environment does not usually understand their ethnic culture [ 47 ]. Loneliness is related to numerous personal characteristics, including low self-esteem, shyness, feelings of alienation, external locus of control, and the belief that the world is not a just place [ 48 ]. Studies show that Black women believe that to “be strong,” they must endure misery, especially in solitude, which results in feelings of disconnection from others [ 49 ]. This construction of the “strong Black woman” stereotype ultimately develops a “maladaptive perfectionism” that involves unrealistically high standards, a high perception of pressure from others, an excessive concern with mistakes, and a perceived discrepancy in relation to one’s own performance [ 50 ]. Thus, Black women who internalize the “strong Black woman” stereotype are vulnerable to mental health problems and feelings of isolation [ 50 ]. Racial discrimination indirectly predicted poorer mental health through a reduction in resilience, an important factor against depression and anxiety [ 51 ]. According to Erving et al. [ 52 ], there is a phenomenon known as anticipatory race-related stress (ARRS), which occurs when, even in the absence of racial threats or violence, racial tension is capable of inducing stress in Black individuals. Among Black university women, ARRS has been found to be positively associated with depressive symptoms [ 52 ]. In a review, Stoll et al. [ 53 ] discuss how academic pressure was a determining factor for the mental distress of Black students. Specifically, Black women reported that their academic knowledge was excessively policed and scrutinized by staff, while learning support and well-being resources were less accessible to them than they were to White individuals, which impacted Black women’s mental health and learning experience [ 54 ]. Another noteworthy finding in our results is that White women exhibit higher levels of depressive symptoms than either White or Black men, with no significant difference observed between the two groups of men. These findings highlight the effect of gender in terms of vulnerability to depressive symptoms. Indeed, there is substantial converging evidence demonstrating that women are more susceptible to depression than men. Women are approximately twice as likely as men to develop depression over the course of their lives, from late adolescence to old age [ 55 ]. This gender disparity in the likelihood of developing depressive symptoms can be attributed to greater exposure to severe adversities, particularly childhood sexual abuse and other forms of violence against women and girls, as well as structural gender inequality [ 55 ]. The 2019 National Health Survey (PNS), conducted in Brazil, enabled the screening of depressive symptoms in a population-based study [ 56 ]. Using the PHQ-9, the survey revealed a higher prevalence of depressive symptoms among women (15.0%) than among men (6.1%) [ 57 ]. Our research revealed an alarmingly elevated prevalence, reaching 57.1% among Black women and 45.3% among White women, when the PHQ-9 cut-off was ≥ 9. This study has some limitations. First, this study was conducted during the ongoing COVID-19 pandemic. We cannot determine the extent to which our results are due to the pandemic, as we lack pre-pandemic data. However, the literature suggests that the COVID-19 pandemic exacerbated an already existing scenario of weakened mental health [ 58 ]. Second, data were collected using a convenience snowball sampling method, which involved distributing a survey link via e-mail and WhatsApp. This approach may have restricted the scope of the study. Additionally, reliance on self-reported data may affect the accuracy and reliability of the responses, as participants could have underreported or overreported certain depressive symptoms. However, this is a common limitation in online surveys and is difficult to overcome, despite the significant contributions of studies that use this experimental approach. Finally, the classification of race was based on self-identification. It is possible that some individuals who self-identified as Black or Brown may have a phenotype that is typically associated with White individuals (or the opposite), as there was no external verification to validate self-reported race. Nevertheless, in Brazil, self-identification is widely accepted, including for accessing affirmative action policies. In conclusion, our study suggests that even though Black women achieve high educational levels and occupy prestigious positions, they remain the most vulnerable group in terms of mental health when compared directly to White women and White and Black men. The findings highlight the urgent need for public policies and actions specifically targeting this social group that move beyond generic mental health support and adopt an intersectional perspective. Black women are underrepresented in science and academia, leading to feelings of isolation and a lack of belonging, which may contribute to mental suffering. Efforts to increase the representation of Black women in academia as actions to combat racism and harassment are crucial for creating a healthier and more inclusive academic environment for everyone. Abbreviations MDD - Major depressive disorder PHQ-9 - Patient Health Questionnaire-9 ANCOVA - Analysis of covariance PNS - National Health Survey WHO - World Health Organization IHME - Institute for Health Metrics and Evaluation IBGE - Brazilian Institute of Geography and Statistics COVID-19 - Coronavirus Disease 2019 CEP-UFF - Research Ethics Committee of the Fluminense Federal University CONEP - National Commission for Research Ethics CAAE - Ethical Appreciation Certificate DSM-V - Diagnostic and Statistical Manual of Mental Disorders - Fifth Edition DSM-IV - Diagnostic and Statistical Manual of Mental Disorders - Fourth Edition SPSS - Statistical Package for the Social Sciences SEM - standard error of the mean CI - confidence interval UK - United Kingdom ONS - Office for National Statistics - England and Wales INEP - National Institute for Educational Studies and Research PQ - productivity grants CNPq - National Council for Scientific and Technological Development PQ-Sr level - Productivity Grants Senior level ARRS - anticipatory race-related stress CAPES - Coordination for the Improvement of Higher Education Personnel FAPERJ - Rio de Janeiro State Research Support Foundation Declarations Ethics approval and consent to participate The experimental protocol and informed consent form were approved by the Research Ethics Committee of the Fluminense Federal University (CEP-UFF) and the National Commission for Research Ethics (CONEP), under the Ethical Appreciation Certificate (CAAE) 52739721.0.0000.5243. All procedures were conducted in accordance with the ethical standards of the 1964 Declaration of Helsinki and its later amendments. Written informed consent was obtained from all participants prior to their inclusion in the study. Consent for publication Not applicable Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding This work was supported in part by federal and state Brazilian research agencies Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ). Authors' contributions Conceptualization, R.M.M, M.G.P and L.O.; methodology, R.M.M, O.F.J, R.S.M.L, M.F.N, S.R.A, A.V.M, L.C.L.P, M.G.P and L.O.; software, R.M.M.; validation, O.F.J, M.G.P and L.O.; formal analysis, R.M.M.; investigation, R.M.M.; resources, R.M.M.; data curation, R.M.M.; writing—original draft preparation, R.M.M and L.O.; writing—review and editing, R.M.M, O.F.J, R.S.M.L, M.F.N, S.R.A, A.V.M, L.C.L.P, I.P.A.D, F.C.S.E, E.V, F.S, Z.M.C.L, M.G.P and L.O.; visualization, R.M.M.; supervision, O.F.J, M.G.P and L.O.; project administration, R.M.M.; funding acquisition, M.G.P and L.O. All authors have read and agreed to the published version of the manuscript. Acknowledgments Not applicable References World Health Organization. Depressive disorder (depression). 2023. https://www.who.int/news-room/fact-sheets/detail/depression. Accessed 30 May 2025. Liu Q, He H, Yang J, Feng X, Zhao F, Lyu J. 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10:07:43","extension":"xml","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":150888,"visible":true,"origin":"","legend":"","description":"","filename":"62dfd3504269486b80fb27d4d13a75571structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8089652/v1/7ded63243d0ed0ebaa37dbe1.xml"},{"id":97128379,"identity":"ab82fbcf-d3f4-47e1-b127-f21fe35b8362","added_by":"auto","created_at":"2025-12-01 08:34:38","extension":"html","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":168440,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8089652/v1/fdee302f4588343e02e1f0a6.html"},{"id":97128366,"identity":"ec58c801-eb08-4340-b1b9-9fb2fb94e366","added_by":"auto","created_at":"2025-12-01 08:34:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":84016,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram representing the steps to achieve the final sample for statistical analyses.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8089652/v1/04105a9a54e64990e2031c3d.png"},{"id":97128368,"identity":"b2c19f7d-b93c-402f-a9ac-9b4d94a619fd","added_by":"auto","created_at":"2025-12-01 08:34:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":112348,"visible":true,"origin":"","legend":"\u003cp\u003eProportion of participants with a probable diagnosis of depression (PHQ-9 ≥ 9) in the total sample (44.2%) and across the four gender–race intersection groups. Black women showed the highest proportion above the cut-off (57.1%, n = 499), followed by White women (45.3%, n = 784), Black men (35.4%, n = 146), and White men (32.9%, n = 276).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8089652/v1/94e78df12cefe124a853a60d.png"},{"id":97142994,"identity":"7310e8ca-c4dd-4468-833b-55142577150e","added_by":"auto","created_at":"2025-12-01 10:08:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":60877,"visible":true,"origin":"","legend":"\u003cp\u003eMean and SEM of the PHQ-9 scores separated by the intersection of gender and race. The score of Black women was significantly greater than that of all other groups (p\u0026lt;0.001). The score of White women was greater than that of men, regardless of whether the men are White (p\u0026lt;0.001) or Black (p=0.002). There was no significant difference between White and Black men (p=0.965).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8089652/v1/4ce9a9e234672c6b0b90b9c0.png"},{"id":97145484,"identity":"45d1f1d6-f102-4305-9753-2f1f847778e5","added_by":"auto","created_at":"2025-12-01 10:13:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1197792,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8089652/v1/9adc36f0-011e-48f6-819b-151879b339af.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Black Women: The Intersection of Race and Gender as a Source of Mental Health Vulnerability in Academia","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMajor depressive disorder (MDD) is currently among the leading mental health concerns worldwide. It affects an estimated 4.0% of the global population, including 5.7% of adults, 4.6% of men, 6.9% of women, and 5.9% of individuals over the age of 60 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Overall, approximately 280\u0026nbsp;million people worldwide live with depression [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Data from the Brazilian National Health Survey (PNS) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] revealed that 10.2% of respondents aged 18 and older experienced moderate to severe depressive symptoms, affecting more than 16\u0026nbsp;million people. During a depressive episode, individuals may experience persistently low mood characterized by sadness, irritability, or emptiness, often accompanied by a loss of interest or pleasure in previously enjoyed activities. Common symptoms include poor concentration, excessive guilt or low self-worth, hopelessness about the future, suicidal thoughts, disrupted sleep, changes in appetite or weight, and low energy or fatigue [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBeyond its clinical manifestations, depression disproportionately affects populations depending on intersecting social determinants such as gender and race. Kimberl\u0026eacute; Crenshaw\u0026rsquo;s theory of intersectionality [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] posits that systems of oppression, such as racism and sexism, interact to produce unique forms of disadvantage for individuals who hold multiple marginalized identities. Rather than experiencing racism and sexism as separate, additive forces, Black women are subject to a compounded form of discrimination that cannot be reduced to the sum of its parts. This framework emphasizes that analysing race and gender in isolation may obscure the unique psychosocial stressors affecting Black women, thereby supporting the hypothesis that they may exhibit increased vulnerability to depressive symptoms. Evidence from the United States provides empirical support for this perspective, as Hargrove et al. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] found that women reported more depressive symptoms than did men within the same racial and ethnic groups, while Black and Hispanic women presented higher symptom levels than did White women and Black and Hispanic men reported more symptoms than did their White counterparts.\u003c/p\u003e\u003cp\u003eAdditional evidence underscores the central role of racism in shaping mental health outcomes. A meta-analysis demonstrated a robust association between perceived racism and adverse mental health, with consistent links to self-reported depression and anxiety [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Specifically, the findings suggest that the relationships between perceived racism and self-reported depression and anxiety are highly robust, underscoring the critical role that experiences of racism may play in mental health outcomes [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Despite the relevance of this topic, research on the intersection of gender and race and its implications for mental health remains scarce, particularly in relation to depressive symptoms [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In the academic context, studies that directly compare depressive symptoms between men and women and between White and Black individuals while explicitly addressing this intersection are noticeably lacking.\u003c/p\u003e\u003cp\u003eWithin academic settings, evidence suggests that higher education can exert both protective and detrimental effects on mental health. Although higher education is generally associated with protective outcomes [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], these benefits tend to be more pronounced among specific groups, particularly individuals from socioeconomically disadvantaged backgrounds [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and they may be undermined by experiences of racial discrimination [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In addition, the academic environment may reproduce both sexism and structural racism, thereby exacerbating mental health challenges among minoritized groups [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. A growing body of evidence indicates that mental health in academic settings is substantially compromised, with a high prevalence of depressive symptoms [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Moreover, Black students underutilize mental health services and are often reluctant to seek help, associating help-seeking with negative processes [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn summary, in this study, we employed an explicitly intersectional approach that directly compared men and women as well as Black and White participants, which may yield critical insights into differential vulnerability across gender and racial groups within academic settings. Such an analysis has the potential to elucidate patterns of compounded disadvantage and identify populations at heightened risk for adverse mental health outcomes. To the best of our knowledge, this study is the first to examine depressive symptoms by simultaneously considering gender and race in an academic context. The findings are expected to inform the development of public policies that promote greater support for women in academia, with special attention to the unique challenges faced by Black women.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003e2.1 Study Design and Recruitment\u003c/h2\u003e\n \u003cp\u003eThe experimental design of this study is part of the PSIcovidA project, a study developed to investigate the impact of the pandemic on the mental health of the Brazilian academic community. Data collection was conducted between March 10, 2022, and June 10, 2022.\u003c/p\u003e\n \u003cp\u003eBy the conclusion of the study, Brazil was still experiencing the effects of the COVID-19 pandemic, with an accumulated total of 32.8\u0026nbsp;million confirmed cases and 673,000 deaths. During the final week of data collection, 402,000 additional cases and 1,696 new deaths were recorded. Overall, 19\u0026nbsp;million vaccine doses had been administered to the Brazilian population [18]. Universities had already resumed their activities, including in-person classes, with the mandatory use of protective face masks. Nationwide, social distancing measures had been progressively relaxed, and the commercial and service sectors were operating under regulations requiring physical distancing and the use of face masks [19].\u003c/p\u003e\n \u003cp\u003eData were collected using the snowball methodology, where participants were encouraged to share the survey with their network of contacts [20]. The study was communicated to the main trade unions representing higher education professionals and student unions so that they could share the link to the form with their members. The study was also disseminated through the official project website (www.psicovida.org) and social media (@projetopsicovida) to reach out to new participants.\u003c/p\u003e\n \u003cp\u003eThe experimental protocol and informed consent form were approved by the Research Ethics Committee of the Fluminense Federal University (CEP-UFF) and the National Commission for Research Ethics (CONEP), under the Ethical Appreciation Certificate (CAAE) 52739721.0.0000.5243. All procedures were conducted in accordance with the ethical standards of the 1964 Declaration of Helsinki and its later amendments. Written informed consent was obtained from all participants prior to their inclusion in the study.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\"\u003e\n \u003ch2\u003e2.2 Participants\u003c/h2\u003e\n \u003cp\u003eInitially, a total of 4,465 participants accessed the online survey. The inclusion criterion required participants to be members of the academic community from a university or research institute, such as professors/faculty members, administrative staff, graduate students, or undergraduate students. The exclusion criteria included individuals who were not part of the academic community from a university or research institute (n\u0026thinsp;=\u0026thinsp;373) and those with duplicate responses (n\u0026thinsp;=\u0026thinsp;39). For the statistical analysis of gender and race, individuals who did not disclose their race or gender (n\u0026thinsp;=\u0026thinsp;45) and those belonging to groups with low sample representation (Asian, Indigenous, and non-binary) (n\u0026thinsp;=\u0026thinsp;151) were not included. Although groups with small sample sizes were not included in the statistical analyses, their descriptive data are presented in Table 1. The final sample consisted of 3,857 participants. Figure 1 provides a flowchart detailing the steps taken to obtain the final sample.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\"\u003e\n \u003ch2\u003e2.3 Psychometric Instruments\u003c/h2\u003e\n \u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003e2.3.1 Sociodemographic Questionnaire\u003c/h2\u003e\n \u003cp\u003eThis instrument was specifically created for this research, with questions regarding gender, age, race, academic position and prior mental disorders. The classification of race follows the official Brazilian census and the Brazilian Institute of Geography and Statistics (IBGE). The questionnaire used the following categories: White, Black (dark-skin Black people), Brown (light-skin Black people), Asian, and Indigenous. The study aimed to understand mental health vulnerability among men and women as well as White and Black individuals. Therefore, Black and Brown participants were grouped into the \u0026ldquo;Black\u0026rdquo; category for statistical analysis, in line with the IBGE\u0026rsquo;s understanding [21] and theories that consider both groups as victims of social marginalization and racism [22]. The participants were also invited to answer a question regarding any previous diagnosis of a mental disorder.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003e2.3.2 Patient Health Questionnaire-9 (PHQ-9) [23, 24]\u003c/h2\u003e\n \u003cp\u003ePHQ-9 scale data were used to assess the likelihood of a depression diagnosis according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-V). The PHQ-9 consists of nine questions addressing major depressive symptoms, as defined in the DSM-IV, and it also evaluates symptom severity through a Likert-type scale, yielding total scores between 0 and 27 points. Each question refers to the frequency of symptoms experienced over the previous two weeks and is scored from 0 (\u0026ldquo;not at all\u0026rdquo;) to 3 (\u0026ldquo;nearly every day\u0026rdquo;) [23].\u003c/p\u003e\n \u003cp\u003eThe PHQ-9 scale was validated for a Brazilian population sample [24]. Following the recommendation of Santos et al. [24], we used a cut-off score of 9 or higher in the sum of the responses to indicate high levels of depressive symptomatology in the Brazilian population, representing a probable diagnosis of depression. The Cronbach\u0026rsquo;s alpha for the PHQ-9 scale in this study was 0.916.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003e2.4 Procedures\u003c/h2\u003e\n \u003cp\u003eThe participants in this study accessed the web survey by clicking on a provided link, which directed them to the Google Forms platform hosting the research instrument and directions. Initially, a brief introduction outlined the overall objectives of the study and asked whether the respondents were part of the academic community. If the response was affirmative, the participants were directed to review the informed consent terms. Those who indicated that they were not part of the academic community were redirected to a page with information on improving mental health.\u003c/p\u003e\n \u003cp\u003eAfter providing consent, participants were asked to complete a sociodemographic questionnaire, followed by the PHQ-9 questionnaire, which assessed their depressive symptoms.\u003c/p\u003e\n \u003cp\u003eParticipants spent approximately 20 minutes completing all the questionnaires. After submitting their responses, they were provided with supportive information on managing mental health and a list of professional contacts for psychological support.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003e2.5 Statistical Analysis\u003c/h2\u003e\n \u003cp\u003eDescriptive statistical analyses were performed to evaluate the profile of the research participants, calculating the proportion of the sample in relation to the following variables: age, gender, race, transsexuality, prior diagnosis of mental health disorders, and probable diagnosis of depression (PHQ-9\u0026thinsp;\u0026ge;\u0026thinsp;9). The characteristics of this sample are described in Table\u0026nbsp;1.\u003c/p\u003e\n \u003cp\u003eTo examine the influence of gender and race on depressive symptoms, the study sample was categorized into four groups: White men, Black men, White women, and Black women. Depressive symptoms served as the dependent variable in the analysis of covariance (ANCOVA), with \u0026ldquo;gender\u0026rdquo; and \u0026ldquo;race\u0026rdquo; as the independent variables. Age and prior psychiatric disorders were incorporated as control variables in the analysis. Statistical differences among the groups were assessed using the Tukey post hoc test. Following this analysis, a logistic regression model was constructed to examine whether gender and race were associated with a probable diagnosis of depression. The participants were classified as either having or not having a probable depression diagnosis based on a cut-off score of \u0026ge;\u0026thinsp;9, which served as the dependent variable in the logistic regression model. Age and prior psychiatric disorders were once again incorporated as control variables in the analysis.\u003c/p\u003e\n \u003cp\u003eStatistical analyses were conducted via SPSS 25 and Stata 12.0 software. The significance level adopted for all analyses was p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Sample Characteristics\u003c/h2\u003e\u003cp\u003eThe final sample consisted of 3,857 participants. Regarding gender, 2,606 (67.6%) identified as female, and 1,251 (32.4%) identified as male. With respect to race, 2,571 (66.7%) identified as White, and 1,286 (33.3%) identified as Black (25.6% as Brown and 7.8% as Black). Five (0.1%) participants identified as transgender, 3,828 (99.3%) did not, and 24 (0.6%) preferred not to declare. A total of 2,506 (65.0%) participants had no previous diagnosis of mental disorders, while 1,351 (35.0%) reported having a previous diagnosis of mental disorders (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e also presents the initial sample, which includes groups that were excluded from the statistical analysis because of small sample sizes (Asian, Indigenous, and non-binary individuals), as well as participants who declined to disclose their gender or racial identity.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSample characteristics.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInitial Sample (n\u0026thinsp;=\u0026thinsp;4053)\u003c/p\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePHQ-9 Score\u003c/p\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFinal Sample (n\u0026thinsp;=\u0026thinsp;3857)\u003c/p\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePHQ-9 Score\u003c/p\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge - years\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18\u0026ndash;29 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1199 (29.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.1 (7.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1142 (29.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.0 (7.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e30\u0026ndash;39 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1129 (27.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.4 (6.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1091 (28.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.3 (6.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e40\u0026ndash;49 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e872 (21.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.1 (6.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e825 (21.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.1 (6.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e50\u0026ndash;59 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e567 (14.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.0 (6.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e531 (13.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.9 (5.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e60 or more years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e286 (7.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.5 (5.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e268 (6.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.4 (5.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWomen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2693 (66,4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.9 (7.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2606 (67.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.9 (7.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1315 (32.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.3 (6.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1251 (32.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.2 (6.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-binary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.9 (7.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDid not declare gender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19 (0.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.7 (7.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTransgender\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3998 (98,6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.1 (7.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3828 (99.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.0 (7.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17 (0.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.8 (5.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (0.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15.8 (4.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDid not declare transsexuality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38 (0.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.6 (7.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24 (0.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.8 (7.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRace\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2598 (64.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.6 (7.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2571 (66.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.5 (7.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBrown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e994 (24,5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.9 (7.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e987 (25.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.9 (7.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlack\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e300 (7.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.9 (7.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e299 (7.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.9 (7.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndigenous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (0.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.6 (7.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAsian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35 (0.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.7 (5.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDid not declare race\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e116 (2.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.8 (8.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender*race\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite men\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e839 (21.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.0 (6.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e839 (21.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.0 (6.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlack men\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e415 (10.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.7 (6.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e412 (10.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.7 (6.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite women\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1732 (44.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.3 (7.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1732 (44.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.3 (7.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlack women\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e881 (2.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.3 (7.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e874 (22.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.3 (7.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAcademic position\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUndergraduate students\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e901 (22.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.6 (7.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e854 (22.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.5 (7.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGraduate students\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e837 (20.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.4 (7.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e858 (22.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.4 (7.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUniversity teachers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1571 (38.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.7 (6.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1427 (37.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.7 (6.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdministrative staff\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e744 (18.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.1 (6.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e718 (18.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.1 (6.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePrevious mental disorders\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2557 (63.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.3 (6.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2506 (65.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.3 (6.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1496 (36.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.3 (7.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1351 (35.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.3 (7.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe average score on the PHQ-9 was 9.04 (SD\u0026thinsp;=\u0026thinsp;7.1) across the entire sample. Using the cut-off point suggested by Santos et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], with a score of 9 or higher indicating a probable diagnosis of depression, a total of 1,705 (44.2%) participants scored above the cut-off. By intersecting gender and race, four groups were analysed: White men, Black men, White women, and Black women. The average PHQ-9 scores for these groups are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. When a cut-off point of 9 or higher for a probable diagnosis of depression was used, 32.9% of White men, 35.4% of Black men, 45.3% of White women, and 57.1% of Black women scored above the cut-off (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAnalysis of covariance (ANCOVA) was conducted to examine the effects of gender (women and men), race (White and Black), and the interaction between these factors on depressive symptoms, with age and a prior diagnosis of a psychiatric disorder included as control variables. The results revealed a significant main effect of gender (F(1,3853)\u0026thinsp;=\u0026thinsp;72.577, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Ƞ\u0026sup2; = 0.015), race (F(1,3853)\u0026thinsp;=\u0026thinsp;9.669, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Ƞ\u0026sup2; = 0.002), and the interaction between gender and race (F(1,3853)\u0026thinsp;=\u0026thinsp;5.526, p\u0026thinsp;=\u0026thinsp;0.019, Ƞ\u0026sup2; = 0.001), even when controlling for age and previous psychiatric diagnoses.\u003c/p\u003e\u003cp\u003ePost hoc analysis revealed that Black women scored significantly higher than all other groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for all comparisons), while White women scored significantly higher than both Black and White men (p\u0026thinsp;=\u0026thinsp;0.002 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, respectively, for each comparison). There were no significant differences between Black and White men (p\u0026thinsp;=\u0026thinsp;0.965; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e/Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePost hoc analyses examining the interaction between gender and race on mean PHQ-9 scores, controlling for age and prior mental disorders.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eGender*Race\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean Difference\u003c/p\u003e\u003cp\u003e(PHQ-9)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStandart error (SEM)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003et\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTukey test\u003c/p\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlack Women\u003c/p\u003e\u003cp\u003eWhite Women\u003c/p\u003e\u003cp\u003eBlack Men\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWhite Men\u003c/p\u003e\u003cp\u003eBlack Men\u003c/p\u003e\u003cp\u003eWhite Women\u003c/p\u003e\u003cp\u003eWhite Men\u003c/p\u003e\u003cp\u003eBlack Men\u003c/p\u003e\u003cp\u003eWhite Men\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.692\u003c/p\u003e\u003cp\u003e2.512\u003c/p\u003e\u003cp\u003e1.258\u003c/p\u003e\u003cp\u003e1.434\u003c/p\u003e\u003cp\u003e1.254\u003c/p\u003e\u003cp\u003e0.180\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.308\u003c/p\u003e\u003cp\u003e0.377\u003c/p\u003e\u003cp\u003e0.262\u003c/p\u003e\u003cp\u003e0.265\u003c/p\u003e\u003cp\u003e0.345\u003c/p\u003e\u003cp\u003e0.378\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.742\u003c/p\u003e\u003cp\u003e6.665\u003c/p\u003e\u003cp\u003e4.797\u003c/p\u003e\u003cp\u003e5.406\u003c/p\u003e\u003cp\u003e3.630\u003c/p\u003e\u003cp\u003e0.475\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e\u003cp\u003e0.002*\u003c/p\u003e\u003cp\u003e0.965\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eModel adjusted for age and previous mental disorders. *p\u0026thinsp;\u0026lt;\u0026thinsp;0.005\u003c/p\u003e\u003cp\u003eAdditionally, we conducted a multivariable logistic regression analysis to identify the likelihood of scoring above the PHQ-9 cut-off (\u0026ge;\u0026thinsp;9, indicating a high probability of a depression diagnosis; Santos et al. 2013) among the different gender and race groups, using White men as the reference group. Age and a prior diagnosis of a psychiatric disorder were included as control variables. Black men did not differ significantly from White men in terms of the likelihood of a probable depression diagnosis (p\u0026thinsp;=\u0026thinsp;0.849). However, White women were 1.47 times more likely to be in the high probability of a depression diagnosis group compared to White men (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while Black women were more than twice as likely to be in the high probability of a depression diagnosis group compared to White men (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariable logistic regression showing the likelihood of a probable diagnosis of depression among different race and gender intersection groups. Multivariable model controlling for age and previous mental disorders.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender*race\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOdds Ratio\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ez\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWald test\u003c/p\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eWhite men\u003c/p\u003e\u003cp\u003eBlack men\u003c/p\u003e\u003cp\u003eWhite women\u003c/p\u003e\u003cp\u003eBlack women\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e[-0,240-0,291]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.849\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.469\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e[0,198-0,572]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e[0,504-0,929]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eMultivariable model controlling for age and previous mental disorders. CI\u0026thinsp;=\u0026thinsp;confidence interval. *p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe aim of this study was to assess the impact of the intersection between race and gender on depressive symptoms within the university community. Our results indicated that Black women exhibit significantly higher levels of depressive symptoms than do all other groups studied. In contrast, White women report fewer depressive symptoms than Black women but significantly more symptoms than either White or Black men. There was no significant difference between White and Black men in terms of depressive symptoms. With respect to potential depression diagnoses, more than half of the Black women in the study (57%) scored above the cut-off point of 9, indicating a higher likelihood of depression. Indeed, logistic regression analysis using White men as the reference group revealed that Black women were twice as likely to meet the criteria for a probable depression diagnosis compared to White men.\u003c/p\u003e\u003cp\u003eTaken together, these findings suggest that even within academic settings, the intersection of race and gender constitutes a set of interrelated vulnerability factors that reinforce one another, making Black women more susceptible to elevated levels of depressive symptoms. These results are consistent with Kimberl\u0026eacute; Crenshaw\u0026rsquo;s theory of intersectionality [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], which posits that overlapping systems of oppression, such as racism and sexism, create unique and compounded disadvantages. In the present study, the detection of a significant interaction underscores the added value of assessing these intersections rather than analysing race and gender as independent variables. The substantially greater burden observed among Black women provides empirical evidence that their experience is qualitatively distinct, shaped by the simultaneous and inseparable forces of race and gender.\u003c/p\u003e\u003cp\u003eA growing body of research has sought to explain the greater vulnerability to mental health problems among Black women. Among the various dimensions of gendered racial microaggressions, the stereotype of the \u0026ldquo;angry Black woman\u0026rdquo; has shown the strongest association with depressive symptoms [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Other pervasive stereotypes can also be profoundly detrimental. For example, the \u0026ldquo;strong Black woman\u0026rdquo; schema [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] often leads to the perception that Black women do not need or deserve support [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Furthermore, the combined effects of gendered racism and the \u0026ldquo;strong Black woman\u0026rdquo; stereotype may discourage help-seeking behaviors, thereby contributing to the higher prevalence of depressive symptoms observed in this group [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. However, to the best of our knowledge, no study has directly compared depressive symptoms among men and women, both White and Black, within the academic community. It might be hypothesized that higher educational attainment and being in a prestigious, successful, and socially ascendant environment, such as academia, could have a protective effect on the mental health of Black women who manage to reach these spaces. However, our findings do not support this hypothesis. On the contrary, Black women remain more vulnerable to depressive symptoms despite being in an academic environment. Sue et al. [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] showed that universities themselves are sites of environmental racial microaggressions that convey exclusion, such as buildings named exclusively after White men, symbolically reinforcing the message that Black people \u0026ldquo;do not belong\u0026rdquo;. Similarly, Mowatt et al. [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] argued that Black women face a paradox of invisibility and hypervisibility: marginalized in academic recognition yet hyperexposed as racialized and gendered bodies subject to stereotyping and surveillance. This echoes the testimonies in Settles et al. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], where Black faculty described being treated as tokens, that is, individuals who, because of their minority status, become hypervisible and subject to heightened scrutiny while simultaneously being rendered invisible through the dismissal of their contributions. These dynamics are not merely symbolic; indeed, they function as chronic psychosocial stressors that erode well-being.\u003c/p\u003e\u003cp\u003eTaken together, our findings support the idea that academic spaces may constitute what Settles et al. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] describe as a \u0026ldquo;paradox of (in)visibility,\u0026rdquo; where Black women\u0026rsquo;s presence is simultaneously overexposed and undervalued. This paradox amplifies minority stress, reinforcing feelings of isolation and intensifying vulnerability to depression. By empirically demonstrating that Black women in academia are twice as likely as White men to meet the criteria for probable depression, our study extends this theoretical framework to the Brazilian context.\u003c/p\u003e\u003cp\u003eHistorically, Black women have been underrepresented in the academic community, especially in higher education leadership positions [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Between 1997 and 2006, 85% of all vice-chancellors in the United Kingdom were men; no Black women were represented, and there was only one Black male vice-chancellor until 2011 [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The lack of racial diversity affects all of higher education in the UK. In the 2021\u0026ndash;2022 academic year, Black individuals represented less than 1% of senior positions, while 88% were held by White individuals [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. These figures contrast sharply with demographic trends in the general population, where the proportion of Black people has been increasing, reaching 4% in 2021 [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn Brazil, there is a lack of data on race in the academic community. It was only in 2009, with the National Plan for the Promotion of Racial Equality, that \u0026ldquo;colour/race\u0026rdquo; was included in student data collection forms at all levels of public and private education. This information is highly relevant for the development of educational policies, which include the transfer of resources from the federal government [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Even with the expansion of the Black population in higher education, this growth is not reflected in career advancement, particularly in access to the highest positions of power and leadership [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. According to data from the National Institute for Educational Studies and Research (INEP), Black students represent 38% of undergraduate students, but only 21% of university professors are Black [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eEven in higher-prestige university positions, Black women still face significant inequality in terms of their academic careers. A study conducted by Parent in Science [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] analysed the distribution of professors\u0026rsquo; productivity grants (PQ) in Brazil awarded by the National Council for Scientific and Technological Development (CNPq), focusing on gender and race. The results revealed that only 5.6% of all productivity scholarships are allocated to Black women [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. PQ grants are awarded to university professors across all fields of knowledge who stand out for their academic work, with the aim of recognizing and supporting their scientific output. The criteria for awarding these grants include scientific production, participation in the supervision of human resources, and effective contributions to the research field [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. PQ grants have different levels, and as the grant level and value increase, the representation of Black women decreases, highlighting an effect known as the \u0026ldquo;scissors effect\u0026rdquo;. At the PQ-Sr level, the highest level possible, there are no self-declared Black people and only 6.2% self-declared Brown people [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOver the past twenty years, affirmative action policies have facilitated Black and Indigenous students\u0026rsquo; access to federal universities across Brazil [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The implementation of these policies appears to have significantly increased representation in highly competitive and prestigious universities. Moreover, universities that adopted strictly race-based criteria also experienced increased enrolment of students from socioeconomically disadvantaged backgrounds [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. However, additional policies to mitigate this socioeconomic and racial inequality are still necessary. Otherwise, this underrepresentation may foster feelings of isolation and a lack of belonging, which could negatively impact their mental health. Black academics are forced to lead double lives or change their cultural codes, as the university environment does not usually understand their ethnic culture [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eLoneliness is related to numerous personal characteristics, including low self-esteem, shyness, feelings of alienation, external locus of control, and the belief that the world is not a just place [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Studies show that Black women believe that to \u0026ldquo;be strong,\u0026rdquo; they must endure misery, especially in solitude, which results in feelings of disconnection from others [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. This construction of the \u0026ldquo;strong Black woman\u0026rdquo; stereotype ultimately develops a \u0026ldquo;maladaptive perfectionism\u0026rdquo; that involves unrealistically high standards, a high perception of pressure from others, an excessive concern with mistakes, and a perceived discrepancy in relation to one\u0026rsquo;s own performance [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Thus, Black women who internalize the \u0026ldquo;strong Black woman\u0026rdquo; stereotype are vulnerable to mental health problems and feelings of isolation [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRacial discrimination indirectly predicted poorer mental health through a reduction in resilience, an important factor against depression and anxiety [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. According to Erving et al. [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], there is a phenomenon known as anticipatory race-related stress (ARRS), which occurs when, even in the absence of racial threats or violence, racial tension is capable of inducing stress in Black individuals. Among Black university women, ARRS has been found to be positively associated with depressive symptoms [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. In a review, Stoll et al. [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] discuss how academic pressure was a determining factor for the mental distress of Black students. Specifically, Black women reported that their academic knowledge was excessively policed and scrutinized by staff, while learning support and well-being resources were less accessible to them than they were to White individuals, which impacted Black women\u0026rsquo;s mental health and learning experience [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAnother noteworthy finding in our results is that White women exhibit higher levels of depressive symptoms than either White or Black men, with no significant difference observed between the two groups of men. These findings highlight the effect of gender in terms of vulnerability to depressive symptoms. Indeed, there is substantial converging evidence demonstrating that women are more susceptible to depression than men. Women are approximately twice as likely as men to develop depression over the course of their lives, from late adolescence to old age [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. This gender disparity in the likelihood of developing depressive symptoms can be attributed to greater exposure to severe adversities, particularly childhood sexual abuse and other forms of violence against women and girls, as well as structural gender inequality [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. The 2019 National Health Survey (PNS), conducted in Brazil, enabled the screening of depressive symptoms in a population-based study [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Using the PHQ-9, the survey revealed a higher prevalence of depressive symptoms among women (15.0%) than among men (6.1%) [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Our research revealed an alarmingly elevated prevalence, reaching 57.1% among Black women and 45.3% among White women, when the PHQ-9 cut-off was \u0026ge;\u0026thinsp;9.\u003c/p\u003e\u003cp\u003eThis study has some limitations. First, this study was conducted during the ongoing COVID-19 pandemic. We cannot determine the extent to which our results are due to the pandemic, as we lack pre-pandemic data. However, the literature suggests that the COVID-19 pandemic exacerbated an already existing scenario of weakened mental health [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Second, data were collected using a convenience snowball sampling method, which involved distributing a survey link via e-mail and WhatsApp. This approach may have restricted the scope of the study. Additionally, reliance on self-reported data may affect the accuracy and reliability of the responses, as participants could have underreported or overreported certain depressive symptoms. However, this is a common limitation in online surveys and is difficult to overcome, despite the significant contributions of studies that use this experimental approach. Finally, the classification of race was based on self-identification. It is possible that some individuals who self-identified as Black or Brown may have a phenotype that is typically associated with White individuals (or the opposite), as there was no external verification to validate self-reported race. Nevertheless, in Brazil, self-identification is widely accepted, including for accessing affirmative action policies.\u003c/p\u003e\u003cp\u003eIn conclusion, our study suggests that even though Black women achieve high educational levels and occupy prestigious positions, they remain the most vulnerable group in terms of mental health when compared directly to White women and White and Black men. The findings highlight the urgent need for public policies and actions specifically targeting this social group that move beyond generic mental health support and adopt an intersectional perspective. Black women are underrepresented in science and academia, leading to feelings of isolation and a lack of belonging, which may contribute to mental suffering. Efforts to increase the representation of Black women in academia as actions to combat racism and harassment are crucial for creating a healthier and more inclusive academic environment for everyone.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eMDD - Major depressive disorder\u003c/p\u003e\n\u003cp\u003ePHQ-9 - Patient Health Questionnaire-9\u003c/p\u003e\n\u003cp\u003eANCOVA - Analysis of covariance\u003c/p\u003e\n\u003cp\u003ePNS - National Health Survey\u003c/p\u003e\n\u003cp\u003eWHO - World Health Organization\u003c/p\u003e\n\u003cp\u003eIHME - Institute for Health Metrics and Evaluation\u003c/p\u003e\n\u003cp\u003eIBGE - Brazilian Institute of Geography and Statistics\u003c/p\u003e\n\u003cp\u003eCOVID-19 - Coronavirus Disease 2019\u003c/p\u003e\n\u003cp\u003eCEP-UFF - Research Ethics Committee of the Fluminense Federal University\u003c/p\u003e\n\u003cp\u003eCONEP - National Commission for Research Ethics\u003c/p\u003e\n\u003cp\u003eCAAE - Ethical Appreciation Certificate\u003c/p\u003e\n\u003cp\u003eDSM-V - Diagnostic and Statistical Manual of Mental Disorders - Fifth Edition\u003c/p\u003e\n\u003cp\u003eDSM-IV - \u0026nbsp;Diagnostic and Statistical Manual of Mental Disorders - Fourth Edition\u003c/p\u003e\n\u003cp\u003eSPSS - Statistical Package for the Social Sciences\u003c/p\u003e\n\u003cp\u003eSEM - standard error of the mean\u003c/p\u003e\n\u003cp\u003eCI - confidence interval\u003c/p\u003e\n\u003cp\u003eUK - United Kingdom\u003c/p\u003e\n\u003cp\u003eONS - Office for National Statistics - England and Wales\u003c/p\u003e\n\u003cp\u003eINEP - National Institute for Educational Studies and Research\u003c/p\u003e\n\u003cp\u003ePQ - productivity grants\u003c/p\u003e\n\u003cp\u003eCNPq - National Council for Scientific and Technological Development\u003c/p\u003e\n\u003cp\u003ePQ-Sr level - Productivity Grants Senior level\u003c/p\u003e\n\u003cp\u003eARRS - anticipatory race-related stress\u003c/p\u003e\n\u003cp\u003eCAPES - Coordination for the Improvement of Higher Education Personnel\u003c/p\u003e\n\u003cp\u003eFAPERJ - Rio de Janeiro State Research Support Foundation\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe experimental protocol and informed consent form were approved by the Research Ethics Committee of the Fluminense Federal University (CEP-UFF) and the National Commission for Research Ethics (CONEP), under the Ethical Appreciation Certificate (CAAE) 52739721.0.0000.5243. All procedures were conducted in accordance with the ethical standards of the 1964 Declaration of Helsinki and its later amendments. Written informed consent was obtained from all participants prior to their inclusion in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported in part by federal and state Brazilian research agencies Coordena\u0026ccedil;\u0026atilde;o de Aperfei\u0026ccedil;oamento de Pessoal de N\u0026iacute;vel Superior (CAPES), Conselho Nacional de Desenvolvimento Cient\u0026iacute;fico e Tecnol\u0026oacute;gico (CNPq) and Funda\u0026ccedil;\u0026atilde;o de Amparo \u0026agrave; Pesquisa do Estado do Rio de Janeiro (FAPERJ).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, R.M.M, M.G.P and L.O.; methodology, R.M.M, O.F.J, R.S.M.L, M.F.N, S.R.A, A.V.M, L.C.L.P, M.G.P and L.O.; software, R.M.M.; validation, O.F.J, M.G.P and L.O.; formal analysis, R.M.M.; investigation, R.M.M.; resources, R.M.M.; data curation, R.M.M.; writing\u0026mdash;original draft preparation, R.M.M and L.O.; writing\u0026mdash;review and editing, R.M.M, O.F.J, R.S.M.L, M.F.N, S.R.A, A.V.M, L.C.L.P, I.P.A.D, F.C.S.E, E.V, F.S, Z.M.C.L, M.G.P and L.O.; visualization, R.M.M.; supervision, O.F.J, M.G.P and L.O.; project administration, R.M.M.; funding acquisition, M.G.P and L.O. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization. Depressive disorder (depression). 2023. https://www.who.int/news-room/fact-sheets/detail/depression. Accessed 30 May 2025.\u003c/li\u003e\n\u003cli\u003eLiu Q, He H, Yang J, Feng X, Zhao F, Lyu J. Changes in the global burden of depression from 1990 to 2017: Findings from the Global Burden of Disease Study. \u003cem\u003eJournal of Psychiatric Research\u003c/em\u003e. 2020;126:134\u0026ndash;140. https://doi.org/10.1016/j.jpsychires.2019.08.002.\u003c/li\u003e\n\u003cli\u003eInstitute of Health Metrics and Evaluation (IHME). Global Health Data Exchange (GHDx). 2021. https://vizhub.healthdata.org/gbd-results/. 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Accessed 13 Oct 2025.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-for-equity-in-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijeh","sideBox":"Learn more about [International Journal for Equity in Health](http://equityhealthj.biomedcentral.com)","snPcode":"12939","submissionUrl":"https://submission.nature.com/new-submission/12939/3","title":"International Journal for Equity in Health","twitterHandle":"@equityhealthj","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Black Women, Mental health, Depressive symptoms, Academic Community, Gender, Race","lastPublishedDoi":"10.21203/rs.3.rs-8089652/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8089652/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eMajor depressive disorder (MDD) affects 3.8% of the global population, with higher rates among women and marginalized groups. Kimberl\u0026eacute; Crenshaw\u0026rsquo;s theory of intersectionality posits that systems of oppression, such as racism and sexism, interact to produce unique forms of disadvantage for individuals who hold multiple marginalized identities. This study examined depressive symptoms in Brazil\u0026rsquo;s academic community, focusing on the intersection of race and gender.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eMembers of the Brazilian academic community completed an online survey that included sociodemographic questions (such as race and gender) and the Patient Health Questionnaire-9 (PHQ-9). Depressive symptoms were assessed using this standardized psychometric instrument in a sample of 3,857 participants.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe results showed that Black women had significantly higher depression scores (57.1% above the cut-off for a probable diagnosis of depression) than did White women (45.3%), Black men (35.4%), and White men (32.9%). ANCOVA confirmed significant effects of gender, race, and their interaction, with Black women having the highest mean PHQ-9 scores. Logistic regression revealed that Black women were twice as likely as White men to meet the criteria for a probable diagnosis of depression. The findings suggest that systemic vulnerabilities for Black women, compounded by racism and sexism, also persist in academic settings. In fact, academic settings present structural barriers, such as underrepresentation and racialized stress, that can exacerbate mental health disparities.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study highlights the urgent need for targeted mental health policies that address intersectional inequalities in academia, with particular attention to the experiences of Black women.\u003c/p\u003e","manuscriptTitle":"Black Women: The Intersection of Race and Gender as a Source of Mental Health Vulnerability in Academia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-01 08:34:33","doi":"10.21203/rs.3.rs-8089652/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-27T10:17:49+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-23T15:11:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"226167111847934236423955858512273245363","date":"2026-03-11T04:47:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"327819277583838929199524870779298553001","date":"2026-03-10T12:20:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-16T12:05:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"219551835650869156131808384607160194923","date":"2025-12-11T23:13:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"50500389321067755009903429191000185256","date":"2025-12-08T16:26:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-25T10:01:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-13T08:16:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-13T03:36:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal for Equity in Health","date":"2025-11-11T18:37:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-for-equity-in-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijeh","sideBox":"Learn more about [International Journal for Equity in Health](http://equityhealthj.biomedcentral.com)","snPcode":"12939","submissionUrl":"https://submission.nature.com/new-submission/12939/3","title":"International Journal for Equity in Health","twitterHandle":"@equityhealthj","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"95f22cee-c4c4-44d5-864f-6df3fe8ea245","owner":[],"postedDate":"December 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T10:25:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-01 08:34:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8089652","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8089652","identity":"rs-8089652","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}