Abstract
Background:
Gender discrimination among healthcare workers (HCWs) negatively impacts their mental health and career development; however, few studies have explored how experiences of gender discrimination change during times of health system strain.
Methods:
This survey-based study assesses the associations between gender discrimination and four stress-related mental health outcomes (posttraumatic stress, depression, anxiety, and burnout), as well as the qualitative experiences of gender discrimination in healthcare during the COVID-19 pandemic.
Results:
Among women, increased gender discrimination was associated with heightened symptoms of posttraumatic stress, depression, anxiety, and burnout after adjusting for demographics and pandemic-related stressors; however, among men, increased gender discrimination was only associated with heightened symptoms of depression. Using thematic analysis, we identified five themes that describe experiences of gender discrimination faced by women in healthcare, including differential valuing of work and contributions, gendered roles and assumptions about roles, maternal discrimination, objectification, and “old boys club.” We also identified two themes describing how men perceived gender discrimination, including instances of symbolic discrimination and woman provider preference.
Conclusion:
These findings suggest that experiences of gender discrimination persist during times of health system strain and negatively impact women HCWs' mental health.
Keywords: gender discrimination, sexism, health care workers, mental health, depression, anxiety, posttraumatic stress, burnout
Introduction
Gender discrimination in health care is a prevalent and impactful issue, particularly for women who work in the field. Across several studies, women health care workers (HCWs) were significantly more likely to experience gender-based discrimination,1,2 gender harassment, and unwanted sexual attention3 compared with men HCWs. These forms of gender discrimination include pay inequities,4,5 disparities in career advancement,6,7 and sexual harassment,3,8 among others. This issue is widespread; previous studies have found that 43%–92% of physicians who identify as a woman experience gender discrimination.1,9–11
The various forms of gender discrimination that women HCWs face have been associated with a range of negative mental health outcomes, including higher symptoms of posttraumatic stress, burnout, and depression.2,11–14 More specific forms of discrimination have also been linked to less favorable mental health outcomes; for example, maternal discrimination has been associated with higher self-reported burnout,9 misidentification of women who are physicians (i.e., the assumption that women physicians are nurses) with feelings of anger and lower levels of job satisfaction,15 and sexual harassment with worse mental health, job satisfaction, and sense of safety at work.3
In addition to women, prior studies have found that HCWs identifying as men perceived gender discrimination in their workplaces. This is particularly true in the nursing profession due to the gendered stereotype that nursing is a feminine career.16 However, physicians and clinical directors identifying as men have also expressed perceived gender discrimination, such as patients preferring women providers,17–19 which is especially true in obstetrical and gynecological care.20 In qualitative studies, HCWs identifying as men have also described instances of symbolic discrimination, whereby members of the dominant social group perceive that they face disadvantages when minority groups are given equitable treatment.21,22
Prior research on the outcomes of gender discrimination among HCWs has been limited in a few key ways. Most studies have focused on the experiences of women physicians,4,6 nurses,5,8 or medical trainees,10,13 leaving a substantial gap in understanding the experiences of men and HCWs who work as health technicians/technologists, nursing or medical assistants, other clinical occupations (e.g., occupational therapists), and nonclinical occupations (e.g., hospitality, reception, and administration). Additionally, most of the studies that assess mental health outcomes related to gender discrimination have focused on burnout2,14 with only a handful measuring other mental health outcomes, such as symptoms of depression,7,13 posttraumatic stress,12 or anxiety.7 Most of the work that has explored this subject also consists of quantitative survey studies, with relatively few articles examining the more nuanced, qualitative experiences of gender discrimination within this population.17,23 This could result in a gap in understanding the subtler aspects of gender discrimination that fall outside of more quantifiable, explicit experiences like sexual harassment or pay disparities.
Furthermore, the COVID-19 pandemic has presented a wide variety of challenges that could impact women in health care, such as changing childcare needs and shifts in work responsibilities.12,24 However, the impact of the pandemic and its associated stressors has largely been unexplored when evaluating the specific issue of gender discrimination faced by HCWs. The present study aims to fill these gaps by assessing the association between gender discrimination and four stress-related mental health outcomes (i.e., posttraumatic stress, depression, anxiety, and burnout) as well as the qualitative experiences of gender discrimination in health care across a variety of health professionals in the context of the COVID-19 pandemic.
Methods
Setting and recruitment
This study is part of a larger project that assesses the psychological impact of the COVID-19 pandemic on HCWs. We used a serial cross-sectional design, whereby we administered an online, anonymous survey every 6 months beginning in June 2020 from different samples of HCWs in the United States.25 Using prior waves of data, we found that more frequent gender discrimination was associated with symptoms of posttraumatic stress and burnout among women HCWs,12 and that women HCWs experience various forms of discrimination that contribute to social and professional isolation.24
In the current study, we expand upon this work by considering both the frequency and perceived impact of gender discrimination experienced by both women and men, and examining experiences of gender discrimination in a different phase of the pandemic. We collected data from January to March 2022 from 30 academic health systems. We sampled health systems located in states with high rates of COVID-19 transmission based on real-time data,26 and emailed their department chairs to invite them to forward our survey to their staff. The recruitment email described that the purpose of the study was to understand HCWs' experiences and wellbeing during the pandemic.
All participants provided written informed consent and the Yale Institutional Review Board approved our study. We compensated participants by entering them into a raffle to win a $35 gift card (1 in 20 participants were awarded gift cards).
Data collection tool
The survey included questions on gender discrimination; symptoms of posttraumatic stress, depression, anxiety, and burnout; and hypothesized risk and protective factors.
Gender discrimination
We assessed gender discrimination quantitatively with two questions asking respondents how often they have been treated unfairly based on their gender in the previous year and the level of associated distress. These questions have been previously used to assess gender discrimination among women, men, and gender minorities.27 The frequency question included a six-point Likert scale, ranging from never (score = 0) to almost all the time (score = 5). Respondents who reported any frequency of gender discrimination (i.e., frequency score >0) were also asked about the associated distress of gender discrimination (rated on a four-point Likert scale from not at all distressed [score = 0] to extremely distressed [score = 3]). The frequency and distress items were summed to create an overall gender discrimination score (range = 0–8). We also assessed experiences of gender discrimination qualitatively; respondents who reported gender discrimination were presented with an open-ended question asking them to describe an event in which they dealt with gender discrimination.
Mental health outcomes
We included validated measures of four stress-related mental health outcomes, including the four-item Primary Care-Posttraumatic Stress Disorder (PC-PTSD) scale to measure symptoms of pandemic-related posttraumatic stress,28 nine-item Patient Health Questionnaire-9 (PHQ-9) to assess depressive symptoms,29 seven-item Generalized Anxiety Disorder-7 (GAD-7) to measure anxiety symptoms,30 and two-item Maslach Burnout Inventory to measure burnout.31 These instruments have been validated and used to assess mental health among HCWs.25,31–36 In the current sample, internal consistencies of these scales were acceptable to excellent (α = 0.69, 0.88, 0.92, and 0.68 for the PC-PTSD, PHQ-9, GAD-7, and Maslach Burnout Inventory, respectively).
Covariates
We assessed other factors that have been associated with mental health among HCWs during the pandemic, including job,25,32,36 history of mental health diagnosis,25,32,37 frontline status,33,36 changes in roles during the pandemic,12 changes in work hours during the pandemic,12 social support needs,25,36,37 and childcare needs.24,38 We included single items to measure job (i.e., physician, medical trainee, nurse, health technologist/technician, nurse/medical assistant, other clinical job, and nonclinical job), history of mental health diagnosis (i.e., none or at least one), and frontline status (i.e., directly work with patients with COVID-19, work remotely with patients with COVID-19, and do not work with patients with COVID-19).
We also included single items to assess pandemic-related changes in work roles and changes in work hours.25 We measured social support needs using one item from the National Health and Nutrition Examination Survey to assess if participants need a lot, some, a little, or no more social support.39 We assessed childcare needs by asking respondents who had children if they needed a lot, a little, or no more childcare support.12 Lastly, we collected demographic information, including gender, age, race, ethnicity, immigrant status, and marital status.
Data analysis
Quantitative analysis
We first conducted a missing data analysis to compare participants in the analytic sample with those who were dropped due to missing data using independent-samples t-tests and chi-squared analyses. We assessed descriptive statistics of the sample stratified by gender, including mean and standard deviation (SD) for continuous variables and frequency and proportion for categorical variables.
We used unadjusted and adjusted linear regression models to test associations between gender discrimination and each mental health outcome, which were also stratified by gender. We included all covariates in the adjusted models (i.e., age, race, ethnicity, immigrant status, marital status, history of mental health diagnosis, job, frontline status, changes in roles, changes in hours, social support needs, and childcare needs). In the adjusted models, we also calculated semipartial eta squared for each variable to describe the proportion of unique variance explained by each variable of the total variance remaining after accounting for variance explained by the other variables. We conducted all analyses in SPSS 27.0.40 We considered p < 0.05 to be statistically significant.
Qualitative analysis
We uploaded all responses to the open-ended question asking about experiences of gender discrimination into Microsoft Excel. Our coding team included a woman doctoral student, woman Master of Public Health student, and a woman undergraduate research assistant (R.H., H.G., and R.P., respectively). We planned to code all data using a codebook that our research group previously developed to describe experiences of gender discrimination in the health care workforce.24 Two coders (R.H. and H.G.) initially read all responses to assess the fit of this codebook for the present data. After agreeing that it was a good fit, all three coders applied codes to the responses. Coders added novel codes to the original codebook as needed. Half of the responses were coded by two analysts to assess if we applied codes similarly; in coding meetings, we discussed any coding discrepancies until consensus was reached.
Once all the responses were coded, we met to discuss relationships between codes to conceptualize cross-cutting themes using thematic analysis.41 We used reflexivity throughout data analysis by reflecting on how our own positionalities may influence our interpretations of the data.42 We present themes and quotations in the “Results” section that are representative of all the open-ended responses.
Results
Sample
Table 1 shows the characteristics of the 695 women and 255 men in our sample. The mean ages of women and men were 40.1 years (SD = 11.0) and 43.3 years (SD = 13.2), respectively. Most participants identified as White (73.7% for women and 72.2% for men) and 32.9% of women and 18.0% of men had a history of at least one mental health diagnosis. About one-third of women participants were mothers to at least one child who required childcare (n = 235; 33.8%); of these, only 24.2% (n = 57) did not need additional childcare support. Similarly, 30.2% of men were fathers to at least one child who required childcare; of these, only 31.2% (n = 24) did not need additional childcare support. About half of women (n = 358; 51.5%) and 18.4% of men (n = 47) reported experiencing gender discrimination.
Table 1.
Participant Characteristics
| Characteristics | Women (n = 695) |
Men (n = 255) |
|---|---|---|
| Mean (SD) or n (%) | Mean (SD) or n (%) | |
| Age | 40.1 (11.0) | 43.3 (13.2) |
| Race | ||
| White | 512 (73.7%) | 184 (72.2%) |
| Southeast Asian | 14 (2.0%) | 8 (3.1%) |
| Middle Eastern/North African | 11 (1.6%) | 9 (3.5%) |
| Black | 31 (4.5%) | 5 (2.0%) |
| Other | 11 (1.6%) | 6 (2.4%) |
| South Asian | 26 (3.7%) | 7 (2.7%) |
| East Asian | 41 (5.9%) | 19 (7.5%) |
| Mixed race | 49 (7.1%) | 17 (6.7%) |
| Ethnicity | ||
| Latinx | 41 (5.9%) | 11 (4.3%) |
| Non-Latinx | 654 (94.1%) | 244 (95.7%) |
| Immigrant | 72 (10.4%) | 29 (11.4%) |
| Marital status | ||
| Single | 180 (25.9%) | 49 (19.2%) |
| Married/partnered | 465 (66.9%) | 201 (78.8%) |
| Divorced/widowed | 50 (7.2%) | 5 (2.0%) |
| History of mental health diagnosis | ||
| No | 466 (67.1%) | 209 (82.0%) |
| Yes | 229 (32.9%) | 46 (18.0%) |
| Job | ||
| Physician | 203 (29.2%) | 132 (51.8%) |
| Nurse | 153 (22.0%) | 22 (8.6%) |
| Nonclinical | 78 (11.2%) | 14 (5.5%) |
| Health technologist | 23 (3.3%) | 6 (2.4%) |
| Trainee | 100 (14.4%) | 61 (23.9%) |
| Other clinical job | 105 (15.1%) | 11 (4.3%) |
| Nurse or medical assistant | 33 (4.7%) | 9 (3.5%) |
| Frontline status | ||
| None | 222 (31.9%) | 44 (17.3%) |
| Indirect | 73 (10.5%) | 15 (5.9%) |
| Direct | 400 (57.6%) | 196 (76.9%) |
| Roles changed during pandemic | ||
| No | 328 (47.2%) | 144 (56.5%) |
| Yes | 367 (52.8%) | 111 (43.5%) |
| Hours changed during pandemic | ||
| No | 414 (59.6%) | 138 (54.1%) |
| Working fewer hours | 48 (6.9%) | 22 (8.6%) |
| Working more hours | 233 (33.5%) | 95 (37.3%) |
| Social support needs | ||
| None | 111 (16.0%) | 86 (33.7%) |
| A little | 167 (24.0%) | 63 (24.7%) |
| Some | 224 (32.2%) | 72 (28.2%) |
| A lot | 193 (27.8%) | 34 (13.3%) |
| Childcare needs | ||
| No child | 460 (66.2%) | 178 (69.8%) |
| Has ≥1 child, does not need more support | 57 (8.2%) | 24 (9.4%) |
| Has ≥1 child, needs a little more support | 91 (13.1%) | 32 (12.5%) |
| Has ≥1 child, needs a lot more support | 86 (12.4%) | 21 (8.2%) |
| Depressive symptoms | 5.44 (4.99) | 4.45 (5.24) |
| Anxiety symptoms | 5.62 (5.07) | 4.25 (4.50) |
| Burnout symptoms | 5.20 (1.91) | 4.78 (2.08) |
| Posttraumatic stress symptoms | 1.68 (1.40) | 1.13 (1.31) |
| Gender discrimination | 1.35 (1.58) | 0.38 (0.94) |
SD, standard deviation.
Association between gender discrimination and mental health
Tables 2 and 3 present the unadjusted and adjusted models, respectively, predicting symptoms of posttraumatic stress, depression, anxiety, and burnout, stratified by gender. After adjusting for demographics and pandemic-related stressors, women who reported higher levels of gender discrimination had heightened symptoms of posttraumatic stress (B = 0.11, standard error [SE] = 0.03, p = 0.001), depression (B = 0.27, SE = 0.12, p = 0.028), anxiety (B = 0.40, SE = 0.12, p = 0.001), and burnout (B = 0.12, SE = 0.04, p = 0.008). The partial eta squared for gender discrimination in the models, including only women ranged from 0.007 to 0.028. Other variables that explained the greatest amount of variance in these models were social support needs (partial eta squared ranged from 0.069 to 0.134) and having a history of a mental health disorder (partial eta squared ranged from 0.001 to 0.074).
Table 2.
Unadjusted Models Predicting Symptoms of Mental Health Outcomes
| Posttraumatic stress B (SE) | Depression B (SE) | Anxiety B (SE) | Burnout B (SE) | |
|---|---|---|---|---|
| Women (n = 695) | ||||
| Gender discrimination | 0.20 (0.03)*** | 0.55 (0.12)*** | 0.69 (0.12)*** | 0.34 (0.04)*** |
| Age | −0.01 (0.01)** | −0.04 (0.02)* | −0.07 (0.02)*** | −0.04 (0.01)*** |
| Race | ||||
| White (reference) | — | — | — | — |
| Southeast Asian | −0.76 (0.38)* | −1.89 (1.35) | −0.91 (1.37) | −0.99 (0.51) |
| Middle Eastern/North African | 0.31 (0.43) | −0.53 (1.52) | 0.50 (1.54) | 0.34 (0.58) |
| Black | −0.33 (0.26) | −0.76 (0.92) | −2.32 (0.93)* | −1.11 (0.35)** |
| Other | 0.86 (0.43)* | −0.17 (1.52) | 1.14 (1.54) | 1.16 (0.58)* |
| South Asian | −0.07 (0.28) | −0.18 (1.00) | −0.27 (1.02) | 0.34 (0.38) |
| East Asian | 0.12 (0.23) | −0.72 (0.81) | −1.63 (0.82)* | 0.14 (0.31) |
| Mixed race | 0.03 (0.21) | 0.59 (0.75) | 0.66 (0.76) | 0.27 (0.28) |
| Ethnicity | ||||
| Non-Latinx (reference) | — | — | — | — |
| Latinx | 0.13 (0.23) | 1.06 (0.80) | 1.19 (0.82) | 0.36 (0.31) |
| Immigrant | ||||
| Nonimmigrant (reference) | — | — | — | — |
| Immigrant | −0.14 (0.18) | 0.25 (0.62) | 0.24 (0.63) | −0.03 (0.24) |
| Marital status | ||||
| Single (reference) | — | — | — | — |
| Married/partnered | −0.37 (0.12)** | −1.59 (0.43)*** | −1.30 (0.44)** | −0.38 (0.17)* |
| Divorced/widowed | −0.38 (0.22) | −1.12 (0.79) | −1.92 (0.81)* | −1.32 (0.30)*** |
| History of mental health diagnosis | ||||
| No (reference) | — | — | — | — |
| Yes | 0.72 (0.11)*** | 3.17 (0.38)*** | 2.97 (0.39)*** | 0.36 (0.15)* |
| Job | ||||
| Physician (reference) | — | — | — | — |
| Nurse | 0.72 (0.15)*** | 1.88 (0.53)*** | 1.84 (0.54)*** | 0.30 (0.20) |
| Nonclinical | 0.06 (0.18) | 1.87 (0.66)** | 1.67 (0.67)* | −0.83 (0.25)*** |
| Health technologist | 0.36 (0.30) | 1.04 (1.09) | 0.30 (1.12) | −1.03 (0.41)* |
| Trainee | 0.28 (0.17) | 1.38 (0.61)* | 0.84 (0.62) | 0.50 (0.23)* |
| Other clinical job | 0.17 (0.17) | 0.74 (0.60) | 0.15 (0.61) | −0.61 (0.22)** |
| Nurse or medical assistant | 0.43 (0.26) | 0.68 (0.93) | 0.90 (0.95) | −0.26 (0.35) |
| Frontline status | ||||
| None (reference) | — | — | — | — |
| Indirect | −0.22 (0.19) | −0.81 (0.67) | −1.05 (0.68) | 0.56 (0.25)* |
| Direct | 0.21 (0.12) | −0.47 (0.42) | −0.51 (0.42) | 0.93 (0.16)*** |
| Roles changed during pandemic | ||||
| No (reference) | — | — | — | — |
| Yes | 0.53 (0.11)*** | 1.39 (0.38)*** | 1.17 (0.38)** | 0.54 (0.14)*** |
| Hours changed during pandemic | ||||
| No (reference) | — | — | — | — |
| Working fewer hours | 0.38 (0.21) | −0.12 (0.75) | 0.83 (0.77) | 0.71 (0.28)* |
| Working more hours | 0.55 (0.11)*** | 1.74 (0.40)*** | 1.28 (0.41)** | 0.94 (0.15)*** |
| Social support needs | ||||
| None (reference) | — | — | — | — |
| A little | 0.59 (0.16)*** | 0.90 (0.57) | 1.12 (0.57)* | 0.97 (0.21)*** |
| Some | 1.18 (0.15)*** | 2.40 (0.54)*** | 2.72 (0.54)*** | 1.83 (0.19)*** |
| A lot | 1.71 (0.15)*** | 5.09 (0.55)*** | 5.58 (0.56)*** | 2.71 (0.20)*** |
| Childcare needs | ||||
| No child (reference) | — | — | — | — |
| Has ≥1 child, does not need more support | −0.34 (0.20) | −1.84 (0.70)** | −1.05 (0.71) | 0.05 (0.27) |
| Has ≥1 child, needs a little more support | 0.01 (0.16) | −0.69 (0.57) | 0.10 (0.58) | 0.08 (0.22) |
| Has ≥1 child, needs a lot more support | 0.01 (0.17) | 0.10 (0.58) | 1.05 (0.59) | 0.50 (0.22)* |
| Men (n = 255) | ||||
| Gender discrimination | 0.12 (0.09) | 1.06 (0.35)** | 0.62 (0.30)* | 0.13 (0.14) |
| Age | −0.02 (0.01)*** | −0.05 (0.03) | −0.05 (0.02)* | −0.05 (0.01)*** |
| Race | ||||
| White (reference) | — | — | — | — |
| Southeast Asian | −0.23 (0.48) | −1.51 (1.90) | −3.13 (1.63) | −0.45 (0.75) |
| Middle Eastern/North African | −0.10 (0.45) | 1.92 (1.80) | 0.18 (1.54) | 0.97 (0.71) |
| Black | −0.10 (0.60) | −2.84 (2.39) | 0.03 (2.05) | −1.30 (0.94) |
| Other | 0.06 (0.55) | −0.64 (2.19) | 1.29 (1.87) | −0.03 (0.86) |
| South Asian | 0.90 (0.51) | −1.06 (2.03) | −0.09 (1.74) | −0.41 (0.80) |
| East Asian | 0.21 (0.32) | −0.90 (1.27) | 0.05 (1.09) | 0.57 (0.50) |
| Mixed race | −0.04 (0.33) | −0.64 (1.34) | −1.02 (1.15) | 0.83 (0.53) |
| Ethnicity | ||||
| Non-Latinx (reference) | — | — | — | — |
| Latinx | 0.06 (0.40) | 0.10 (1.62) | −0.35 (1.39) | 0.80 (0.64) |
| Immigrant | ||||
| Nonimmigrant (reference) | — | — | — | — |
| Immigrant | 0.36 (0.26) | −0.19 (1.04) | −0.24 (0.89) | 0.21 (0.41) |
| Marital status | ||||
| Single (reference) | — | — | — | — |
| Married/partnered | −0.64 (0.21)* | −1.90 (0.83)* | −0.74 (0.72) | −0.76 (0.33)* |
| Divorced/widowed | −1.05 (0.60) | −1.98 (2.45) | −2.28 (2.11) | −2.63 (0.96)** |
| History of mental health diagnosis | ||||
| No (reference) | — | — | — | — |
| Yes | 0.53 (0.21)* | 3.19 (0.83)*** | 2.38 (0.72)** | 0.27 (0.34) |
| Job | ||||
| Physician (reference) | — | — | — | — |
| Nurse | 0.48 (0.30) | 0.57 (1.20) | 1.08 (1.04) | −0.04 (0.48) |
| Nonclinical | 0.40 (0.36) | 3.82 (1.47)* | 1.81 (1.27) | −0.92 (0.58) |
| Health technologist | 0.28 (0.54) | 2.58 (2.18) | 2.81 (1.88) | 0.21 (0.86) |
| Trainee | 0.62 (0.20)** | 1.14 (0.81) | 0.82 (0.70) | 0.73 (0.32)* |
| Other clinical job | 0.57 (0.41) | 1.43 (1.64) | 1.13 (1.41) | −0.17 (0.65) |
| Nurse or medical assistant | 0.01 (0.45) | 1.25 (1.80) | 1.53 (1.55) | 0.82 (0.71) |
| Frontline status | ||||
| None (reference) | — | — | — | — |
| Indirect | 0.25 (0.39) | 1.05 (1.57) | 0.44 (1.35) | −0.77 (0.62) |
| Direct | 0.47 (0.22)* | −0.30 (0.88) | −0.19 (0.75) | 0.42 (0.35) |
| Roles changed during pandemic | ||||
| No (reference) | — | — | — | — |
| Yes | 0.58 (0.16)*** | 0.96 (0.66) | 1.03 (0.57) | 0.13 (0.26) |
| Hours changed during pandemic | ||||
| No (reference) | — | — | — | — |
| Working fewer hours | −0.11 (0.30) | 1.48 (0.22) | −0.53 (1.02) | −1.04 (0.47)* |
| Working more hours | 0.45 (0.17)* | 1.77 (0.69)* | 1.56 (0.59)** | 0.52 (0.27) |
| Social support needs | ||||
| None (reference) | — | — | — | — |
| A little | 0.21 (0.20) | −0.13 (0.85) | 0.77 (0.72) | 1.32 (0.31)*** |
| Some | 0.84 (0.19)*** | 2.51 (0.82)** | 2.15 (0.69)** | 1.86 (0.30)*** |
| A lot | 1.68 (0.24)*** | 2.54 (1.04)* | 3.65 (0.88)*** | 2.77 (0.38)*** |
| Childcare needs | ||||
| No child (reference) | — | — | — | — |
| Has ≥1 child, does not need more support | −0.49 (0.28) | −0.17 (1.14) | −0.64 (0.97) | 0.17 (0.45) |
| Has ≥1 child, needs a little more support | −0.23 (0.25) | 0.83 (1.00) | 0.18 (0.86) | 0.21 (0.40) |
| Has ≥1 child, needs a lot more support | 0.09 (0.30) | 2.31 (1.21) | 2.23 (1.03)* | 0.81 (0.48) |
p < 0.05; **p < 0.01; ***p < 0.001.
SE, standard error.
Table 3.
Adjusted Models Predicting Symptoms of Mental Health Outcomes
| Posttraumatic stress |
Depression |
Anxiety |
Burnout |
|||||
|---|---|---|---|---|---|---|---|---|
| B (SE) | Partial eta squared | B (SE) | Partial eta squared | B (SE) | Partial eta squared | B (SE) | Partial eta squared | |
| Women (n = 695) | ||||||||
| Gender discrimination | 0.11 (0.03)** | 0.016 | 0.27 (0.12)* | 0.007 | 0.40 (0.12)** | 0.028 | 0.12 (0.04)** | 0.011 |
| Age | −0.01 (0.01) | 0.002 | −0.01 (0.02) | 0.001 | −0.04 (0.02)* | 0.008 | −0.03 (0.01)*** | 0.034 |
| Race | ||||||||
| White (reference) | — | — | — | — | — | — | — | — |
| Southeast Asian | −0.27 (0.35) | 0.001 | −0.50 (1.26) | 0.001 | 0.07 (1.27) | 0.001 | −0.55 (0.45) | 0.002 |
| Middle Eastern/North African | 0.22 (0.38) | 0.001 | −1.01 (1.37) | 0.001 | −0.18 (1.38) | 0.001 | −0.29 (0.49) | 0.001 |
| Black | −0.28 (0.23) | 0.002 | −0.69 (0.83) | 0.001 | −2.33 (0.84)** | 0.011 | −1.04 (0.30)*** | 0.018 |
| Other | 0.58 (0.38) | 0.003 | −0.54 (1.39) | 0.001 | 0.44 (1.40) | 0.001 | 0.99 (0.49)* | 0.006 |
| South Asian | 0.08 (0.25) | 0.001 | 0.10 (0.92) | 0.001 | −0.27 (0.93) | 0.001 | −0.09 (0.33) | 0.001 |
| East Asian | 0.38 (0.21) | 0.005 | 0.04 (0.75) | 0.001 | −1.01 (0.76) | 0.003 | 0.05 (0.27) | 0.001 |
| Mixed race | −0.18 (0.44) | 0.001 | −0.79 (1.58) | 0.001 | −0.34 (1.60) | 0.001 | 0.53 (0.56) | 0.001 |
| Ethnicity | ||||||||
| Non-Latinx (reference) | — | — | — | — | — | — | — | — |
| Latinx | 0.26 (0.47) | 0.001 | 1.32 (1.72) | 0.001 | 0.81 (1.74) | 0.001 | −0.53 (0.61) | 0.001 |
| Immigrant | ||||||||
| Nonimmigrant (reference) | — | — | — | — | — | — | — | — |
| Immigrant | −0.24 (0.17) | 0.003 | −0.06 (0.60) | 0.001 | −0.02 (0.61) | 0.001 | −0.28 (0.22) | 0.003 |
| Marital status | ||||||||
| Single (reference) | — | — | — | — | — | — | — | — |
| Married/partnered | −0.17 (0.12) | 0.003 | −0.66 (0.45) | 0.003 | −0.55 (0.46) | 0.002 | −0.12 (0.16) | 0.001 |
| Divorced/widowed | −0.13 (0.22) | 0.001 | −0.44 (0.79) | 0.001 | −1.01 (0.80) | 0.002 | −0.58 (0.28)* | 0.006 |
| History of mental health diagnosis | ||||||||
| No (reference) | — | — | — | — | — | — | — | — |
| Yes | 0.54 (0.10)*** | 0.040 | 2.71 (0.37)** | 0.074 | 2.34 (0.38)*** | 0.055 | 0.11 (0.13) | 0.001 |
| Job | ||||||||
| Physician (reference) | — | — | — | — | — | — | — | — |
| Nurse | 0.67 (0.14)*** | 0.034 | 1.33 (0.50)** | 0.010 | 1.37 (0.51)** | 0.011 | 0.27 (0.18) | 0.003 |
| Nonclinical | 0.19 (0.19) | 0.002 | 1.15 (0.68) | 0.004 | 0.85 (0.68) | 0.002 | −0.63 (0.24)* | 0.010 |
| Health technologist | 0.88 (0.29)** | 0.014 | 1.57 (1.04) | 0.003 | 0.65 (1.05) | 0.001 | −0.39 (0.37) | 0.002 |
| Trainee | −0.13 (0.17) | 0.001 | 0.17 (0.62) | 0.001 | −0.57 (0.62) | 0.001 | −0.14 (0.22) | 0.001 |
| Other clinical job | 0.13 (0.17) | 0.001 | −0.33 (0.62) | 0.001 | −0.84 (0.62) | 0.003 | −0.52 (0.22)* | 0.008 |
| Nurse or medical assistant | 0.42 (0.24) | 0.005 | −0.44 (0.87) | 0.001 | −0.08 (0.88) | 0.001 | −0.22 (0.31) | 0.001 |
| Frontline status | ||||||||
| None (reference) | — | — | — | — | — | — | — | — |
| Indirect | −0.28 (0.17) | 0.004 | −1.03 (0.60) | 0.004 | −1.28 (0.61)* | 0.007 | 0.37 (0.22) | 0.004 |
| Direct | 0.10 (0.12) | 0.001 | −0.86 (0.45) | 0.005 | −1.24 (0.45)** | 0.011 | 0.23 (0.16) | 0.003 |
| Roles changed during pandemic | ||||||||
| No (reference) | — | — | — | — | — | — | — | — |
| Yes | 0.23 (0.10)* | 0.008 | 0.53 (0.36) | 0.003 | 0.18 (0.36) | 0.001 | −0.09 (0.13) | 0.001 |
| Hours changed during pandemic | ||||||||
| No (reference) | — | — | — | — | — | — | — | — |
| Working fewer hours | −0.02 (0.19) | 0.001 | −1.39 (0.70)* | 0.006 | −0.49 (0.71) | 0.001 | 0.22 (0.25) | 0.001 |
| Working more hours | 0.27 (0.11)* | 0.009 | 0.98 (0.39)* | 0.010 | 0.51 (0.39) | 0.003 | 0.56 (0.14)*** | 0.024 |
| Social support needs | ||||||||
| None (reference) | — | — | — | — | — | — | — | — |
| A little | 0.51 (0.15)*** | 0.017 | 0.88 (0.55) | 0.004 | 1.10 (0.56)** | 0.006 | 0.70 (0.20)*** | 0.018 |
| Some | 0.88 (0.15)** | 0.050 | 1.54 (0.54)** | 0.012 | 1.85 (0.55)*** | 0.017 | 1.37 (0.19)*** | 0.070 |
| A lot | 1.29 (0.16)*** | 0.090 | 4.07 (0.58)*** | 0.069 | 4.43 (0.59)*** | 0.079 | 2.10 (0.21)*** | 0.134 |
| Childcare needs | ||||||||
| No child (reference) | — | — | — | — | — | — | — | — |
| Has ≥1 child, does not need more support | −0.12 (0.18) | 0.001 | −1.03 (0.65) | 0.004 | −0.44 (0.65) | 0.001 | 0.28 (0.23) | 0.002 |
| Has ≥1 child, needs a little more support | −0.13 (0.15) | 0.001 | −0.96 (0.55) | 0.005 | −0.39 (0.56) | 0.001 | −0.26 (0.20) | 0.003 |
| Has ≥1 child, needs a lot more support | −0.21 (0.16) | 0.003 | −0.46 (0.56) | 0.001 | 0.16 (0.57) | 0.001 | −0.05 (0.20) | 0.001 |
| Men (n = 255) | ||||||||
| Gender discrimination | 0.05 (0.08) | 0.002 | 0.78 (0.35)* | 0.022 | 0.50 (0.31) | 0.012 | 0.16 (0.13) | 0.007 |
| Age | 0.01 (0.01) | 0.001 | −0.02 (0.04) | 0.001 | −0.03 (0.03) | 0.005 | −0.04 (0.01)** | 0.042 |
| Race | ||||||||
| White (reference) | — | — | — | — | — | — | — | — |
| Southeast Asian | −0.67 (0.43) | 0.011 | −2.53 (1.86) | 0.008 | −3.91 (1.62)* | 0.025 | −1.03 (0.67) | 0.011 |
| Middle Eastern/North African | −0.46 (0.43) | 0.005 | 0.74 (1.84) | 0.001 | −0.63 (1.61) | 0.001 | −0.04 (0.66) | 0.001 |
| Black | −0.80 (0.55) | 0.009 | −3.78 (2.35) | 0.011 | −1.43 (2.05) | 0.002 | −2.72 (0.84)** | 0.045 |
| Other | −0.80 (0.55) | 0.001 | −1.67 (2.24) | 0.002 | 0.45 (1.95) | 0.001 | −1.74 (0.80)* | 0.021 |
| South Asian | 0.72 (0.47) | 0.010 | −1.18 (2.03) | 0.002 | −0.12 (1.77) | 0.001 | −1.24 (0.73) | 0.013 |
| East Asian | −0.29 (0.30) | 0.004 | −1.38 (1.30) | 0.005 | −0.38 (1.13) | 0.001 | −0.25 (0.47) | 0.001 |
| Mixed race | −0.27 (0.49) | 0.001 | −2.13 (2.11) | 0.005 | −2.40 (1.84) | 0.008 | 0.98 (0.76) | 0.008 |
| Ethnicity | ||||||||
| Non-Latinx (reference) | — | — | — | — | — | — | — | — |
| Latinx | −0.52 (0.60) | 0.003 | 0.43 (2.59) | 0.001 | 0.62 (2.25) | 0.001 | −1.20 (0.93) | 0.007 |
| Immigrant | ||||||||
| Nonimmigrant (reference) | — | — | — | — | — | — | — | — |
| Immigrant | 0.26 (0.25) | 0.005 | −0.18 (1.09) | 0.001 | −0.18 (1.07) | 0.001 | 0.14 (0.39) | 0.001 |
| Marital status | ||||||||
| Single (reference) | — | — | — | — | — | — | — | — |
| Married/partnered | −0.32 (0.23) | 0.009 | −1.51 (0.98) | 0.011 | 0.01 (0.85) | 0.001 | −0.31 (0.35) | 0.003 |
| Divorced/widowed | −0.33 (0.57) | 0.001 | −2.48 (2.48) | 0.001 | −0.18 (2.15) | 0.001 | −1.41 (0.89) | 0.011 |
| History of mental health diagnosis | ||||||||
| No (reference) | — | — | — | — | — | — | — | — |
| Yes | 0.35 (0.20) | 0.014 | 3.00 (0.85)*** | 0.053 | 2.29 (0.74)** | 0.041 | 0.24 (0.30) | 0.003 |
| Job | ||||||||
| Physician (reference) | — | — | — | — | — | — | — | — |
| Nurse | 0.27 (0.29) | 0.004 | −0.66 (1.23) | 0.001 | 0.66 (1.07) | 0.002 | −0.64 (0.44) | 0.009 |
| Nonclinical | 0.48 (0.37) | 0.007 | −0.66 (1.23) | 0.017 | 1.30 (1.40) | 0.004 | −1.37 (0.57)* | 0.025 |
| Health technologist | 0.41 (0.52) | 0.003 | 2.66 (2.26) | 0.006 | 2.83 (1.97) | 0.009 | −0.06 (0.81) | 0.001 |
| Trainee | 0.40 (0.23) | 0.013 | 0.58 (0.99) | 0.002 | 0.17 (0.87) | 0.001 | −0.28 (0.36) | 0.003 |
| Other clinical job | 0.67 (0.40) | 0.012 | 1.68 (1.71) | 0.004 | 1.06 (1.49) | 0.002 | −0.68 (0.61) | 0.005 |
| Nurse or medical assistant | −0.08 (0.42) | 0.001 | 0.74 (1.81) | 0.001 | 1.39 (1.57) | 0.003 | 0.45 (0.65) | 0.002 |
| Frontline status | ||||||||
| None (reference) | — | — | — | — | — | — | — | — |
| Indirect | −0.36 (0.37) | 0.004 | −0.32 (1.58) | 0.001 | −0.73 (1.38) | 0.001 | −1.23 (0.57)* | 0.021 |
| Direct | 0.27 (0.23) | 0.006 | 0.05 (0.99) | 0.001 | −0.41 (0.86) | 0.001 | −0.22 (0.35) | 0.002 |
| Roles changed during pandemic | ||||||||
| No (reference) | — | — | — | — | — | — | — | — |
| Yes | 0.42 (0.16)* | 0.028 | −0.22 (0.70) | 0.001 | 0.20 (0.61) | 0.001 | −0.30 (0.25) | 0.006 |
| Hours changed during pandemic | ||||||||
| No (reference) | — | — | — | — | — | — | — | — |
| Working fewer hours | −0.21 (0.28) | 0.002 | 1.91 (1.21) | 0.011 | −0.26 (1.05) | 0.001 | −0.70 (0.43) | 0.011 |
| Working more hours | 0.18 (0.16) | 0.006 | 1.33 (0.71) | 0.016 | 1.19 (0.62) | 0.017 | 0.30 (0.25) | 0.006 |
| Social support needs | ||||||||
| None (reference) | — | — | — | — | — | — | — | — |
| A little | 0.04 (0.21) | 0.001 | −0.05 (0.90) | 0.001 | 0.73 (0.79) | 0.004 | 1.01 (0.32)** | 0.042 |
| Some | 0.73 (0.20)*** | 0.055 | 2.18 (0.86)* | 0.028 | 1.78 (0.75)* | 0.025 | 1.55 (0.31)*** | 0.101 |
| A lot | 1.52 (0.26)*** | 0.130 | 2.05 (1.14) | 0.014 | 2.79 (0.99)** | 0.034 | 2.51 (0.41)*** | 0.145 |
| Childcare needs | ||||||||
| No child (reference) | — | — | — | — | — | — | — | — |
| Has ≥1 child, does not need more support | −0.44 (0.27) | 0.012 | 0.45 (1.16) | 0.001 | −0.81 (1.01) | 0.003 | 0.41 (0.42) | 0.004 |
| Has ≥1 child, needs a little more support | −0.16 (0.24) | 0.002 | 1.66 (1.02) | 0.012 | 0.15 (0.89) | 0.001 | 0.08 (0.37) | 0.001 |
| Has ≥1 child, needs a lot more support | −0.30 (0.30) | 0.005 | 2.64 (1.29)* | 0.019 | 1.41 (1.12) | 0.007 | 0.33 (0.46) | 0.002 |
p < 0.05; **p < 0.01; ***p < 0.001.
Among men, increased gender discrimination was associated with heightened symptoms of depression (B = 0.78, SE = 0.35, p = 0.03), but not posttraumatic stress, anxiety, or burnout. The partial eta squared for gender discrimination in the models including men ranged from 0.002 to 0.022. Other variables that had higher partial eta squared values included having a history of a mental health disorder (ranged from 0.003 to 0.053) and social support needs (ranged from 0.014 to 0.145).
Experiences of gender discrimination
Among the 358 women who reported experiencing gender discrimination, 339 (94.7%) provided a response to the open-ended question asking them to describe these experiences. Five themes emerged from the open-ended responses detailing gender discrimination experienced by women: (1) differential valuing of work and contributions, (2) gendered roles and assumptions about roles, (3) maternal discrimination, (4) objectification, and (5) “old boys club.” Representative quotes for each of these themes are presented in Table 4.
Table 4.
Qualitative Themes of Experiences of Gender Discrimination
| Theme | Quotes |
|---|---|
| Women | |
| Differential valuing of work and contributions | We have systematically lower pay rates for females than males at our institution. There are also fewer opportunities for females to advance. (ID: 10892) It's not necessarily personal to me, but over and over, only men have been recognized for their work on COVID when many others, including women and younger faculty, have done a lot (or could have been given such opportunities.) (ID: 10787) One example is that I am underpaid and my contributions undervalued based on both gender and race by my primary nonclinical supervisor. I had to correct what I see as unfair exclusion from the ladder track. This has not happened among my clinical colleagues. (ID: 10084) |
| Gendered roles and assumptions about roles | The contribution of women to the clinical workload is greater than that by men, but not acknowledged and not taken into consideration in regards to lesser academic output (due to less time) from women. Women who request protected time for academic or administrative responsibilities are denied, but men who seek protected time for similar responsibilities are successful in receiving it. (ID: 10862) NO acknowledgment that I am a PHYSICIAN, let alone the ATTENDING physician by parents of a baby. They kept saying no doctor had been by to talk to them despite my updating them every day. I kept reidentifying myself and explaining my role. I am frustrated by the assumption that any male person is the doctor and any female is the nurse. (ID: 10955) When my secretary retired, hospital administration allowed/forced me to assume most of her responsibilities until her replacement starts. I don't think they would have asked a male in my role to do the clerical work I have been asked to do as a female. (ID: 10109) |
| Maternal discrimination | Just the lack of understanding of why women who are mothers may not function well with the same set of rules or expectations for men and childless people. This came up most in terms of clinical hours and scheduling and needing to have more regularity/predictability. It has become clear to me also why women don't get leadership positions and promotions in this setting because they largely don't have the reserve after managing households (even with great spouses) to play the academic medicine game I've been told to play. (ID: 11233) I was asked about my relationship status by the highest administrator in the department. I have also been asked about having children during residency. These made me very uncomfortable and were not things that were conveyed to my male counterparts. (ID: 11059) I was made to feel badly for taking maternity leave and repeatedly told comments about how men wished they could take maternity leave. “I wish I had a uterus so I could take a 3-month vacation” (ID: 10926) |
| Objectification | I have had some inappropriate comments from an attending about my ethnic background, any financial privileges they assumed I might have had growing up, and how my “breast size” could be affecting my posture. It is especially difficult in these circumstances because they're evaluating my performance and I feel like I can't speak up whenever a situation arises that makes me uncomfortable. (ID: 10615) Have been sexually harassed by patients “I can think of several things you can do for me, none of which are appropriate”; “I'd like to ask you for more but I know I would be out of line”…I feel like an imposter, like I did when I was bullied as a child, as my value and merit do not count. (ID: 10958) Some male patients use threats of violence or sexual harassment toward female physicians or NPs [nurse practitioners] that male colleagues don't experience. Level of patient agitation and violence is much increased since before pandemic. (ID: 11071) |
| “Old boys club” | I also see the “old boy network” providing subtle help—new opportunities, mentoring, career support—to younger faculty who look more like them. (ID: 10450) Male coworkers less chatty and likely to engage in developing professional relationships with me. (ID: 10007) Co medical director colleague hangs out with other male colleagues/Dept chair and talk business while they are (fishing, having dinner, smoking cigars, etc. etc.) when I am not included because it's their “buddy time”…Tells me he already talked about this or that with them. (ID: 11025) |
| Men | |
| Symbolic discrimination | I was initially a candidate for an administrative position however it was made clear that this position would be reserved for candidates of proper race and gender and I am a White male. We are in an era where “white men need not apply”… (ID: 10487) Our faculty has gradually become so inclusive that now straight, White, males are representative of an old “problem.” There is now such a strong Girls Club in our department that males are the last option for all positions and promotions. (ID: 10610) There have been circumstances where women were selected for leadership positions based on gender plus merit where males were judged on merit alone. (ID: 11085) |
| Woman provider preference | Working on Labor and delivery as a male. Patient did not want to see me as a provider because I was male. It made me feel upset that I am not to be trusted because I am male. (ID: 10347) Patient's refusing ob/gyn care based on my sex. Felt annoyed that I had to go find a colleague to help advance my work ups. (ID: 11105) Nursing staff did not want males on the OB [obstetrics] floor; kept giving comments about men being a problem; men are the source of the medical issues; etc. It made me feel angry, inadequate, and sorry for the patients who were denied care because of my gender. (ID: 11027) |
First, women described that they perceived that colleagues, supervisors, and patients view their work as less valuable compared with men. Women described that this led to fewer opportunities for promotion, inequitable pay, fewer supports (e.g., office space), and decreased recognition of their work from their supervisors and hospital administration. Respondents also explained that patients devalued their work by preferring and requesting to have a man provide care to them. Women identifying as a racialized minority described that they perceived this differential valuing to be due to their race as well as gender.
Second, women described that their roles and assumptions about their roles in the workplace were gendered. For example, respondents described that they were asked to take on gendered work tasks, such as secretarial tasks, even when their job description did not merit it. Other respondents described how they were asked to take on more clinical work, and were not provided protected time for other duties, such as research, administrative, and leadership tasks, while men were provided with these supports. Women also described that patients contributed to gendered assumptions about their roles in the hospital; women physicians in particular described being repeatedly misidentified as a nurse, even after introducing themselves as a physician.
Third, women described that they were discriminated based on their roles as mothers. Many HCWs described that there was a lack of support given to mothers in the health system; for example, some described that they were not granted flexible schedules to accommodate their family responsibilities. HCWs also provided examples of how their colleagues and supervisors made them feel guilty and judged for taking maternity leave, and assumed that they would not return to work. Even among women who were not currently mothers, respondents described that colleagues and patients fixated on their decision to rear children.
Fourth, women described being objectified, such as routine comments about their appearance and being sexually harassed. Women described that comments on their appearance, such as weight, body shape, and hair, were made by patients, colleagues, and supervisors, and made them feel devalued. Others described that they were explicitly sexually harassed by patients or colleagues.
Lastly, women explained that there was increased social capital associated with being a man in health care through the engrained “old boys club.” Women described that this culture leads to implicit feelings of exclusion from the inner circle of men. For example, women described instances of exclusion from social events outside of work where only men were invited. They also described instances of more implicit exclusion, for example, they described that men were less “chatty” with women and that men in more senior roles were less likely to mentor women in junior roles.
Among the 47 men who reported gender discrimination, 41 (87.2%) provided a response to the open-ended question asking about these experiences. We identified two themes related to gender discrimination experienced by men: (1) symbolic discrimination and (2) woman provider preference. Representative quotes for each of these themes are presented in Table 4.
First, men described that they now receive fewer opportunities for career advancement and promotion because many departments have initiatives to increase representation of women and racialized minorities in leadership roles. Men who reported this described that they are not only judged based on their merit for these roles, but also based on their gender and racial identity, which they perceive as unfair.
Next, men described that patients sometimes prefer having a woman provider. Men who reported this issue were all involved in obstetrics and gynecology care. Men reported feeling upset that they could not provide care for patients or were not trusted by patients based on their gender.
Discussion
In the present study, we assessed the association of gender discrimination and four stress-related mental health outcomes, in addition to the narratives of these experiences. We found that increased gender discrimination was associated with heightened symptoms of posttraumatic stress, depression, anxiety, and burnout among women, but only with symptoms of depression among men. We also identified five themes that describe various experiences of gender discrimination that women face in health care, including differential valuing of work and contributions, gendered roles and assumptions about roles, maternal discrimination, objectification, and “old boys club.” Lastly, men described instances of symbolic discrimination and that patients prefer woman providers over men.
Our findings align with prior literature that has established gender discrimination as a risk factor for several negative mental health consequences.2,7,13 The quantitative results of our analysis extend this literature by demonstrating that gender discrimination is a significant predictor of not only burnout, but also posttraumatic stress, depression, and anxiety symptoms among women. Among men, gender discrimination was only associated with heightened symptoms of depression, suggesting that the mental health toll of gender discrimination may be more profound for women HCWs. However, we found that gender discrimination explained a modest proportion of variance in mental health outcomes, and other variables that described a greater proportion of variance in mental health outcomes included having a history of a mental health disorder and needing additional social support. These findings suggest that gender discrimination is important when considering the mental health of HCWs, but there are also other influencing factors that should be taken into account.
Our qualitative results largely echo women's experiences of gender discrimination that have been documented in several prior studies. For example, differential valuing of work and contributions is a widely documented form of gender discrimination in the health care field. This has been reflected in quantitative studies that have examined pay gaps,5,43 promotion disparities,6,7 and a lack of respect19 that align with our sample's experiences of differential valuing. Gendered roles and assumptions about roles are also common experiences of gender discrimination that women face, such as women physicians being mistaken for nonphysicians.1,15 Maternal discrimination is another extremely widespread phenomenon in the medical field,9 a finding that was reflected in our sample's reports of poor scheduling flexibility and overt discrimination against HCWs who are mothers. Objectification and sexual harassment also continue to be major themes in this literature. Across several studies that measured sexual harassment among HCWs, women were significantly more likely to experience it than their male counterparts,2,3,7,19 a finding that is consistent with the experiences of sexual harassment in our sample.
The themes we uncovered in our qualitative analysis add a unique dimension to prior findings, as they present a more nuanced picture of the largely quantitative results in previous literature and are filtered through the lens of COVID-19. For example, the theme of the “old boys club” implicitly excluding women from the inner circle was not examined in previous, similar studies of gender discrimination.24 Although women HCWs have reported feeling as though there is an “old boys club” in medicine44 and have felt deterred away from certain specialties due to this phenomenon in prior studies,45 these findings do not fully capture the experiences of women HCWs in our sample, who expressed feeling that they do not have as much social capital as their counterparts who identify as men and that there is a social hierarchy that often excludes them. This finding could have emerged due to our examination of gender discrimination after the initial waves of the pandemic when socializing was less restricted, potentially making social exclusion a more central experience for women HCWs.
Our qualitative analysis also provided a nuanced understanding of how men experience gender discrimination in health care. Men, particularly those in the field of obstetrics and gynecology, described that patients prefer women providers. While other studies have found that patients do prefer women providers in this field,20 the present study provides insights into how this impacts men's affect when this occurs, including feelings of anger, unappreciation, and frustration. Furthermore, men described instances of symbolic discrimination, which occurs when men, White men in particular, perceive disadvantage when equity programs provide opportunities to racialized minorities and gender minorities.21,22 In the present study, symbolic discrimination took the form of men perceiving that they are disadvantaged when women and racialized minorities are given promotions or career opportunities over White men.
Although other studies have identified that nurses identifying as men face gender discrimination in the workplace,16 our qualitative analysis did not identify any themes related to differences in gender discrimination experienced by men in various health care professions. This could be due to the relatively small number of male nurses in our sample, and future studies should better explore the mental health toll and narrative experiences of gender discrimination faced by men in nursing.
Our study was strengthened by its mixed-methods approach, which allowed us to assess the quantifiable associations between gender discrimination and mental health as well as narrative experiences. The qualitative component in particular allowed for examination of the more implicit forms of gender discrimination, such as exclusion, as well as instances that may fall outside of strict definitions of experiences such as sexual harassment or pay discrimination, but that still cause distress. Another strength of this study is that the sample included both women and men, allowing for comparison of how women and men differentially experience gender discrimination. The examination of several pandemic-related stressors also enabled us to adjust for stressors that could have confounded the associations between gender discrimination and mental health outcomes.
However, there were still several limitations to the present study. First, the sample was largely dominated by non-Latinx White women, making it nonrepresentative of the health care workforce at large. The survey that yielded these data was also only sent to academic health care systems, leaving a gap in examining the experiences of HCWs who work in nonacademic health care settings. Furthermore, participation in the survey required internet access in addition to English language literacy; thus, HCWs from lower socioeconomic backgrounds and who primarily speak a language other than English might have not been able to participate. The perpetrators of gender discrimination were also not a factor examined in the present analysis; future work could add more nuance to our findings by examining whether associations differ by whether HCWs experience discrimination from their patients, colleagues, supervisors, and hospital leaders.
Lastly, our study was not statistically powered to assess for differences in the mental health toll of gender discrimination faced by women in different health care professions and identifying as different racial groups; future work should focus on understanding the ways in which health care role and racial identity interact to influence the experiences and impact of gender discrimination.
Conclusion
We found that gender discrimination is a significant predictor of stress-related mental health outcomes among women HCWs, even when adjusting for demographic risk factors and pandemic-related stressors. We also identified five themes of gender discrimination that further described the experiences of the women in our sample. Hospitals can and should use these data to improve the experience of women HCWs through the implementation of evidence-based initiatives like increasing mentorship for women in medicine and providing more robust maternity leave and childcare support.
Authors' Contributions
R.H.: Conceptualization, methodology, formal analysis, investigation, data curation, writing—original draft, and funding acquisition. R.P.: Formal analysis and writing—review and editing. H.G.: Formal analysis and writing—review and editing. S.R.L.: Conceptualization, methodology, formal analysis, investigation, data curation, writing—review and editing, supervision, and funding acquisition.
Disclaimer
The funders had no role in the study design, data collection, and analysis, decision to publish, or preparation of the article.
Author Disclosure Statement
The authors have no conflicts of interest to disclose.
Funding Information
R.H. and S.R.L. received funding support from Yale University's COVID-19 Response Coordination Team. R.H. received funding support from the National Institutes of Health Medical Scientist Training Program (Training Grant T32GM007205).
References
- 1. Schlick CJR, Ellis RJ, Etkin CD, et al. Experiences of gender discrimination and sexual harassment among residents in general surgery programs across the US. JAMA Surg 2021;156:942–952. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Wang LJ, Tanious A, Go C, et al. Gender-based discrimination is prevalent in the integrated vascular trainee experience and serves as a predictor of burnout. J Vasc Surg 2020;71(1):220–227. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Vargas EA, Brassel ST, Cortina LM, et al. #MedToo: A large-scale examination of the incidence and impact of sexual harassment of physicians and other faculty at an academic medical center. J Womens Health (Larchmt) 2020;29(1):13–20. [DOI] [PubMed] [Google Scholar]
- 4. Lo Sasso AT, Richards MR, Chou C-F, et al. The $16,819 pay gap for newly trained physicians: The unexplained trend of men earning more than women. Health Aff (Millwood) 2011;30(2):193–201. [DOI] [PubMed] [Google Scholar]
- 5. Muench U, Sindelar J, Busch SH, et al. Salary differences between male and female registered nurses in the United States. JAMA 2015;313(12):1265–1267. [DOI] [PubMed] [Google Scholar]
- 6. Richter KP, Clark L, Wick JA, et al. Women physicians and promotion in academic medicine. N Engl J Med 2020;383(22):2148–2157. [DOI] [PubMed] [Google Scholar]
- 7. Chen W, Schilling BK, Bourne DA, et al. A report of gender bias and sexual harassment in current plastic surgery training: A national survey. Plast Reconstr Surg 2021;147(6):1454–1468. [DOI] [PubMed] [Google Scholar]
- 8. Lu L, Dong M, Lok GKI, et al. Worldwide prevalence of sexual harassment towards nurses: A comprehensive meta-analysis of observational studies. J Adv Nurs 2020;76(4):980–990. [DOI] [PubMed] [Google Scholar]
- 9. Adesoye T, Mangurian C, Choo EK, et al. Perceived discrimination experienced by physician mothers and desired workplace changes: A cross-sectional survey. JAMA Intern Med 2017;177(7):1033–1036. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Barnes KL, Dunivan G, Sussman AL, et al. Behind the mask: An exploratory assessment of female surgeons' experiences of gender bias. Acad Med 2020;95(10):1529–1538. [DOI] [PubMed] [Google Scholar]
- 11. Sudol NT, Guaderrama NM, Honsberger P, et al. Prevalence and nature of sexist and racial/ethnic microaggressions against surgeons and anesthesiologists. JAMA Surg 2021;156(5):e210265. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Hennein R, Bonumwezi J, Nguemeni Tiako MJ, et al. Racial and gender discrimination predict mental health outcomes among healthcare workers beyond pandemic-related stressors: Findings from a cross-sectional survey. Int J Environ Res Public Health 2021;18(17):9235. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Anderson N, Lett E, Asabor EN, et al. The association of microaggressions with depressive symptoms and institutional satisfaction among a national cohort of medical students. J Gen Intern Med 2022;37(2):298–307. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Moore LR, Ziegler C, Hessler A, et al. Burnout and career satisfaction in women neurologists in the United States. J Womens Health (Larchmt) 2019;28(4):515–525. [DOI] [PubMed] [Google Scholar]
- 15. Berwick S, Calev H, Matthews A, et al. Mistaken identity: Frequency and effects of gender-based professional misidentification of resident physicians. Acad Med 2021;96(6):869–875. [DOI] [PubMed] [Google Scholar]
- 16. Mao A, Cheong PL, Van IK, et al. “I am called girl, but that doesn't matter”—perspectives of male nurses regarding gender-related advantages and disadvantages in professional development. BMC Nurs 2021;20(1):24. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Ko M, Dorri A. Primary care clinician and clinic director experiences of professional bias, harassment, and discrimination in an underserved agricultural region of California. JAMA Netw Open 2019;2(10):e1913535. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Lu DW, Lall MD, Mitzman J, et al. #MeToo in EM: A multicenter survey of academic emergency medicine faculty on their experiences with gender discrimination and sexual harassment. West J Emerg Med 2020;21(2):252–260. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Bhandari S, Jha P, Cooper C, et al. Gender-based discrimination and sexual harassment among academic internal medicine hospitalists. J Hosp Med 2021;16(2):84–89. [DOI] [PubMed] [Google Scholar]
- 20. Turrentine M, Ramirez M, Stark L, et al. Role of physician gender in the modern practice of obstetrics and gynecology: Do obstetrician-gynecologists perceive discrimination from their sex? South Med J 2019;112(11):566–570. [DOI] [PubMed] [Google Scholar]
- 21. Sears DO, Henry PJ. Over thirty years later: A contemporary look at symbolic racism. Adv Exp Soc Psychol 2005;37(1):95–125. [Google Scholar]
- 22. Hennein R, Tineo P, Bonumwezi J, et al. “They Wanted to Talk to a ‘Real Doctor’”: Predictors, perpetrators, and experiences of racial and ethnic discrimination among healthcare workers. J Gen Intern Med 2021:1–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Cerdeña JP, Rehman T, Hardeman RR. Why bias matters in medicine: Qualitative insights from anonymous, online reports. J Natl Med Assoc 2020;112(1):6–14. [DOI] [PubMed] [Google Scholar]
- 24. Hennein R, Gorman H, Chung V, et al. Gender discrimination among women healthcare workers: Findings from a mixed methods study. PLoS One 2023;18(2):e0281367. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Hennein R, Mew EJ, Lowe SR. Socio-ecological predictors of mental health outcomes among healthcare workers during the COVID-19 pandemic in the United States. PLoS One 2021;16(2):e0246602. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect Dis 2020;20(5):533–534. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Vu M, Li J, Haardörfer R, et al. Mental health and substance use among women and men at the intersections of identities and experiences of discrimination: Insights from the intersectionality framework. BMC Public Health 2019;19(1):1–13. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Cameron RP, Gusman D. The primary care PTSD screen (PC-PTSD): Development and operating characteristics. Prim Care Psychiatry 2003;9(1):9–14. [Google Scholar]
- 29. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: Validity of a brief depression severity measure. J Gen Intern Med 2001;16(9):606–613. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Spitzer RL, Kroenke K, Williams JB, et al. A brief measure for assessing generalized anxiety disorder: The GAD-7. Arch Intern Med 2006;166(10):1092–1097. [DOI] [PubMed] [Google Scholar]
- 31. West CP, Dyrbye LN, Sloan JA, et al. Single item measures of emotional exhaustion and depersonalization are useful for assessing burnout in medical professionals. J Gen Intern Med 2009;24(12):1318–1321. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32. Pappa S, Ntella V, Giannakas T, et al. Prevalence of depression, anxiety, and insomnia among healthcare workers during the COVID-19 pandemic: A systematic review and meta-analysis. Brain Behav Immun 2020:88;901–907. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33. Lai J, Ma S, Wang Y, et al. Factors associated with mental health outcomes among health care workers exposed to coronavirus disease 2019. JAMA Netw Open 2020;3(3):e203976. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34. Mata DA, Ramos MA, Bansal N, et al. Prevalence of depression and depressive symptoms among resident physicians: A systematic review and meta-analysis. JAMA 2015;314(22):2373–2383. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35. Jackson T, Provencio A, Bentley-Kumar K, et al. PTSD and surgical residents: Everybody hurts… sometimes. Am J Surg 2017;214(6):1118–1124. [DOI] [PubMed] [Google Scholar]
- 36. Chutiyami M, Cheong AMY, Salihu D, et al. COVID-19 pandemic and overall mental health of healthcare professionals globally: A meta-review of systematic reviews. Front Psychiatry 2021;12:804525. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37. Peccoralo LA, Pietrzak RH, Feingold JH, et al. A prospective cohort study of the psychological consequences of the COVID-19 pandemic on frontline healthcare workers in New York City. Int Arch Occup Environ Health 2022:1–13. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38. Frank E, Zhao Z, Fang Y, et al. Experiences of work-family conflict and mental health symptoms by gender among physician parents during the COVID-19 pandemic. JAMA Netw Open 2021;4(11):e2134315. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39. National Health and Nutrition Examination Survey. Available from: https://wwwn.cdc.gov/nchs/nhanes/Search/variablelist.aspx?Component=Questionnaire [Last accessed: October 17, 2020].
- 40. IBM. SPSS statistics for windows [computer program]. Version 26.0. IBM Corp.: Armonk, NY; 2019.
- 41. Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol 2006;3(2):77–101. [Google Scholar]
- 42. Hall WA, Callery P. Enhancing the rigor of grounded theory: Incorporating reflexivity and relationality. Qual Health Res 2001;11(2):257–272. [DOI] [PubMed] [Google Scholar]
- 43. Weaver AC, Wetterneck TB, Whelan CT, et al. A matter of priorities? Exploring the persistent gender pay gap in hospital medicine. J Hosp Med 2015;10(8):486–490. [DOI] [PubMed] [Google Scholar]
- 44. Georgi M, Morka N, Patel S, et al. The impact of same gender speed-mentoring on women's perceptions of a career in surgery: A prospective cohort study. J Surg Educ 2022;79:1166–1176. [DOI] [PubMed] [Google Scholar]
- 45. Gargiulo DA, Hyman NH, Hebert JC. Women in surgery: Do we really understand the deterrents? Arch Surg 2006;141(4):405–407; discussion 407–408. [DOI] [PubMed] [Google Scholar]