Medical School Negatively Affected Underrepresented Minority Student Wellness: A Rapid Review
Bibliographic record
Abstract
Introduction: Medical school is an important time for aspiring physicians, shaping their identities and well-being. The wellness of underrepresented minority students has recently gained attention due to unique stressors leading to isolation, imposter syndrome, and burnout, affecting academic performance and patient care. This rapid review aimed to provide a comprehensive examination of the factors influencing the wellness of underrepresented minority medical students and identify initiatives that contribute to diversity, equity, and inclusion in medical education, advocating for a supportive learning environment. Methods: The PsycINFO [OVID] and MEDLINE [OVID] databases were used to search for relevant peer-reviewed manuscripts between March 2014 and March 2024. Covidence was utilized for screening. During the initial screening round, titles and abstracts were reviewed. The findings were synthesized narratively and categorized into six common themes. The literature was summarized into racial microaggressions, LGBTQ+ medical student experiences, African American and Asian American medical student experiences, multiple marginalized identities, and the medical school learning environment. Results: 17 studies were relevant to the criteria and were included in the review. Unique groups (Latinas, African Americans, Asian Americans, and LGBTQ+) all faced racial microaggressions that negatively impacted their mental well-being. It was found that each of these groups had different types of stressors and challenges. Students with multiple marginalized identities reported increased mistreatment and discrimination during medical school. A negative medical school learning environment also increases depression symptoms among students. Discussion: Factors such as racial microaggressions, multiple marginalized identities, and a negative medical school environment contribute to poor student mental health. Understanding racial differences is crucial for providing tailored support and interventions for each group. Women's findings were excluded from this paper because women now make up the majority in medical school. This study was not without its limitations, including a single reviewer, potential publication, and response bias. Conclusion: Medical schools should focus on adopting inclusive policies, mentorship programs, and mental health initiatives that cater specifically to the needs of underrepresented minority medical students. Future research should examine the long-term impact of these educational interventions on the wellness and success of these students.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.015 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".