Gender-Based Discrimination Among Medical Students: A Cross-Sectional Study in Brazil
Bibliographic record
Abstract
INTRODUCTION: Gender-based discrimination (GBD) creates a hostile environment during medical school, affecting students' personal life and academic performance. Little is known about how GBD affects the over 204,000 medical students in Brazil. This study aims to explore the patterns of GBD experienced by medical students in Brazil. METHODS: This is a cross-sectional study using an anonymous, Portuguese survey disseminated in June 2021 among Brazilian medical students. The survey was composed of 24 questions to collect data on GBD during medical school, formal methods for reporting GBD, and possible solutions for GBD. RESULTS: Of 953 responses, 748 (78%) were cisgender women, 194 (20%) were cisgender men, and 11 (1%) were from gender minorities. 65% (616/942) of respondents reported experiencing GBD during medical school. Women students experienced GBD more than men (77% versus 22%; P < 0.001). On comparing GBD perpetrator roles, both women (82%, 470/574) and men (64%, 27/42) reported the highest rate of GBD by faculty members. The occurrence of GBD by location differed between women and men. Only 12% (115/953) of respondents reported knowing their institution had a reporting mechanism for GBD. CONCLUSIONS: Most respondents experienced GBD during medical school. Cisgender women experienced GBD more than cisgender men. Perpetrators and location of GBD differed for men and women. Finally, an alarming majority of students did not know how to formally report GBD in their schools. It is imperative to adopt broad policy changes to diminish the rate of GBD and its a consequential burden on medical 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 | 0.051 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".