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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 source (direct Gemma or distilled Codex), 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".