The quest for greater equity: a national cross-sectional study of the experiences of Black Canadian medical students
Bibliographic record
Abstract
BACKGROUND: Black medical students have been consistently underrepresented in Canadian medical schools, and data on the impact of discrimination on their medical education remain limited. In this cross-sectional study, we aimed to investigate the experiences of Black medical students through the Black Medical Students' Association of Canada (BMSAC). METHODS: We developed a 63-item instrument around the domains of inclusion and diversity, wellness, discrimination, career advancement and diversity in medical education. The anonymous web-based questionnaire was sent to 128 medical students and first-year residents from all 17 Canadian medical schools via the BMSAC listserv. We obtained frequencies for demographic data and self-reported experiences. RESULTS: We received 52 responses. Of respondents, 59% had at least 1 personal encounter with discrimination in medical school. Discrimination was experienced in both clinical and academic contexts, notably from patients, peers and hospital staff. Students further along in their medical training were more likely to endorse having experienced discrimination in medical school. Most respondents had positive experiences with academic and clinical inclusion, as well as resiliency in the face of discrimination. However, most respondents had negative experiences relating to reporting discrimination, their well-being, career advancement, sentiments of minority tax and low diversity in medical education. INTERPRETATION: We found that discrimination has important implications on the learning experiences of Black medical students surveyed from the BMSAC. This directly challenges the notion that Canadian medical schools are impervious to racism and highlights the need for advocacy and systemic changes to eliminate institutional racism.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".