The association between applicant gender and racial or ethnic identity and success in the admissions process at a Canadian medical school: a prospective cohort study
Bibliographic record
Abstract
Background: Canadian data suggests that Black candidates may be less successful than other groups when applying to medical school. We sought to comprehensively describe the racial and/or ethnic identity, gender identity, sexual orientation, and ability of applicants to a single Canadian medical school. We also examined for an association between success at each application stage and applicant gender and racial identity. Methods: Class of 2024 applicants to a single Canadian medical school were invited to complete a demographics survey. The odds of achieving each application stage (offered an interview, offered a position, and matriculating) were determined for each demographic group. Results: There were 595 participants (32.4% response rate). The demographics of the applicant pool and matriculating class were similar. There was no difference in interview offers or matriculation between BIPOC and white candidates. Cisgender men were overrepresented in interviews compared to cisgender women (OR 0.64; 95%CI 0.43-0.95; p = 0.03) but not in matriculation. BIPOC cisgender women received more interview invitations compared to other groups (OR 2.74, 95%CI 1.20-6.25; p = 0.02). Conclusions: Differences in applicant success for differing demographic groups were most pronounced being offered an interview.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".