Performance of Black and Indigenous applicants in a medical school admissions process
Bibliographic record
Abstract
Background: Diversity in medical schools has lagged behind Canada’s growing multicultural population. Dalhousie medical school allows Black and Indigenous applicants to self-identify. We examined how these applicants performed and progressed through the admissions process compared to Other group (applicants who did not self-identify). Methods: Retrospective analysis of four application cycles (2015-2019) was conducted, comparing demographic data, scores for application components (Computer-Based Assessment for Sampling Personal Characteristics (CASPer), MCAT, GPA, supplemental, discretionary, Multiple Mini Interview (MMI)), and final application status between the three groups. Results: Of 1322 applicants, 104 identified as Black, 64 Indigenous, and 1154 Other. GPA was higher in the Other compared to the Indigenous group (p < 0.001). CASPer score was higher in the Other compared to the Black group (p = 0.047). There was no difference between groups for all other application components. A large proportion of Black and Indigenous applicants had incomplete applications. Acceptance rates were similar between all groups. Black applicants declined an admission offer substantially more than expected (31%; p < 0.001). Conclusions: Black and Indigenous applicants who completed their application progressed well through the admissions process. The pool of diverse applicants needs to be increased and support provided for completion of applications. Further study is warranted to understand why qualified applicants decline acceptance.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".