Black students applying and admitted to medicine in the province of Quebec, Canada: what do we know so far?
Bibliographic record
Abstract
To address the underrepresentation of Black students in medical schools in Canada and identify barriers in selection processes, we compare data from the latest Canadian census to that of an exit-survey conducted after a situational judgment test (Casper) among medical school applicants and from questionnaires done after selection interviews in Quebec, Canada. The proportion of Black people aged 15-34 years old in Quebec in 2016 was 5.3% province-wide and 8.2% in the Montreal metropolitan area. The proportion in the applicant pool for 2020 in Quebec was estimated to be 4.5% based on Casper exit-survey data. Comparatively, it is estimated that Black people represented 1.8% of applicants invited to admission interviews and 1.2% of admitted students in Quebec in 2019. Although data from different cohorts and data sources do not allow for direct comparisons, these numbers suggest that Black students applying to medical school are disproportionately rejected at the first step compared to non-Black students. Longitudinal data collection among medical school applicants will be necessary to monitor the situation. Further studies are required to pinpoint the factors contributing to this underrepresentation, to keep improving the equity of our selection processes.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".