Tracking Indigenous Applicants Through the Admissions Process of a Socially Accountable Medical School
Bibliographic record
Abstract
PURPOSE: To describe the admissions process and outcomes for Indigenous applicants to the Northern Ontario School of Medicine (NOSM), a Canadian medical school with the mandate to recruit students whose demographics reflect the service region's population. METHOD: The authors examined 10-year trends (2006-2015) for self-identified Indigenous applicants through major admission stages. Demographics (age, sex, northern and rural backgrounds) and admission scores (grade point average [GPA], preinterview, multiple mini-interview [MMI], final), along with score-based ranks, of Indigenous and non-Indigenous applicants were compared using Pearson chi-square and Mann-Whitney tests. Binary logistic regression was used to assess the relationship between Indigenous status and likelihood of admission outcomes (interviewed, received offer, admitted). RESULTS: Indigenous qualified applicants (338/17,060; 2.0%) were more likely to be female, mature (25 or older), or of northern or rural background than non-Indigenous applicants. They had lower GPA-based ranks than non-Indigenous applicants (P < .001) but had comparable preinterview-, MMI-, and final-score-based ranks across all admission stages. Indigenous applicants were 2.4 times more likely to be interviewed and 2.5 times more likely to receive an admission offer, but 3 times less likely to accept an offer than non-Indigenous applicants. Overall, 41/338 (12.1%) Indigenous qualified applicants were admitted compared with 569/16,722 (3.4%) non-Indigenous qualified applicants. CONCLUSIONS: Increased representation of Indigenous peoples among applicants admitted to medical school can be achieved through the use of socially accountable admissions. Further tracking of Indigenous students through medical education and practice may help assess the effectiveness of NOSM's social accountability admissions process.
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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 | 0.001 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.057 | 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".