The Allocation of Medical School Spaces in Canada by Province and Territory: The Need for Evidence-Based Health Workforce Policy
Bibliographic record
Abstract
BACKGROUND: Most Canadian medical schools allocate admission based on province or territory of residence. This may result in inequities in access to medical school, disadvantaging highly qualified students from particular provinces. METHOD: The number of medical school spaces available to applicants from each province and territory was compared to the total number of available spaces in Canada, the regional application pressure and enrolment in 2017/2018. RESULTS: There is differential access to medical schools based on the absolute numbers of available spaces and application pressure. Applicants from Prince Edward Island are afforded the greatest number of spaces per 100,000 population aged 20 to 29 (5,568.8). Applicants from Ontario experience the lowest ratio of available spaces to relevant population (54.3). DISCUSSION: Health workforce policy must balance equity and regional social accountability. Privileging regional residence over academic aptitude and personal characteristics may be justified by strong evidence that these applicants are likely to serve populations that would otherwise be underserved. CONCLUSION: The availability of medical school spaces in Canada differs as a function of the province or territory from which applicants apply. Determining whether this differential is justified requires appraisal of the consequences of the policies with respect to their goals.
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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 | 0.025 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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 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".