Are Postgraduate Medical Residency Training Positions in Atlantic Canada Evenly Distributed?
Bibliographic record
Abstract
Background The distribution of postgraduate medical training (residency) positions in Canada is administered by medical schools and universities in conjunction with individual provinces. In Atlantic Canada, the Maritime provinces are considered a single unit under Dalhousie University in Nova Scotia (NS), although distributed medical undergraduate education through Dalhousie and Sherbrooke has enabled medical students to complete their entire course of study in New Brunswick (NB). It is unclear if postgraduate medical education has been distributed in a similar fashion in Atlantic Canada, particularly in New Brunswick and Prince Edward Island (PE). Methods Data on the number of R1 residency positions was obtained from the Canadian Resident Matching Service (CaRMS) database. The distribution of R1 positions was described and compared nationally and through the Atlantic provinces. The analysis was completed using MS Excel and Prism. Results Rates of R1 positions per million persons varied widely; the national median rate was 97 positions per million persons, with a range of 34 to 138. The combined Maritime provinces rate of R1 positions was 71 per million persons and the rate in Newfoundland (NL) was 138 positions per million. The NS rate was 106 positions per million while the NB rate was 54 per million and the PE rate 34 per million. Sixty-four percent of all residency training positions in Atlantic Canada were based in the two most urban areas of Halifax, NS or St John's, NL. Royal College (specialty) positions were more likely to be based at the main university campus city than family medicine training positions (97 vs. 3%; 33 vs. 67%, respectively). Conclusion There is a high level of variation in available residency positions among the individual provinces, especially in Atlantic Canada. The lower prevalence of opportunities in NB and PE may influence the ability of these provinces to recruit and retain new physicians.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".