Trends in Canadian ophthalmology residency match outcomes
Bibliographic record
Abstract
BACKGROUND: To date, there exists no formal assessment of the competitiveness of the residency match for Canadian ophthalmology programs. The primary objective of this study was to use Canadian Resident Matching Service (CaRMS) data to describe trends in the number of positions, number of applicants and level of competition for the Canadian ophthalmology match. METHODS: The number of positions and the number of applicants for each ophthalmology program were received from CaRMS for each cycle of the match from 2006-2017. The level of competition was calculated by dividing total number of applicants by the total number of positions in any given year. RESULTS: The level of competition was consistently high with a median number of 2.0 applicants per anglophone Canadian Medical Graduate (CMG) position, 2.6 applicants per francophone CMG position and 32.5 applicants per International Medical Graduate (IMG) position. Over the study period, the level of competition decreased for francophone CMG and IMG positions and did not change for anglophone CMG positions. CONCLUSION: Consistently there are a greater number of applicants than positions for Canadian ophthalmology residency programs and therefore CMG applicants should be encouraged to apply to more than one discipline. The trends in the number of residency positions can be used to update supply projections for ophthalmologists and guide human resource planning.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".