Trends in ophthalmology applicants going unmatched in the Canadian Resident Matching Service
Bibliographic record
Abstract
Background: Applicants to ophthalmology have high rates of going unmatched during the CaRMS process, but how this compares to other competitive or surgical specialties remains unclear. Our research aims to examine this phenomenon by identifying trends and comparing match data with other specialties, to identify disparities that may inform the need for future interventions to improve the match process for applicants. Methods: We used a cross-sectional analysis of data provided by CaRMS on the residency match from 2013 to 2022. Results: We obtained data from 608 ophthalmology, 5,153 surgery, and 3,092 top five (most competitive) specialty first choice applicants from 2013-2022. Ophthalmology applicants were more likely to go unmatched (18.9% [120/608]) than applicants to the top five (11.9% [371/3,092]) and surgical (13.5% [702/5,153]) specialties (p<0.001) and were twice as likely to rank no alternate disciplines (31.8%, p < 0.001) over the study period. In the first iteration, when alternate disciplines were ranked, the match rate to alternate disciplines was highest for ophthalmology applicants (0.41, p < 0.001). The majority (57.8%) of unmatched ophthalmology applicants do not participate in the second iteration. Conclusion: Compared to other competitive specialties, first choice ophthalmology applicants were more likely to go unmatched, rank no alternate disciplines, and choose not to participate in the second iteration. Ophthalmology applicant behaviours should be further studied to help explain these study findings.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".