Geographic distribution of ophthalmology residency matches: a comparison of pre- and post-virtual interview cycles in Canada
Bibliographic record
Abstract
OBJECTIVE: To determine the impact of virtual interviews on the geographic distribution of matched ophthalmology residency applicants. DESIGN: A retrospective database review. PARTICIPANTS: A total of 334 residents commencing ophthalmology training from 2016 to 2024 in 15 Canadian ophthalmology residency programs. METHODS: We gathered publicly available information to compare match outcomes pre-virtual (2016-2019) to virtual interviews (2020-2024). Variables collected included resident names, medical school, and residency program location. Chi-squared tests were conducted to determine whether there was a significant difference in the proportion of applicants matching in the same region, province, and institution as their medical schools in pre-virtual versus virtual interview cycles. RESULTS: A total of 292 residents (88% completion rate) were identified. Home program matches were similar between the pre-virtual (51.18%) and virtual cohorts (49.70%). Same-province but different-school matches decreased slightly (18.90% pre-virtual vs 16.36% virtual), while same-region but different-province matches (6.30% vs 6.06%) were similar. Conversely, inter-regional matches increased from 23.62% pre-virtual to 27.88% virtual. Chi-squared analysis showed no statistically significant difference in geographic placement between the two groups (χ² = 0.80; p = 0.85). CONCLUSIONS: The transition to virtual interviews in Canadian ophthalmology residency programs did not significantly alter the geographic distribution of matched applicants. While concerns exist that virtual interviews might limit applicants' ability to explore programs outside their home institutions, our findings suggest that other virtual engagement methods, such as online networking, mentorship programs, and social media, may have helped maintain geographic mobility. Future studies could assess whether these trends persist over time and whether similar strategies could enhance applicant program exposure in other specialties.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.005 | 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".