Effect of COVID-19 on Canadian Medical Student Attitudes toward Ophthalmology Residency Application
Bibliographic record
Abstract
Abstract Objective This study aims to survey medical students interested in ophthalmology about how changes in electives and Canadian Residency Matching Service (CaRMS) due to the novel coronavirus disease 2019 (COVID-19) pandemic have affected their attitudes toward applying to ophthalmology residency. Design This is a cross-sectional survey. Participants A total of 32 Canadian medical students interested in ophthalmology responded to the survey. Methods A 32-question survey tool used was developed in consultation with medical students, academic ophthalmologists, and residency program directors. The survey was distributed through e-mail by local ophthalmology interest groups at all of Canada's medical schools. Results Respondents felt that changes in ability to travel for electives significantly decreased their likelihood of applying to ophthalmology residency. Additionally, respondents expressed concerns that lack of travel for electives and in-person CaRMS interviews significantly reduced their chances of successfully matching to ophthalmology. Respondents identified one-on-one video calls with program directors and residents as the initiatives that would best counteract the negative impacts from COVID-19. Increased presence of programs on social media were relatively less valued. Conclusion Canadian medical students interested in ophthalmology have concerns about how changes in electives and the CaRMS match due to COVID-19 will impact their ability to be fairly assessed and successfully match to ophthalmology. Lack of travel for electives and interviews has also resulted in students feeling ill equipped to make informed choices about program selection. However, there remains a sustained interest in ophthalmology among applicants.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".