Exploring the gender gap in Canadian ophthalmology applicants: a mixed methods study
Bibliographic record
Abstract
OBJECTIVE: To examine and compare medical students' perceptions of ophthalmology as a career, with a focus on women and under-represented students. STUDY DESIGN: Phase 1 of a multi-institutional, explanatory sequential mixed-methods study. METHODS: Medical students from 4 geographically representative Canadian institutions with varying levels of ophthalmology exposure completed a 17-item survey assessing perceptions, barriers, and facilitators to pursuing ophthalmology. Quantitative data were analyzed using nonparametric tests and ordinal logistic regression to assess associations between both demographics and context-specific factors and survey responses. Open-ended qualitative responses were analyzed thematically using Braun and Clarke's framework. RESULTS: A total of 314 students participated, including 213 (67.8%) women, 125 (39.8%) visible minorities, and 73 (23.3%) Canadian immigrants. Negative perceptions of pursuing ophthalmology increased during medical school, with 29.9% of students expressing a negative view at the start of medical school compared to 45.5% at the time of the survey-a 52.1% relative increase (p = 0.007). Only 30.9% viewed the field as racially diverse, and 26.8% as gender balanced. Analysis of the qualitative responses showed that barriers to pursuing ophthalmology included intense competition, limited early exposure, lack of mentorship, perceived exclusivity based on personal connections, high research expectations, and difficulties with parallel planning. CONCLUSIONS: Increased early exposure, improved mentorship opportunities, and promoting diversity may support greater gender and racial representation in ophthalmology.
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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 | 0.020 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".