Trends in female applicants to Canadian ophthalmology residency programs from 1998-2020
Bibliographic record
Abstract
Background: Ophthalmology has historically been a male-dominated specialty. Despite there being a higher proportion of females in Canadian medical schools since the early 2000s, it is unknown if trends in female applicants and those accepted to ophthalmology have followed suit. This study aims to evaluate trends in gender representation of ophthalmology applicants to Canadian residency programs from 1998 to 2020 and to compare those trends to other surgical specialties. Methods: We obtained aggregate data of the annual number of male and female applicants ranking and successfully matching to ophthalmology as their first-choice specialty from the Canadian Residency Matching Service (CaRMS) database. We then carried out a retrospective cross-sectional analysis on the publicly available data. Subsequently, we compared trends in female applicants to ophthalmology, as well as female practicing ophthalmologists, to other surgical disciplines. Results: The proportion of female applicants increased from 24.3% in 1998 to 33.3% in 2020 (p = 0.001), and matched female applicants increased from 28.6% in 1998 to 40.5% in 2020 (p = 0.023). However, the incremental change in proportion did not statistically significantly increase in 2008-2012, 2013-2016, and 2017-2020. Comparison of male and female matching success rates did not reveal a significant difference (p = 0.45). Trends in female applicants to ophthalmology and female practicing ophthalmologists were similar to other surgical specialties. Conclusions: Although the proportion of female applicants is increasing, there is a recent plateau and an inability to equalize the female-to-male ratio in ophthalmology. Further studies are needed to identify potential barriers and mitigate possible residual gender biases.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".