A Gendered Analysis of Canadian Academic Ophthalmology Leaders
Bibliographic record
Abstract
Objective To describe and analyze the gender distribution of Canadian academic ophthalmology leadership. Methods This study assessed the characteristics of ophthalmology department chairs, program directors, undergraduate directors, fellowship directors, and research directors in Canada. Gender, subspecialties, graduate degrees, and academic rank were collected from institutional websites. Research productivity measures (number of published documents, h-index, and years active) were extracted from Elsevier SCOPUS. All statistical analyses were performed using SPSS. Results In the 15 Canadian ophthalmology programs, 132 leadership positions were held by 122 physicians. 33 (27.0%) of those physicians were women, and 89 (73.0%) were men, with a significant proportion difference (p Conclusion Compared to the 28% of active women ophthalmologists (Canadian Medical Association, 2019), our study demonstrates a similar proportion of women leaders with 27% overall. Positive outlooks are noted when regarding the proportions of women chairpersons (28.6%) and program directors (35.3%). Women leaders were underrepresented in academic ranks and most ophthalmic subspecialties, while there were no significant differences in their research scores (h-index and m-quotient). Future directions include understanding factors contributing to advancement in leadership and strategies to improve the gender gap 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".