Closing the Gender Gap Among Canadian Ophthalmology Societies
Bibliographic record
Abstract
Objective To evaluate gender distribution in Canadian ophthalmology societies’ leadership and to determine associations between gender, academic productivity, and institutional rank.Methods We identified members and assessed their gender composition using publicly available updated webpages. SCOPUS database was used to gather research metrics.Results In this study, data was collected from 12 Canadian ophthalmology societies, which included 277 executive committee members. Of these, 70.5% (196) were male and 29.1% (81) were female (p < .0001). Males were significantly more prevalent in presidential leadership roles (39 males vs. 23 females, p = .02), while females were more represented in other leadership categories (77 females vs. 61 males, p = .03). The Canadian Ophthalmological Society (COS) showed an upward trend in female representation from 19.2% in 2016 to 42.3% in 2021. Research productivity showed a positive correlation with society leadership rank, with a correlation coefficient of 0.732 for the m-index (p < .001) and 0.356 for the h-index (p < .05). Academic rank was also positively correlated with society leadership rank, with a correlation coefficient of 0.536 (p < .001). There was no significant difference in h-index (12.7 ± 1.0 for males vs. 13.8 ± 1.5 for females, p = .85) or number of publications (48.6 ± 5.1 for males vs. 60.0 ± 11.3 for females, p = .83) between male and female executive members, but females had a higher m-index (0.67 ± 0.05) compared to males (0.58 ± 0.03, p < .05). In academic rank, males were more likely to be associate professors (25% vs. 5% for females, p = .0001) or instructors (14.8% vs. 6.3% for females, p = .05), while a higher proportion of females held assistant professor positions (47.5% for females vs. 30.1% for males, p = .006).Conclusion In this study, we found that males were more prevalent in executive positions, particularly in presidential roles among Canadian ophthalmology societies. The gender distribution in leadership reflected the gender composition of practicing ophthalmologists in Canada. There was a positive correlation between research productivity and society rank, as well as academic position and society rank. Male and female executive members had similar h-index and number of publications, but females had a higher m-index. These findings highlight the need for continued efforts to address gender disparities in ophthalmology leadership.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".