Gender of Award Recipients in Major Ophthalmology Societies
Bibliographic record
Abstract
PURPOSE: To assess the gender distribution of major ophthalmology society award recipients DESIGN: Retrospective, observational study METHODS: The study population included award recipients from 9 ophthalmologic societies: American Academy of Ophthalmology, American Association for Pediatric Ophthalmology and Strabismus, American Glaucoma Society, American Society of Cataract and Refractive Surgery (ASCRS), American Society of Ophthalmic Plastic and Reconstructive Surgery, American Society of Retina Specialists, American Uveitis Society, Cornea Society, and North American Neuro-Ophthalmology Society. A gender-specific pronoun and a photograph of each award recipient were extracted from professional websites to assign their gender. Main outcome measures were gender distribution by award society, year (1970-2020), type (lectureship or not), category (achievement, education, research contribution, research item, international member achievement, public service-global health, service to society), and training level. RESULTS: Out of 2,150 recipients for 78 awards, 1,606 (74.7%) were men and 544 (25.3%) were women. The proportion of women recipients increased from 0% in 1970 to 33.2% in 2020 (P < .001). Women representation varied within each society (P < .01), with ASCRS having the highest percentage (40.8%). Women received 11.0% of awards accompanied by a lecture. Women received a significantly greater proportion of research-related awards than achievement or service awards. Awards for trainees and early-career ophthalmologists had a greater proportion of women (39.8%) than the rest of the awards (21.5%) (P < .001). CONCLUSIONS: Overall, women received awards (25.3%) at a higher rate than the average 1970-2020 American gender distributions of ophthalmologists. However, women are still under-represented in many award categories and subspecialties.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".