Advancement of female representation within ophthalmology in Canada: an assessment of representation at the Canadian Ophthalmology Society annual meeting
Bibliographic record
Abstract
OBJECTIVE: Females in ophthalmology represent a small proportion of senior positions. Participation in academic endeavours (e.g., involvement at conferences) plays a crucial role in promoting a physician's career. This study evaluates the representation of females from 2003 to 2021 at the Canadian Ophthalmology Society (COS) annual meeting. DESIGN: Retrospective cross-sectional study. METHODS: Data were extracted for the following and classified according to gender (female or male): oral presentations, free workshops, skills transfer courses, committee members, moderators, keynote speakers, and panelists. Percentages of gender were calculated and trended per category and in aggregate. RESULTS: The total percentage of females in any conference position demonstrated a positive trend. Over 18 years, there was an 18.2% increase in females (24.9%-43.1%). Excluding duplicates, only a 12.7% increase (27.4%-40.1%) was found. An increase in representation among all categories was observed, most significantly in female committee members (14.3%-50.0%). Female keynote speakers continue to be the most underrepresented category (8.33%-35.0%). CONCLUSIONS: While underrepresented, females continue to trend upward in participation at COS meetings. Continuous analysis of females participating in academic positions such as at COS meetings will aid in limiting gender disparities 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".