Gender representation in leadership & research: A 13-year review of the Annual Canadian Society of Otolaryngology Meetings
Bibliographic record
Abstract
BACKGROUND: The gender disparity in surgical disciplines, specifically in speakers across North American medical and surgical specialty conferences, has been highlighted in recent literature. Improving gender diversity at society meetings and panels may provide many benefits. Our aim was to determine the state of gender diversity amongst presenters and speakers at the annual Canadian Society of Otolaryngology-Head and Neck Surgery (CSO) meetings. METHODS: Scientific programs for the CSO annual meetings from 2008 to 2020 were obtained from the national society website. Participant name, role, gender, location, and subspecialty topic were recorded for all roles other than poster presenter. Gender (male or female) was determined using an online search. The total number of opportunity spots and proportion of women was then calculated. Gender differences were analyzed using chi-square test and logistic regression with odds ratios. Four categories were analyzed: Society Leadership, Invited Speaker Opportunities, Workshop Composition (male-only panels or "manels", female-only panels, or with at least one female speaker), and Oral Paper Presenters (first authors). RESULTS: There were 1874 leadership opportunity spots from 2008 to 2020, of which 18.6% were filled by women. Among elected leadership positions in the society, only 92 unique women filled 738 leadership opportunity spots. 13.2% of workshop chairs, 20.8% of panelists and 22.7% of paper session chairs were female. There was an overall increase in the proportion of leadership positions held by women, from 13.9% of leadership spots in 2008 to 30.1% in 2020. Of the 368 workshops, 61.1% were led by men only, 36.4% by at least 1 female surgeon, and 2.5% by women only. "Manels" have comprised at least 37.5% of workshops each year. CONCLUSIONS: The proportion of women in speaking roles at the annual CSO meetings has generally increased over time, particularly among panelists, leading to fewer male-only speaking panels. However, there has been a slower rate of growth in the proportion of unique women in speaker roles. There remains an opportunity to increase gender/sex diversity at the major Canadian otolaryngology meeting.
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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 | 0.022 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.018 | 0.031 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".