Calling on Sponsorship: Analysis of Speaker Gender Representation at Hand Society Meetings
Bibliographic record
Abstract
BACKGROUND: The paucity of leadership diversity in surgical specialties is well documented. Unequal opportunities for participation at scientific meetings may impact future promotions within academic infrastructures. This study evaluated gender representation of surgeon speakers at hand surgery meetings. METHODS: Data were retrieved from the 2010 and 2020 meetings of the American Association for Hand Surgery (AAHS) and American Society for Surgery of the Hand (ASSH). Programs were evaluated for invited and peer-reviewed speakers excluding keynote speakers and poster presentations. Gender was determined from publicly available sources. Bibliometric data (Hirsch index) for invited speakers were analyzed. RESULTS: In 2010 at the AAHS ( n = 142) and ASSH meetings ( n = 180), female surgeons represented 4% of the invited speakers and in 2020 increased to 15% at AAHS ( n = 193) and 19% at ASSH ( n = 439). From 2010 to 2020, female surgeon invited speakers had a 3.75-fold increase at AAHS and 4.75-fold increase at ASSH. Representation of female surgeon peer-reviewed presenters at these meetings was similar (2010 AAHS, 26%; and 2010 ASSH, 22%; 2020 AAHS, 23%; 2020 ASSH, 22%). The academic rank of women speakers was significantly lower ( P < 0.001) than for male speakers. At the assistant professor level, the mean Hirsch index was significantly lower ( P < 0.05) for female invited speakers. CONCLUSIONS: Although there was a significant improvement in gender diversity in invited speakers at the 2020 meetings compared with 2010, female surgeons remain underrepresented. Gender diversity is lacking at national hand surgery meetings, and continued effort and sponsorship of speaker diversity is imperative to curate an inclusive hand society experience.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".