Gender Equity in Membership, Leadership, and Award Recognition in the Wilderness Medical Society
Bibliographic record
Abstract
INTRODUCTION: Despite near gender parity for women entering medical careers, women remain underrepresented in medical societies. This study evaluated the gender distribution associated with Wilderness Medical Society (WMS) activities. METHODS: A retrospective review was performed on the gender breakdown of the following WMS members: a single-day 2020 snapshot, conference attendees 2012 through 2020, conference presenters from winter 2017 through winter 2021, and leadership and awards data from 1984 through 2021. Genderize.io was used to generate probability-based gender categorizations (male/female) based on first names or pronoun associations. RESULTS: Gender was assigned in 91% (4043/4461) of 2020 WMS members, 92% (6179/6720) of 2012-2020 conference attendees, and 100% of remaining categories. Women represented 28% (1143/4043) of members, 27% (1679/6179) of conference attendees, 31% (143/465) of all conference presenters, 20% (62/303) of mainstage presenters, 23% (17/75) of all board members, 38% (14/37) of committee chairs, and 10% (2/20) of board presidents. Women received 18% (42/228) of recognition awards and 31% (15/48) of research grants issued. CONCLUSIONS: Although women comprise a minority of WMS participants, gender distribution was similar across categories for membership, conference presenters, total board positions, and research grant awards. Relative underrepresentation was seen in the highest leadership levels, in recognition awards, and in mainstage presenters. Ongoing auditing may help to identify and address sources of bias and/or barriers to participation. Although it is only one of many components of equity, identifying successes and future opportunities for gender balance can strengthen the base of the WMS, promote growth, and ensure a strong leadership pipeline.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".