The Speaker Gender Gap at Critical Care Conferences
Bibliographic record
Abstract
OBJECTIVES: To review women's participation as faculty at five critical care conferences over 7 years. DESIGN: Retrospective analysis of five scientific programs to identify the proportion of females and each speaker's profession based on conference conveners, program documents, or internet research. SETTING: Three international (European Society of Intensive Care Medicine, International Symposium on Intensive Care and Emergency Medicine, Society of Critical Care Medicine) and two national (Critical Care Canada Forum, U.K. Intensive Care Society State of the Art Meeting) annual critical care conferences held between 2010 and 2016. SUBJECTS: Female faculty speakers. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Male speakers outnumbered female speakers at all five conferences, in all 7 years. Overall, women represented 5-31% of speakers, and female physicians represented 5-26% of speakers. Nursing and allied health professional faculty represented 0-25% of speakers; in general, more than 50% of allied health professionals were women. Over the 7 years, Society of Critical Care Medicine had the highest representation of female (27% overall) and nursing/allied health professional (16-25%) speakers; notably, male physicians substantially outnumbered female physicians in all years (62-70% vs 10-19%, respectively). Women's representation on conference program committees ranged from 0% to 40%, with Society of Critical Care Medicine having the highest representation of women (26-40%). The female proportions of speakers, physician speakers, and program committee members increased significantly over time at the Society of Critical Care Medicine and U.K. Intensive Care Society State of the Art Meeting conferences (p < 0.05), but there was no temporal change at the other three conferences. CONCLUSIONS: There is a speaker gender gap at critical care conferences, with male faculty outnumbering female faculty. This gap is more marked among physician speakers than those speakers representing nursing and allied health professionals. Several organizational strategies can address this gender gap.
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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 | 0.007 | 0.034 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".