An Update on Gender Disparity in Critical Care Conferences
Bibliographic record
Abstract
This commentary’s objective was to identify whether female representation at critical care conferences has improved since our previous publication in 2018. We audited the scientific programs from three international (International Symposium on Intensive Care and Emergency Medicine [ISICEM], European Society of Intensive Care Medicine [ESICM], and Society of Critical Care Medicine [SCCM]) and two national (State of the Art [SOA] and Critical Care Canada Forum) critical care conferences from the years 2017 to 2022. We collected data on the number of female faculty members and categorized them into physicians, nurses, allied health professions (AHPs), and other. Across all conferences, there was an increased representation of females as speakers and moderators over the 6 years. However, at each conference, male speakers outnumbered female speakers. Only two conferences achieved gender parity in speakers, SCCM in 2021 (48% female) and 2022 and SOA in 2022 (48% female). These conferences also had the highest representation of female nursing and AHP speakers (25% in SCCM, 2021; 19% in SOA, 2022). While there was a statistically significant increase in female speakers ( p < 0.01) in 2022 compared with 2016, there was a persistent gender gap in the representation of men and female physicians. While the proportion of female moderators increased in each conference every year, the increase was statistically only significant for ISICEM, ESICM, and SCCM ( p < 0.05). The proportion of female nurses and AHP speakers increased in 2022 compared with 2016 ( p < 0.0001) but their overall representation was low with the highest proportion (25%) in the 2022 SCCM conference and the lowest (0.5%) in the 2017 ISICEM conference. This follow-up study demonstrates a narrowing but persisting gender gap in the studied critical care conferences. Thus, a commitment toward minimizing gender inequalities is warranted.
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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 | 0.023 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".