Toward Gender Equity in Critical Care Medicine: A Qualitative Study of Perceived Drivers, Implications, and Strategies*
Bibliographic record
Abstract
OBJECTIVES: Critical care medicine is a medical specialty where women remain underrepresented relative to men. The purpose of this study was to explore perceived drivers (i.e., influencing factors) and implications (i.e., associated consequences) of gender inequity in critical care medicine and determine strategies to attract and retain women. DESIGN: Qualitative interview-based study. SETTING: We recruited participants from the 13 Canadian Universities with adult critical care medicine training programs. PARTICIPANTS: We invited all faculty members (clinical and academic) and trainees to participate in a semistructured telephone interview and purposely aimed to recruit two faculty members (one woman and one man) and one trainee from each site. Interviews were transcribed verbatim, and two investigators conducted thematic analysis. INTERVENTIONS: Not applicable. MEASUREMENTS AND MAIN RESULTS: Three-hundred seventy-one faculty members (20% women, 80% men) and 105 trainees (28% women, 72% men) were invited to participate, 48 participants were required to achieve saturation. Participants unanimously described critical care medicine as a specialty practiced predominantly by men. Most women described experiences of being personally or professionally impacted by gender inequity in their group. Postulated drivers of the gender gap included institutional and interpersonal factors. Mentorship programs that span institutions, targeted policies to support family planning, and opportunities for modified role descriptions were common strategies suggested to attract and retain women. CONCLUSIONS: Participants identified a gender gap in critical care medicine and provided important insight into the impact for personal, professional, and group dynamics. Recommended improvement strategies are feasible, map broadly onto reported drivers and implications, and are applicable to critical care medicine and more broadly throughout medical specialties.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".