Identification and Assessment of Strategies to Address Gender Inequity in the Specialty of Critical Care Medicine: A Scoping Review, Modified Consensus Process, and Stakeholder Meeting
Bibliographic record
Abstract
We sought to identify and prioritize improvement strategies that Critical Care Medicine (CCM) programs could use to inform and advance gender equity among physicians in CCM. DESIGN: This study involved three sequential phases: 1) scoping review that identified strategies to improve gender equity in all medical specialties; 2) modified consensus process with 48 CCM stakeholders to rate and rank identified strategies; and 3) in-person stakeholder meeting to refine strategies and discuss facilitators and barriers to their implementation. SETTING: CCM. SUBJECTS: CCM stakeholders (physicians, researchers, and decision-makers; mutually inclusive). INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We identified 190 unique strategies from 416 articles. Strategies were grouped thematically into 20 categories across four overarching pillars of equity: access, participation, reimbursement, and culture. Participants prioritized 22 improvement strategies for implementation in CCM. The top-rated strategy from each pillar included: 1) nominate gender diverse candidates for faculty positions and prestigious opportunities (equitable access); 2) mandate training in unconscious bias and equitable treatment for committee (e.g., hiring, promotion) members (equitable participation); 3) ensure equitable starting salaries regardless of sex or gender (equitable reimbursement); and, 4) conduct 360° evaluations of leaders (including their direct work circle of supervisors, peers, and subordinates) through a diversity lens (equitable culture). Interprofessional collaboration, leadership, and local champions were identified as key enablers for implementation. CONCLUSIONS: We identified stakeholder-prioritized strategies that can be used to inform and enhance gender equity among physicians in CCM under four overarching equity pillars: access, participation, reimbursement, and culture. Implementation approaches should include education, policy creation, and measurement, and reporting.
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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 | 0.310 | 0.394 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.042 | 0.023 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".