Impact of Gender on Clinical Evaluation of Trainees in the Intensive Care Unit
Bibliographic record
Abstract
Abstract Background Gender disparities in medical education are increasingly demonstrated, including in trainee assessment. Objective This study aimed to evaluate whether gender differences exist in trainees’ evaluation during intensive care unit (ICU) rotations, which has not been previously studied. Methods We reviewed the in-training evaluation reports (ITERs) for trainees rotating through five academic ICUs at the University of Toronto over a 10-year period (2007–2017). We compared the mean global score for the rotation and the mean score for seven training subdomains between men and women trainees. All scores were reported on a scale of 1 (unsatisfactory) to 5 (outstanding). Results Over the 10-year period, there were 3,203 ITERS overall, representing 1,207 women and 1,996 men trainees. The mean overall score was lower for women than for men trainees: 4.26 (standard deviation [SD], 0.58) for women and 4.30 (SD, 0.60) for men (P = 0.04). This difference was driven by anesthesia trainees, in whom the mean overall score was 4.21 for women and 4.37 for men (P < 0.001), with men trainees scoring consistently higher across all seven training subdomains. Within surgical, internal medicine, and critical care residents, there were no differences between men and women in the overall score or the scores across any of the seven subdomains. Across all ITERS, women were less likely than men to receive an overall rating of 5 (outstanding) for the ICU rotation (33% women vs. 37% men; odds ratio, 0.83; 95% confidence interval, 0.71–0.96). Conclusion Overall, quantitative evaluation scores between women and men trainees in the ICU are relatively similar. Within anesthesia trainees, scores for men were consistently higher across all domains of evaluation, a finding that requires further investigation.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".