Effect of Professional Background and Gender on Residents’ Perceptions of Leadership
Bibliographic record
Abstract
PURPOSE: To examine the impact of professional background and gender of a resuscitation team leader on residents' perceptions of leadership skills. METHOD: The authors video-recorded a scripted, simulated resuscitation scenario twice, with either a male or a female team leader. They copied each video and labeled the leader as physician (MD) or nurse practitioner (NP), creating 4 conditions: female NP, female MD, male NP, or male MD. The authors recruited resident participants from 5 specialties at 4 institutions; they randomly assigned residents to view one version of the video and rate the team leader's performance using the Ottawa Crisis Resource Management Global Rating Scale (Ottawa CRM) in an online survey. The authors conducted 2-way ANOVA to examine interactions between team leader gender and profession on Ottawa CRM ratings. RESULTS: One hundred sixty residents responded (89 females, 71 males). A statistically significant main effect of team leader gender on residents' ratings was found in 2 of the 6 Ottawa CRM domains, leadership (F1,156 = 6.97, P = .009) and communication skills (F1,156 = 8.53, P = .004), due to lower ratings for female than male leaders (5.29 ± 0.95 vs 5.74 ± 1.17; 5.05 ± 1.20 vs 5.57 ± 1.06). There was no effect of profession on ratings and no significant interaction between profession and gender of the team leader on ratings for any of the domains. CONCLUSIONS: These findings indicate bias among residents against females as team leaders. Mitigating such bias is essential to successfully establish shared leadership models in health care.
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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 | 0.002 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".