Beyond the Scores: Gendered Interpretations of Emergency Medicine Resident Assessments of Interdependent Performances
Bibliographic record
Abstract
Purpose: In medicine, gender bias and gendered language within assessments of individual performance are well established. Recent shifts toward assessing interdependence (the ability to work supportively and collaboratively within teams) demand we understand how gender bias and gendered language influence assessments. In exploring how faculty assess residents' interdependent performances, this study evaluated how gender-presentation influences faculty raters' assessments of residents' interdependence in Emergency Medicine (EM). Methods: Using a multiple-methods (an experimental within-subjects study with follow-up interviews), 18 EM faculty from Canada and the United States assessed scripted videos of identical clinical encounters acted by male- and female-presenting residents. Faculty assessed female residents via anonymous online surveys and, six months later, assessed male residents via follow-up interviews using the same clinical scenarios. After every clip, faculty completed entrustable professional activity (EPA) and Milestone ratings and provided narrative justifications. Statistical analyses were conducted using Wilcoxon signed-rank tests to assess gender differences in EPA and Milestone scores. Qualitative data were analyzed using thematic analysis to identify recurring, gendered patterns in narrative justifications. Results: Quantitative results revealed no gender differences in Milestone and EPA scores, except for the resuscitation entrustment rating, where male residents were rated less favorably (z = -3.09, p = 0.002). Qualitative findings uncovered subtle gender differences. For the same clinical performances, male residents were frequently described as leaders, while female residents as collaborative. Furthermore, male residents' help-seeking was framed as proactive, whereas female residents' help-seeking was indicative of lacking knowledge. Finally, bias was not consistent across genders: male leadership expectations could negatively flavor assessments of male collaborative performances. Conclusion: EPA and Milestone scores showed marginal gender-based differences, while narrative justifications reflected clear gendered expectations about residents' interdependence. These findings highlight the need for equity-oriented assessment practices that interrogate both the numbers and the narratives. As team-based competencies like interdependence become central to clinical training, ensuring that assessments reflect fair, unbiased interpretations are essential to supporting all learners equitably.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".