Gender differences in emergency medicine resident assessment: A scoping review
Bibliographic record
Abstract
Background: Growing literature within postgraduate medical education demonstrates that female resident physicians experience gender bias throughout their training and future careers. This scoping review aims to describe the current body of literature on gender differences in emergency medicine (EM) resident assessment. Methods: We conducted a scoping review which adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines. We included research involving resident physicians or fellows in EM (population and context), which focused on the impact of gender on assessments (concept). We searched seven databases from the databases' inception to April 4, 2022. Two reviewers independently screened citations, completed full-text review, and abstracted data. A third reviewer resolved any discrepancies. Results: A total of 667 unique citations were identified; 10 studies were included, and all were conducted within the United States. Four studies reported differences in EM resident assessments attributable to gender within workplace-based assessments (qualitative comments and quantitative scores) by both attending physicians and nonphysicians. Six studies investigating clinical competency committee scores, procedural scores, and simulation-based assessments did not report any significant differences attributable to gender. Conclusions: This scoping review found that gender bias exists within EM resident assessment most notably at the level of narrative comments typically received via workplace-based assessments. As female EM residents receive higher rates of negative or critical comments and discordant feedback documented on assessment, these findings raise concern about added barriers female EM residents may face while progressing through residency and the impact on their clinical and professional development.
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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 | 0.032 | 0.177 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".