Systematic Review of Gender Differences in Reference Letters for Postgraduate Surgical Training Programs
Bibliographic record
Abstract
OBJECTIVE: This study is the first systematic review to synthesize the literature examining gender bias within reference letters for postgraduate training programs of all surgical disciplines. DESIGN: MEDLINE, Embase, and CINAHL databases were searched to June 2023 and original studies on gender differences in reference letters were included in a systematic review. SETTING: Surgical postgraduate (residency and fellowship) programs PARTICIPANTS: n/a RESULTS: After screening 477 studies, 39 met inclusion criteria and were included in the systematic review. Key gender differences included the greater use of communal terminology for female applicants and increased discussion of male applicants' leadership skills. There were no consistent gender differences in the use of agentic language or discussions of applicants' research and teaching skills. CONCLUSIONS: There are gender differences in reference letters written for male and female applicants to postgraduate surgical training programs. Although strides have been made towards improving gender representation in surgery, bias in reference letters may negatively affect female applicants' success in obtaining postgraduate training positions, which serve as the initial barrier to entering the field of surgery. Awareness of these biases and development of strategies by selections committees and letter writers to mitigate these biases are recommended.
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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 | 0.026 | 0.177 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".