Gender bias in reference letters for residency and academic medicine: a systematic review
Bibliographic record
Abstract
Reference letters play an important role for both postgraduate residency applications and medical faculty hiring processes. This study seeks to characterise the ways in which gender bias may manifest in the language of reference letters in academic medicine. In particular, we conducted a systematic review in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We searched Embase, MEDLINE and PsycINFO from database inception to July 2020 for original studies that assessed gendered language in medical reference letters for residency applications and medical faculty hiring. A total of 16 studies, involving 12 738 letters of recommendation written for 7074 applicants, were included. A total of 32% of applicants were women. There were significant differences in how women were described in reference letters. A total of 64% (7/11) studies found a significant difference in gendered adjectives between men and women. Among the 7 studies, a total of 86% (6/7) noted that women applicants were more likely to be described using communal adjectives, such as "delightful" or "compassionate", while men applicants were more likely to be described using agentic adjectives, such as "leader" or "exceptional". Several studies noted that reference letters for women applicants had more frequent use of doubt raisers and mentions of applicant personal life and/or physical appearance. Only one study assessed the outcome of gendered language on application success, noting a higher residency match rate for men applicants. Reference letters within medicine and medical education exhibit language discrepancies between men and women applicants, which may contribute to gender bias against women in medicine.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".