Gendered Language in Letters of Recommendation for Applicants to Pulmonary Critical Care Fellowships
Bibliographic record
Abstract
Abstract Background Previous work has demonstrated letters of recommendation for women in academic medicine are shorter and emphasize communal traits over grindstone or agentic traits. Objective To determine if there are sex-based differences in letters of recommendation written for applicants applying to pulmonary critical care medicine fellowships and if the sex of the letter writer impacts these differences. Methods All fellowship applications submitted to a pulmonary critical care medicine fellowship program in 2020 were included in this study. The applicant demographics and self-reported accomplishments were extracted from their application. The sex of letter writers was identified through public online searches. Word count and language differences in the letters of recommendation were analyzed for each applicant using the Linguistic Inquiry and Word Count (LIWC2015) program. Multivariable linear regressions were performed controlling for applicant characteristics to identify if applicant sex was associated with total word counts and total agentic word counts. Results Of the 529 complete applications, 2,024 letters of recommendation were reviewed. A majority of the applicants (70%, n = 370/530) and letter writers (75%, n = 1,515/2,024) were male. When adjusting for applicant demographic and accomplishments, female applicants had longer letters of recommendation (30.91 words longer, 95% confidence interval [CI], 1.53–60.29; P = 0.04) and more supportive letters (3.27 words longer, 95% CI, 1.59–4.95; P < 0.01) as compared with male applicants. Female letter writers wrote longer and more supportive letters than male letter writers, and this difference was greatest for female applicants. Conclusion Female applicants received longer and more supportive letters of recommendation. Further work is needed to understand if this finding is the beginning of a change in the letters of recommendation for female applicants.
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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 | 0.003 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".