Gender bias in the evaluation of interns in different medical specialties: An archival study
Bibliographic record
Abstract
Introduction The field of medicine is characterized by within-field gender segregation: Gender ratios vary systematically by subdisciplines. This segregation might be, in part, due to gender bias in the assessment of women and men medical doctors. Methods We examined whether the assessments, i.e. overall score, department scores and skills scores, interns receive by their superiors during their internship year, vary as a function of their gender and the representation of women in the field. We analyzed an archival data set from a large hospital in Israel which included 3326 assessments that were given to all interns who completed their internship year between 2015 and 2019. Results Women received lower department scores and skills scores in fields with a low (versus high) representation of women. Men received higher scores in fields with a high (versus low) representation of men, yet there was no difference in their skills scores. Conclusions Women are evaluated more negatively in fields with a low representation of women doctors. Similarly, men are evaluated more negatively in fields with a low representation of men, yet this cannot be explained by their skills. This pattern of results might point to a gender bias in assessments. A better understanding of these differences is important as assessments affect interns’ career choices and options.
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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 | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".