Is There Evidence of Gender Bias in the Oral Examination for Initial Certification by the American Board of Physical Medicine & Rehabilitation?
Bibliographic record
Abstract
OBJECTIVE: Unconscious bias may result in a prejudicial evaluation of another person and lead to unfair treatment. Potential gender bias risk exists in the scoring process on the American Board of Physical Medicine and Rehabilitation oral examination (Part II) because of the face-to-face interactions between candidates and examiners. This study was undertaken to determine whether performance on the American Board of Physical Medicine and Rehabilitation Part II examination differed based on candidate gender or configuration of examiner/candidate gender pairings. The impact of examiner unconscious bias training on candidate performance was also assessed. DESIGN: This is a retrospective observational study of first-time Part II physical medicine and rehabilitation certification examination test takers between 2013 and 2018. RESULTS: There were significant differences in pass rates (men 84%, women 89%) and mean scaled scores (men 6.56, women 6.81) between men and women (P < 0.001) with the biggest domain score differences in data acquisition and interpersonal and communication skills. Implementation of examiner unconscious bias training did not impact candidate performance. CONCLUSIONS: Women candidates scored higher and had a higher pass rate than men candidates overall on the American Board of Physical Medicine and Rehabilitation Part II examination. This difference does not seem to be due to scoring gender bias by the Part II examiners or due to candidate aptitude as measured on the Part I examination.
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".