A randomised trial of the influence of racial stereotype bias on examiners’ scores, feedback and recollections in undergraduate clinical exams
Bibliographic record
Abstract
BACKGROUND: Asian medical students and doctors receive lower scores on average than their white counterparts in examinations in the UK and internationally (a phenomenon known as "differential attainment"). This could be due to examiner bias or to social, psychological or cultural influences on learning or performance. We investigated whether students' scores or feedback show influence of ethnicity-related bias; whether examiners unconsciously bring to mind (activate) stereotypes when judging Asian students' performance; whether activation depends on the stereotypicality of students' performances; and whether stereotypes influence examiner memories of performances. METHODS: This is a randomised, double-blinded, controlled, Internet-based trial. We created near-identical videos of medical student performances on a simulated Objective Structured Clinical Exam using British Asian and white British actors. Examiners were randomly assigned to watch performances from white and Asian students that were either consistent or inconsistent with a previously described stereotype of Asian students' performance. We compared the two examiner groups in terms of the following: the scores and feedback they gave white and Asian students; how much the Asian stereotype was activated in their minds (response times to Asian-stereotypical vs neutral words in a lexical decision task); and whether the stereotype influenced memories of student performances (recognition rates for real vs invented stereotype-consistent vs stereotype-inconsistent phrases from one of the videos). RESULTS: Examiners responded to Asian-stereotypical words (716 ms, 95% confidence interval (CI) 702-731 ms) faster than neutral words (769 ms, 95% CI 753-786 ms, p < 0.001), suggesting Asian stereotypes were activated (or at least active) in examiners' minds. This occurred regardless of whether examiners observed stereotype-consistent or stereotype-inconsistent performances. Despite this stereotype activation, student ethnicity had no influence on examiners' scores; on the feedback examiners gave; or on examiners' memories for one performance. CONCLUSIONS: Examiner bias does not appear to explain the differential attainment of Asian students in UK medical schools. Efforts to ensure equality should focus on social, psychological and cultural factors that may disadvantage learning or performance in Asian and other minority ethnic students.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".