Bias against foreign‐born or foreign‐trained doctors: experimental evidence
Bibliographic record
Abstract
OBJECTIVES: Bias against foreign-born or -trained medical students and doctors is not well understood, despite its documented impact on recruitment, integration and retention. This research experimentally examines the interaction of location of medical education and nationality in evaluations of doctors' competence and trustworthiness. METHODS: A convenience sample of prospective patients evaluated fictitious candidates for a position as a doctor in community practice at a new local health clinic. All applicants were described as having the same personality profile, legal qualifications to practise, a multi-degree education and relevant work experience. The location of medical education (the candidate's home country or the UK) and national background (Australia or Pakistan) of the applicants were independently experimentally manipulated. RESULTS: Consistent with previous research on skills discounting and bias, foreign-born candidates were evaluated less favourably than native-born candidates, despite their comparable education level, work experience and personality. However, overseas medical education obtained in the First World both boosted evaluations (of competence and trustworthiness) and attenuated bias based on nationality. CONCLUSIONS: The present findings demonstrate the selective discounting of foreign-born doctors' credentials. The data show an interaction of location of medical education and birth nationality in bias against foreign doctors. On an applied level, the data document that the benefits of medical education obtained in the First World can extend beyond its direct outcomes (high-quality training and institutional recognition) to the indirect benefit of the attenuation of patient bias based on nationality.
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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 | 0.001 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.064 | 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".