Passing MRCP (UK) PACES: a cross-sectional study examining the performance of doctors by sex and country
Bibliographic record
Abstract
BACKGROUND: There is much discussion about the sex differences that exist in medical education. Research from the United Kingdom (UK) and United States has found female doctors earn less, and are less likely to be senior authors on academic papers, but female doctors are also less likely to be sanctioned, and have been found to perform better academically and clinically. It is also known that international medical graduates tend to perform more poorly academically compared to home-trained graduates in the UK, US, and Canada. It is uncertain whether the magnitude and direction of sex differences in doctors' performance is variable by country. We explored the association between doctors' sex and their performance at a large international high-stakes clinical examination: the Membership of the Royal Colleges of Physicians (UK) Practical Assessment of Clinical Examination Skills (PACES). We examined how sex differences varied by the country in which the doctor received their primary medical qualification, the country in which they took the PACES examination, and by the country in which they are registered to practise. METHODS: Seven thousand six hundred seventy-one doctors attempted PACES between October 2010 and May 2013. We analysed sex differences in first time pass rates, controlling for ethnicity, in three groups: (i) UK medical graduates (N = 3574); (ii) non-UK medical graduates registered with the UK medical regulator, the General Medical Council (GMC), and thus likely to be working in the UK (N = 1067); and (iii) non-UK medical graduates without GMC registration and so legally unable to work or train in the UK (N = 2179). RESULTS: Female doctors were statistically significantly more likely to pass at their first attempt in all three groups, with the greatest sex effect seen in non-UK medical graduates without GMC registration (OR = 1.99; 95% CI = 1.65-2.39; P < 0.0001) and the smallest in the UK graduates (OR = 1.18; 95% CI = 1.03-1.35; P = 0.02). CONCLUSIONS: As found in a previous format of this examination and in other clinical examinations, female doctors outperformed male doctors. Further work is required to explore why sex differences were greater in non-UK graduates, especially those without GMC registration, and to consider how examination performance may relate to performance in practice.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".