Passing MRCP (UK) PACES: a cross-sectional study examining the performance of doctors by sex and country
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».