DOI: 10.1177/01410768261455488 ISSN: 0141-0768

Ethnic differences in specialty destinations in UK medicine: a repeated cross-sectional analysis of secondary data

Celia Brown, Nariell Morrison, Karen Aus, Anna Coulson, Amir H Sam

Objectives:

To examine time-trends in ethnic differences in UK medical specialty destinations at two key career points: entry into specialty training (CT/ST1) and awarding Certificate of Completion of Training (CCT).

Design:

Repeated cross-sectional analysis using General Medical Council (GMC) secondary data; CT/ST1: 12 specialties, 2012-2022; CCT: 11 specialties, 2007–2021.

Setting:

Postgraduate specialty training groups within the UK National Health Service.

Participants:

Doctors with a UK Primary Medical Qualification (CT/ST1: 64,995; CCT: 62,380), categorised as White, Asian and other ethnic groups using GMC data.

Main outcome measures:

The ethnic distribution across specialties was examined using chi-squared tests. The temporal trend was evaluated with ordinary least squares regression of the effect size. Trends in over- and under-representation of ethnic groups within specialties were identified using Kendall’s tau-b correlations.

Results:

White doctors comprised the majority proportion (67.1% CT/ST1; 70.3% CCT), followed by Asian (23.4% CT/ST1; 22.6% CCT) and other ethnicities (9.5% CT/ST1; 7.1% CCT). Ethnic differences were statistically significant for all years except CCT in 2007, with larger effect sizes at CT/ST1. Effect sizes increased over time at both levels (CT/ST1 p  = 0.041; CCT p  = 0.024). Asian doctors were over-represented in Ophthalmology at both levels and Radiology at CT/ST1, and White doctors over-represented in Acute Care at CT/ST1 and Emergency Medicine at CCT.

Conclusions:

Persistent ethnic differences exist in UK medical specialty destinations. Systemic factors may therefore influence specialty choice, selection processes and progression to CCT. Future research should explore the underlying causes of ethnic differences between specialties to assess if biases affect career progression.

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