Title: Selecting the best medical workforce: The UK Medical Applicant Cohort Study
Funder: Department of Health and Social Care

Abstract: The NHS is critically short of doctors, especially in general practice (GP) and certain geographical areas, resulting in unequal access to care. The sustainability of the UK medical workforce depends on medical schools selecting and educating more GPs and doctors able and willing to care for under-served patient populations.The Government recently announced an extra 1,500 English medical school places, likely to go to schools that produce more GPs, and that select and train doctors from non-traditional backgrounds' (i.e. from social groups currently underrepresented in medicine), who - it is assumed - will be better at caring for under-served populations. A major problem with this solution is the poor evidence base for how medical schools should achieve it.Medical schools vary in how they attract, select, and educate future doctors. Some produce more GPs than others, and some attract and accept more applicants from non-traditional backgrounds than others. It is unclear why. There is a tension between the desire to provide doctors more representative of the patient population and maintaining academic standards. Doctors from minority ethnic backgrounds and lower socioeconomic groups can struggle academically but it is uncertain how much this relates to their characteristics at application how much to their experiences during medical training. It is also uncertain how much academic performance relates to patient care.Most medical selection research examines selection tests; however an understanding how applicant choices affect selection outcomes is also needed, since, as one medical school admissions dean put it, we can only select from those who apply. Long term follow-up of medical students is necessary to understand how applicant factors predict doctor outcomes.The proposed research programme consists of two interlinked workstreams, which will benefit patients by 1) improving how medical schools attract and select future doctors from diverse backgrounds, and 2) providing the foundation for research on how medical selection shapes the medical workforce.1. The first workstream asks how do medical applicant characteristics influence choice of medical school and application success?'. This question is broken into three sub-questions. The first two which factors influence applicant choice of medical school?' and do factors influencing choice vary by applicant background?' will be answered using qualitative data from interviews with potential applicants from underrepresented groups, and current first year medical students in 2017 and 2018. The results will be used to develop a questionnaire together with stakeholders, which will be piloted and then administered to all those registering to take the UK Clinical Aptitude Test in 2018 for entry into medicine in 2019 a sample including the majority of all UK medical applicants. The third sub-question Does applicant choice of medical school mediate the relationship between applicant background and likelihood of gaining a place to study medicine?' will be answered by linking questionnaire data with data about which, if any, medical schools participants applied to, received offers from, and accepted offers from. These data will be used to develop, with stakeholders, a tool for medical schools to improve how they help potential applicants - including those from non-traditional groups - make informed choices. Information provision is one of the few low-cost widening participation activities supported by evidence. 2. The second workstream will enable the successful medical applicants in 2018 to be followed up through their careers as doctors, by linking the results into the UK Medical Educational Database (UKMED). UKMED collates routinely-collected administrative data on all UK-trained doctors. It has no data on unsuccessful applicants, making it impossible to understand how selection influences medical workforce outcomes. This project will solve that problem, and provide a uniquely valuable resource.

Lay abstract: The NHS has a critical shortage of doctors, especially in general practice (GP) and in certain geographical areas, resulting in poor and unequal access to care. The sustainability of the UK medical workforce depends on medical schools selecting and educating more medical students who will become GPs, and who are able and willing to care for under-served patient populations when they become doctors.The Government recently announced an extra 1,500 English medical school places. These are likely to go to medical schools that will produce more GPs, and that will select and train doctors from non-traditional backgrounds' (i.e. from social groups currently underrepresented in medicine), who - it is assumed - will be better at caring for under-served patient populations. A major problem with this solution is the poor evidence base for how medical schools should achieve it.We know medical schools vary in how they attract, select, and educate future doctors. We also know that some medical schools produce more GPs than others, and some attract and accept more applicants from non-traditional backgrounds than others. We know much less about why this is the case. There is a tension among many medical schools between the desire to provide doctors who are more representative of the patient population and the desire to maintain academic standards. Doctors from minority ethnic backgrounds and lower socioeconomic groups can struggle academically but it is uncertain how much this relates to their characteristics at application how much to their experiences during medical training. It is also uncertain how much doctors' academic performance relates to the care they give to patients.Most research on medical selection examines the technical aspects of selection tests; however to be able to predict the outcomes of selection we also need to understand applicant choices. As one medical school admissions dean put it, we can only select from those who apply. Long term follow-up of medical students is also necessary to understand whether what we know about applicants at selection can predict what they are like once they become doctors, this being the ultimate test of any selection process.This fellowship is intended to support the development of an academic based in one of the UK's top centres of medical education, to develop and lead a programme of research that aims to benefit patients by improving how medical schools attract and select future doctors from diverse backgrounds, and by providing the foundation for research on how medical selection shapes the medical workforce.The programme consists of two interlinked workstreams:The first asks how do medical applicant characteristics influence choice of medical school and application success?'. We will interview potential and current medical applicants from diverse backgrounds about how they choose a medical school. Together with stakeholders, the findings will be transformed into a questionnaire, which we will give to the vast majority of 2018 medical applicants, linking the results to data on applications, offers, and acceptances. Results will be used to develop a tool for medical schools to improve the information they give potential applicants from diverse social backgrounds; information provision being one of the few low-cost interventions proven to increase success in applicants from non-traditional backgrounds.The second will enable the successful applicants who enter medical school in 2019 to be followed up throughout their careers as doctors, by linking the results into the UK Medical Educational Database (UKMED). UKMED collates routinely-collected administrative data on all UK-trained doctors. It has no data on unsuccessful applicants, making it impossible to understand how selection influences medical workforce outcomes. This project will solve that problem, and provide a uniquely valuable resource.

Research activity codes: 8.5 RESOURCES AND INFRASTRUCTURE (HEALTH SERVICES)
Health categories: GENERIC HEALTH RELEVANCE