MRC ENTERPRISE STUDENTSHIP PROGRAMME 2023 - GPs labour trajectories and patients’ health outcomes in general practices
MRC ENTERPRISE STUDENTSHIP PROGRAMME 2023 (INDUSTRIAL CASE AWARDS)
Ten industrial CASE (iCASE) studentships are available for doctoral study at Oxford, to start in October 2023.
Designed to nurture the academic entrepreneurs of the future, the Enterprise studentship programme offers a stimulating educational experience as part of the Oxford-MRC DTP cohort, with the additional benefit of working closely with an industrial partner. This will provide entrepreneurial training opportunities and an insight into how commercial science is conducted alongside a superb academic base within the University. Students will work for at least 3 months in the associated company.
They are open to both UK and non-UK nationals and will follow the UKRI student eligibility requirements. UKRI will normally limit the proportion of international students appointed each year through individual training grants to 30% of the total intake each year.
Each iCASE studentship is fully-funded - it includes four years of stipend at the UKRI stipend level + £2,500 p.a., course fees, and a generous research training support grant.
Full details of scheme - including details about how to apply - can be found here: https://www.medsci.ox.ac.uk/study/graduateschool/mrcdtp/icase-2023
GPs labour trajectories and patients’ health outcomes in general practices
Commercial partner: EMIS, Leeds
Despite massive improvements in the production and accessibility of UK educational and training data (e.g., UKMED, UCAS, HESA, NPD), we still know very little about the educational and professional trajectories of medical students or of those who opt as postgraduates for primary care training.
Previous studies have shown that students from more affluent socioeconomic backgrounds and independent schools are more likely to attend medical schools (at both undergraduate and graduate levels). A deeper understanding on what are the consequences of different educational trajectories on transitions to general practices and, more generally, to the health care sector is much needed. A recent report from the General Medical Council indicates that the lack of diversity and growing geographical and socioeconomic segregation in the doctors' workforce might have negative consequences on the health of patients, especially for those living in economically deprived areas of the UK. Importantly, most of the existing evidence on access to medical schools, learning outcomes and trajectories of medical students, as well as its consequences for public health, is purely correlational and thus not ideal for informing policy. Furthermore, only 7% of male and 13% of female graduates say they want a career in general practice. An assessment of undergraduates’ career intentions regarding general practice and the transition from school to the labour market is important to explore further health policies that could help retain GPs and push more students to become GPs.
The aim of this studentship is to understand in a more comprehensive way than past studies how sociodemographic characteristics and the educational trajectories of medical students determine employment transitions in the healthcare sector and patients’ health outcomes.
For this project, the DPhil student will use advanced econometric techniques such as Difference-in-Differences models, network- and peer- effects analysis, and multilevel regression analysis. The student will create a novel dataset obtained from linking UKMED (UK Medical Education Database), the national GP training dataset (a complete record of postgraduate GP training), a primary care appraisal dataset (a database of appraisal data), and ORCHID (Oxford Royal College of General Practitioners Clinical Informatics Digital Hub) datasets. ORCHID is a primary care database of pseudonymised primary care data, which will also provide health outcomes.
First, the student will create a unique database, linking UKMED (educational trajectories of medical students), the national GP training dataset and the ORCHID dataset (transition to GPs and patients outcomes), evaluate the quality of the linkage through different linkage techniques (i.e., deterministic vs probabilistic data linkages), and harmonise variables for the analysis. Second, the student will conduct econometric analysis on a) determinants of educational trajectories and learning outcomes of medical students, accounting for socio-demographic and medical school characteristics; b) transitions to GP practices, retention, and labour careers of doctors; and c) patients outcomes and their intersection with inequalities.
Apply using course: DPhil in Primary Health Care