Can we really predict who will make a good doctor?
Supervisors: Dr Sandra Gibson, Dr Patrick Musonda and Professor Sam Leinster
Introduction
Access to medicine is highly competitive; in 2006 approximately 10% of applicants were successful, off those that gain a place up to 14% may drop out before graduating to progress to the Foundation Year of training (FY1). It has been reported that up to a further 20% may leave the National Health Service (NHS) within two years of graduating. Earlier identifying of students at risk of leaving training, for whatever reason, will ensure that more timely feedback, educational support or change of career advice can be offered. Helping to reduce the potential emotional and financial stress for students leaving courses later. Additionally, it will reduce the training cost burden to University and NHS when students leave, and improve the standards of students who start FY1 training.
Leading medical educationalists recently asked if new ways of approaching student assessment could be found, challenge traditional analysis theories combining qualitative and quantitative sources to generate meaningful models of performance. A number of measures have been independently shown to predict success on MB/BS courses, including academic ability, socioeconomic data, motivation and suitability, but as yet there has been no comprehensive attempt to model these data to generate a predictive tool to aid in medical student selection, support and training.
In this study we will build on current research looking at the predictive validity of admissions criteria using expertise transferred from the field of risk modelling of disease to include other areas of student generated data.
Aims
In this study we aim to:
• develop a predictive risk model to identify students at risk of non-completion of the course
• evaluate the validity of educational interventions and support mechanisms offered to at risk students
References
Those applicants whose first language is not English must demonstrate evidence of appropriate English language proficiency, normally defined as a minimum IELTS score of 7.5 (Overall Band Score) with 7.5 in all elements or equivalent.
1. Schuwirth, L. T and Van der Vleuten, C. P. M (2006). A plea for new psychometric models in educational assessment Med Ed 40: 296-300
2. Miles, S. and Leinster, S. J. (2007). Medical student’s perceptions of their educational environment: expected versus actual perceptions Med Ed 41: 265-272
3. www.ucas.ac.uk
4. Arulampalam, W, Naylor, R. A and Smith, J. P. (2007). Dropping out of medical school in the UK: explaining the changes over 10 years. Med Ed 41: 385-394
5. Lumb, A. B and Vail, A (2004). Comparison of academic, application form and social factors predicting early performance on the medical course. Med Ed 38: 1002-1005
Entry requirements
Open to UK/EU applicants only.
Applicants should hold a 2:1 degree or above or a master's degree in science, statistical modeling, biostatistics, social science or health related subject or equivalent.
Informal enquiries can be directed to Patrick Musonda.
Patrick Musonda, BSc(Hons), MSc(Medstats), PhD
Honorary Lecturer
Medical Statistician
University of East Anglia
Norwich NR4 7TJ
01603 591367
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