title: Graduate entry medicine: selection criteria and student performance.
abstract: BACKGROUND
  Graduate entry medicine raises new questions about the suitability of students with different backgrounds. We examine this, and the broader issue of effectiveness of selection and assessment procedures.
  
  
  METHODS
  The data included background characteristics, academic record, interview score and performance in pre-clinical modular assessment for two years intake of graduate entry medical students. Exploratory factor analysis is a powerful method for reducing a large number of measures to a smaller group of underlying factors. It was used here to identify patterns within and between the selection and performance data.
  
  
  RESULTS
  Basic background characteristics were of little importance in predicting exam success. However, easily interpreted components were detected within variables comprising the 'selection' and 'assessment' criteria. Three selection components were identified ('Academic', 'GAMSAT', 'Interview') and four assessment components ('General Exam', 'Oncology', 'OSCE', 'Family Case Study'). There was a striking lack of relationships between most selection and performance factors. Only 'General Exam' and 'Academic' showed a correlation (Pearson's r = 0.55, p<0.001).
  
  
  CONCLUSIONS
  This study raises questions about methods of student selection and their effectiveness in predicting performance and assessing suitability for a medical career. Admissions tests and most exams only confirmed previous academic achievement, while interview scores were not correlated with any consequent assessment.
authors: School of Medicine, Swansea University, Swansea, United Kingdom. O.Bodger@swansea.ac.uk
authors: Owen
authors: O
authors: Bodger
authors: Aidan
authors: A
authors: Byrne
authors: Philip A
authors: PA
authors: Evans
authors: Sarah
authors: S
authors: Rees
authors: Gwen
authors: G
authors: Jones
authors: Claire
authors: C
authors: Cowell
authors: Mike B
authors: MB
authors: Gravenor
authors: Rhys
authors: R
authors: Williams
mesh_terms: D019992:Databases as Topic; D004504:Education, Medical, Undergraduate / Q000706:statistics & numerical data*; D004521:Educational Measurement / Q000706:statistics & numerical data*; D005163:Factor Analysis, Statistical; D005260:Female; D006801:Humans; D008297:Male; D015999:Multivariate Analysis; D017711:Nonlinear Dynamics; D018401:Sample Size; D012570:School Admission Criteria / Q000706:statistics & numerical data*; D013337:Students, Medical / Q000706:statistics & numerical data*