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The efficacy of medical student selection tools in Australia and New Zealand


Shulruf, B and Bagg, W and Begun, M and Hay, M and Lichtwark, I and Turnock, A and Warnecke, E and Wilkinson, TJ and Poole, PJ, The efficacy of medical student selection tools in Australia and New Zealand, Medical Journal of Australia, 208, (5) pp. 214-218. ISSN 0025-729X (2018) [Refereed Article]

Copyright Statement

Copyright 2018 AMPCo Pty Ltd. Produced with Elsevier B.V. All rights reserved.

DOI: doi:10.5694/mja17.00400


Objectives: To estimate the efficacy of selection tools employed by medical schools for predicting the binary outcomes of completing or not completing medical training and passing or failing a key examination; to investigate the potential usefulness of selection algorithms that do not allow low scores on one tool to be compensated by higher scores on other tools.

Design, setting and participants: Data from four consecutive cohorts of students (3378 students, enrolled 20072010) in five undergraduate medical schools in Australia and New Zealand were analysed. Predictor variables were student scores on selection tools: prior academic achievement, Undergraduate Medicine and Health Sciences Admission Test (UMAT), and selection interview. Outcome variables were graduation from the program in a timely fashion, or passing the final clinical skills assessment at the first attempt.

Main outcome measures: Optimal selection cut-scores determined by discriminant function analysis for each selection tool at each school; efficacy of different selection algorithms for predicting student outcomes.

Results: For both outcomes, the cut-scores for prior academic achievement had the greatest predictive value, with medium to very large effect sizes (0.441.22) at all five schools. UMAT scores and selection interviews had smaller effect sizes (0.000.60). Meeting one or more cut-scores was associated with a significantly greater likelihood of timely graduation in some schools but not in others.

Conclusions: An optimal cut-score can be estimated for a selection tool used for predicting an important program outcome. A "sufficient evidence" selection algorithm, founded on a non-compensatory model, is feasible, and may be useful for some schools.

Item Details

Item Type:Refereed Article
Keywords:medical education, medical students, Australia, New Zealand, Undergraduate Medicine and Health Sciences Admission Test, academic achievement
Research Division:Health Sciences
Research Group:Health services and systems
Research Field:Health services and systems not elsewhere classified
Objective Division:Health
Objective Group:Other health
Objective Field:Other health not elsewhere classified
UTAS Author:Turnock, A (Dr Allison Turnock)
UTAS Author:Warnecke, E (Dr Emma Warnecke)
ID Code:125816
Year Published:2018
Web of Science® Times Cited:11
Deposited By:Medicine
Deposited On:2018-05-08
Last Modified:2018-12-11

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