How well do selection tools predict performance later in a medical programme?

Article

Shulruf, Boaz, Poole, Phillippa, Wang, Grace Ying, Rudland, Joy and Wilkinson, Tim. 2012. "How well do selection tools predict performance later in a medical programme?" Advances in Health Sciences Education. 17 (5), pp. 615-626. https://doi.org/10.1007/s10459-011-9324-1

Article Title	How well do selection tools predict performance later in a medical programme?
ERA Journal ID	20516
Article Category	Article
Authors	Shulruf, Boaz (Author), Poole, Phillippa (Author), Wang, Grace Ying (Author), Rudland, Joy (Author) and Wilkinson, Tim (Author)
Journal Title	Advances in Health Sciences Education
Journal Citation	17 (5), pp. 615-626
Number of Pages	12
Year	2012
Place of Publication	Netherlands
ISSN	1382-4996
	1573-1677
Digital Object Identifier (DOI)	https://doi.org/10.1007/s10459-011-9324-1
Web Address (URL)	https://link.springer.com/article/10.1007/s10459-011-9324-1
Abstract	The choice of tools with which to select medical students is complex and controversial. This study aimed to identify the extent to which scores on each of three admission tools (Admission GPA, UMAT and structured interview) predicted the outcomes of the first major clinical year (Y4) of a 6 year medical programme. Data from three student cohorts (n = 324) were analysed using regression analyses. The Admission GPA was the best predictor of academic achievement in years 2 and 3 with regression coefficients (B) of 1.31 and 0.9 respectively (each P < 0.001). Furthermore, Admission GPA predicted whether or not a student was likely to earn 'Distinction' rather than 'Pass' in year 4. In comparison, UMAT and interview showed low predictive ability for any outcomes. Interview scores correlated negatively with those on the other tools. None of the tools predicted failure to complete year 4 on time, but only 3% of students fell into this category. Prior academic achievement remains the best measure of subsequent student achievement within a medical programme. Interview scores have little predictive value. Future directions include longer term studies of what UMAT predicts, and of novel ways to combine selection tools to achieve the optimum student cohort.
Keywords	Admission criteria; GPA; Interviews; Medical programme; Student selection; UMAT
ANZSRC Field of Research 2020	390201. Education policy
	390303. Higher education
Public Notes	Files associated with this item cannot be displayed due to copyright restrictions.
Byline Affiliations	University of Auckland, New Zealand
	University of Otago, New Zealand
Institution of Origin	University of Southern Queensland

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