How Well Do Selection Tools Predict Performance Later in a Medical Programme?

Autor/inn/en	Shulruf, Boaz; Poole, Phillippa; Wang, Grace Ying; Rudland, Joy; Wilkinson, Tim
Titel	How Well Do Selection Tools Predict Performance Later in a Medical Programme?
Quelle	In: Advances in Health Sciences Education, 17 (2012) 5, S.615-626 (12 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1382-4996
DOI	10.1007/s10459-011-9324-1
Schlagwörter	Medical Students; Medical Schools; Clinical Teaching (Health Professions); Structured Interviews; Statistical Analysis; Regression (Statistics); Predictor Variables; College Entrance Examinations; Academic Achievement; Academic Persistence; Grade Point Average; Selection Tools; Admission Criteria; Competitive Selection; Selection Criteria; Prediction; Predictive Validity + Suchen Sie Ihr Suchwort? Medizinische Ausbildung; Statistische Analyse; Regression; Regressionsanalyse; Prädiktor; Aufnahmeprüfung; Schulleistung; Auslesekriterium; Admission; Admission procedures; Zulassungsbedingung; Zulassungsverfahren; Zulassung; Selection criterion; Vorhersage
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 less than 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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10