The relationship between extracurricular activities assessed during selection and during medical school and performance

Abstract

Several medical schools include candidates' extracurricular activities in their selection procedure, with promising results regarding their predictive value for achievement during the clinical years of medical school. This study aims to reveal whether the better achievement in clinical training of students selected on the basis of their extracurricular activities could be explained by persistent participation in extracurricular activities during medical school (msECAs). Lottery-admitted and selected student admission groups were compared on their participation in three types of msECAs: (1) research master, (2) important board positions or (3) additional degree programme. Logistic regression was used to measure the effect of admission group on participation in any msECA, adjusted for pre-university GPA. Two-way ANCOVA was used to examine the inter-relationships between admission group, participation in msECAs and clerkship grade, with pre-university GPA as covariate. Significantly more selected students compared to lottery-admitted students participated in any msECA. Participation in msECAs was associated with a higher pre-university GPA for lottery-admitted students only, whereas participation in msECAs was associated with higher clerkship grades for selected students only. These results suggest that persistent participation in extracurricular activities of selected students favours better clinical achievement, supporting the inclusion of ECAs in the selection procedure. More insight in the rationale behind participation in extracurricular activities during medical school may explain differences found between lottery-admitted and selected students.

Lessons learned from 15 years of non-grades-based selection for medical school

“…It should be noted, however, that this study did not look at differences in clinical performance between the three groups. In our most recent study we found that students selected on the basis of their puECAs persisted in their ECAs during medical school and that this persistent participation was related to better clinical performance, which further supports the inclusion of puECAs in the selection procedure …”

“…In our most recent study we found that students selected on the basis of their puECAs persisted in their ECAs during medical school and that this persistent participation was related to better clinical performance, which further supports the inclusion of puECAs in the selection procedure. 41 It is good to note that although puECAs can be seen as a form of personal statements, 17 their assessment, at least in the Dutch medical schools, is different from analysing the content of free response personal statements, as for example described by Ferguson et al 42 The use of a highly structured application form in combination with a highly structured rating form provides a high degree of structure to the raters' judgements. This may positively affect the criterion-related validity, in the same way as job interviews are positively affected by the degree of structure.…”

A Signal Detection Approach in a Multiple Cohort Study: Different Admission Tools Uniquely Select Different Successful Students

“…The predictive validity of SSGPA, ACS, and NCS was, as expected, confirmed by a comparison of Signal Detection analyses and correlational analyses in a previous study (van Ooijen-van der Linden et al, 2017) and the regression analyses matching the Signal Detection analyses for the data in this study are presented in Online Resource 1. A summary of the data collection and Signal Detection approach used in this study, a specification of b and d in Figure 1 for this study, is depicted in Figure 2.…”

“…Research into predictors other than secondary school grade point average (SSGPA)-which is far from accurate-is still work in progress, and a "gold standard" has not yet been conceived (Richardson et al, 2012;Schripsema et al, 2014Schripsema et al, , 2017Shulruf and Shaw, 2015;Makransky et al, 2016;Patterson et al, 2016;Pau et al, 2016;Sladek et al, 2016;Yhnell et al, 2016;Wouters et al, 2017). Program-and institution specific work samples appear promising valuable predictors of academic achievement in addition to past academic achievement (Niessen et al, 2016;Stegers-Jager, 2017;van Ooijen-van der Linden et al, 2017).…”

“…These latter measures are intended to capture motivation, community commitment or leadership skills that may be predictive of future academic achievement, and clearly differ from testing gained knowledge and competencies, such as writing, reasoning and abstract thinking (Urlings-Strop et al, 2017). To date, the available knowledge about the characteristics of admission tools is primarily based on analyses at group level (Schmitt, 2012;Stegers-Jager et al, 2015).…”