TY - JOUR
T1 - From admissions to licensure
T2 - education data associations from a multi-centre undergraduate medical education collaboration
AU - Chahine, S.
AU - Bartman, I.
AU - Kulasegaram, K.
AU - Archibald, D.
AU - Wang, P.
AU - Wilson, C.
AU - Ross, B.
AU - Cameron, E.
AU - Hogenbirk, J.
AU - Barber, C.
AU - Burgess, R.
AU - Katsoulas, E.
AU - Touchie, C.
AU - Grierson, L.
N1 - Publisher Copyright:
© The Author(s), under exclusive licence to Springer Nature B.V. 2024.
PY - 2024/9
Y1 - 2024/9
N2 - This paper reports the findings of a Canada based multi-institutional study designed to investigate the relationships between admissions criteria, in-program assessments, and performance on licensing exams. The study’s objective is to provide valuable insights for improving educational practices across different institutions. Data were gathered from six medical schools: McMaster University, the Northern Ontario School of Medicine University, Queen’s University, University of Ottawa, University of Toronto, and Western University. The dataset includes graduates who undertook the Medical Council of Canada Qualifying Examination Part 1 (MCCQE1) between 2015 and 2017. The data were categorized into five distinct sections: demographic information as well as four matrices: admissions, course performance, objective structured clinical examination (OSCE), and clerkship performance. Common and unique variables were identified through an extensive consensus-building process. Hierarchical linear regression and a manual stepwise variable selection approach were used for analysis. Analyses were performed on data set encompassing graduates of all six medical schools as well as on individual data sets from each school. For the combined data set the final model estimated 32% of the variance in performance on licensing exams, highlighting variables such as Age at Admission, Sex, Biomedical Knowledge, the first post-clerkship OSCE, and a clerkship theta score. Individual school analysis explained 41–60% of the variance in MCCQE1 outcomes, with comparable variables to the analysis from of the combined data set identified as significant independent variables. Therefore, strongly emphasising the need for variety of high-quality assessment on the educational continuum. This study underscores the importance of sharing data to enable educational insights. This study also had its challenges when it came to the access and aggregation of data. As such we advocate for the establishment of a common framework for multi-institutional educational research, facilitating studies and evaluations across diverse institutions. This study demonstrates the scientific potential of collaborative data analysis in enhancing educational outcomes. It offers a deeper understanding of the factors influencing performance on licensure exams and emphasizes the need for addressing data gaps to advance multi-institutional research for educational improvements.
AB - This paper reports the findings of a Canada based multi-institutional study designed to investigate the relationships between admissions criteria, in-program assessments, and performance on licensing exams. The study’s objective is to provide valuable insights for improving educational practices across different institutions. Data were gathered from six medical schools: McMaster University, the Northern Ontario School of Medicine University, Queen’s University, University of Ottawa, University of Toronto, and Western University. The dataset includes graduates who undertook the Medical Council of Canada Qualifying Examination Part 1 (MCCQE1) between 2015 and 2017. The data were categorized into five distinct sections: demographic information as well as four matrices: admissions, course performance, objective structured clinical examination (OSCE), and clerkship performance. Common and unique variables were identified through an extensive consensus-building process. Hierarchical linear regression and a manual stepwise variable selection approach were used for analysis. Analyses were performed on data set encompassing graduates of all six medical schools as well as on individual data sets from each school. For the combined data set the final model estimated 32% of the variance in performance on licensing exams, highlighting variables such as Age at Admission, Sex, Biomedical Knowledge, the first post-clerkship OSCE, and a clerkship theta score. Individual school analysis explained 41–60% of the variance in MCCQE1 outcomes, with comparable variables to the analysis from of the combined data set identified as significant independent variables. Therefore, strongly emphasising the need for variety of high-quality assessment on the educational continuum. This study underscores the importance of sharing data to enable educational insights. This study also had its challenges when it came to the access and aggregation of data. As such we advocate for the establishment of a common framework for multi-institutional educational research, facilitating studies and evaluations across diverse institutions. This study demonstrates the scientific potential of collaborative data analysis in enhancing educational outcomes. It offers a deeper understanding of the factors influencing performance on licensure exams and emphasizes the need for addressing data gaps to advance multi-institutional research for educational improvements.
KW - Assessment measures
KW - Educational outcomes
KW - Hierarchical regression
KW - Institutional collaboration
KW - Longitudinal study
KW - Multi-institutional
U2 - 10.1007/s10459-024-10326-2
DO - 10.1007/s10459-024-10326-2
M3 - Article
SN - 1382-4996
VL - 29
SP - 1393
EP - 1415
JO - Advances in Health Sciences Education
JF - Advances in Health Sciences Education
IS - 4
ER -