Associations between demographic factors and the academic trajectories of medical students in Japan.

Abstract

BackgroundGroup-based trajectory modeling is a useful tool for categorizing students’ academic trajectories and their determinants. Using insights gained from the analyses, we can identify students at risk for poor academic performance and monitor them to provide support. To date, studies investigating the associations between demographic factors and academic performance trajectories among medical students are scarce. The study objective was to examine the associations between demographic factors and academic performance trajectories in medical students using group-based trajectory modeling.MethodsParticipants included all medical students admitted to Tokyo Medical and Dental University in Japan in 2013 and 2014 (n = 202). Academic performance was evaluated by biannual grade point average (GPA) scores in preclinical years. We used group-based trajectory modeling to categorize students into GPA trajectories. Multinomial logistic regression was used to examine the association between the odds of being in a certain GPA trajectory group and demographic factors such as high school type, high school geographical area, admission test type, high school graduation year, whether the student was a biology major, and sex.ResultsStudents’ GPA trajectories were classified into four trajectory groups as well as another group that consisted of students who withdrew or repeated years. We found that students whose high school geographical area was outside the National Capital Region were 7.2 times more likely to withdraw or repeat years in comparison with students whose school was inside the National Capital Region (OR: 7.21, 95% CI: 1.87, 27.76). In addition, admission test type, high school graduation year, and sex were associated with GPA trajectories.ConclusionsHigh school geographical area, admission test type, high school graduation year, and sex were associated with GPA trajectories. These findings provide important insights into identifying students at risk for poor academic performance and strategies for monitoring them to provide adequate and timely support.