Predictive value of veterinary student application data for assessing adjustment to year 1 of veterinary school

Abstract

Background: The veterinary education literature warns of psychological distress among doctor of veterinary medicine (DVM) students. Despite its impact on veterinary education, there is little research on predictors of DVM student wellbeing, as most researchers have focused on predictors of academic performance. Methods: We used best subset regression to examine the relationship between application variables and student’s self-reported first year well-being. Objective: To determine whether variables available in students’ applications to veterinary school predicted self-reported well-being in their first year. Results: Age, graduate record exam (GRE) score, pre-DVM grade point average (GPA), hometown population, and paid employment experience were all significant predictors of various facets for self-reported first year well-being and involvement among DVM students. Conclusions: The predictive value of these variables, while significant, was quite low; consequently, veterinary educators should not base policy decisions on this information alone. We hope that our model serves as a useful tool to predict which applicants may need additional wellness resources during their first year.