Building and validating a predictive model for DVM academic performance

Abstract

Background: Predicting success in the veterinary curriculum with admissions variables is a longstanding interest of veterinary faculty. As linear models have consistently outperformed experts' opinions when making quantitative estimates, integrating them into admissions could both improve the outcome and reduce the burden of the admissions process. Aims and Objectives: To build and test linear models for predicting first year grade point average (GPA) and practice readiness in the Doctor of Veterinary Medicine (DVM) program. Materials and Methods: The authors built and validated models for predicting first year GPA and clinical rotation performance using data from the college's application management system and internal records. Lasso regression was used to select the subset of variables that best predicted both first year GPA and clinical faculty's ratings of practice readiness. Results: Validated models indicated no application variables reliably predicted practice readiness. Only total undergraduate GPA, GRE verbal/quantitate score, reference letter positivity, and number of unexplained course withdrawals reliably predicted first year GPA. Conclusion: Selecting applicants who will be successful in the first year of the veterinary curriculum is an important objective, particularly given the challenges many students face during this part of the veterinary curriculum. The overarching goal of a veterinary curriculum, however, is to produce practice ready veterinarians, thus additional work must be done to improve our ability to identify applicants who will be poised for success upon graduation.