Raters Broadly Sample the Data From an Innovative Admissions Program

• In Event: Admissions and Enrollment Management in Health Professions Education

Abstract

Our Medical School has instituted an innovative admissions program, including a group task on interview day. In this study, we used multiple linear regression to assess the influence of components of the data on final candidate rankings. We tested a model, based upon ratings of academics, interviews and written applications, and found that it accounted for 52% of the variance in outcomes. Importantly, our findings set upper limits on the influence of the MCAT upon rankings. A model excluding academic measures still accounted for 46% of outcome variance. Given the established role of the MCAT as a barrier to under-represented in medicine (URM) applicants, the MCAT’s modest impact upon our final rankings suggests our innovations may improve URM enrollment.

Authors

• Joel Daboub, University of Texas at Austin

• Stephen Smith

• Andrew David Bergemann, University of Texas at Austin