Contribution of Cognitive and Non-Cognitive Admissions Measures to Pharm.D. Applicant Admission

Background: Recent literature reviews have highlighted the need for more quantitative measures of non-cognitive behavior that, coupled with cognitive measures, can inform a holistic Pharmacy admissions process to train expert clinical practitioners in patient-centered care in an inter-professional collaborative practice environment. The present investigation was a retrospective analysis of cognitive (science GPAs, PCAT composite scores) and non-cognitive (professional development ratings (RATE), and multiple mini-interview scores (MMI)) variables to determine how these metrics could contribute to an effective admissions process in a Pharm.D. program.

Methods: Analyses were conducted with de-identified data from applicants to a Pharm.D program during the 2015 and 2016 application cycles. Professional ratings (RATE) were developed by the college’s admissions committee and had been used effectively since 2010. MMI scenarios were initially obtained from a set developed by ProFitHR and then adapted for the US health care system and pharmacy by a scenario implementation team that was part of the MMI task force. A two-part screening system was utilized in which step 1 included GPA, PCAT and RATE to determine those individuals invited for interviews and step 2 included these plus the MMI score in a holistic review process. A final composite z-score, calculated as the arithmetic mean of the z-scores for GPA, PCAT, RATE and MMI was calculated and used to rank the interviewed applicants. This ranking was used as a check during admissions meetings and as a way to determine the order in which applications were discussed. Data were obtained from a total of 235 applicants who were interviewed in 2015 and 207 applicants who were interviewed in 2016. Data were analyzed by logistic regression. Exempt status was approved by the IRB.

Results: All four admissions variables showed significant overlap in relation to offers of admission along with discrimination between applicants receiving offers and those not receiving offers. There was less overlap for the MMI scores between applicants receiving offers and those not receiving offers. Logistic regression demonstrated that GPA, PCAT, RATE and MMI were all important predictors of admission.

Conclusions: Together, the results suggest that a 2-step admissions process consisting of a step 1 using cognitive and non-cognitive admissions measures refined by a step 2 with more advanced non-cognitive measures yields a balance of predictors to be used by admissions committees.

Author(s): Richard E Wilcox, Kenneth A Lawson and Wyanza R Acosta

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