2026 Eastern States Conference

Title: Correlating application screening rubric categories with interview invitations at a postgraduate year 1 (PGY1) residency program
Authors: Paige Spencer, PharmD, Gail M. Sanchez, PharmD, BCPS, DPLA and Xia Thai, PharmD, BCPS; Cambridge Health Alliance (CHA), Cambridge, MA.
Objective: At the conclusion of the presentation, audience members will be able to state the process used to evaluate an application screening rubric for a PGY1 residency program.
Self Assessment Question: What is the importance of evaluating an application screening rubric for PGY1 residency programs discussed today?
Background: Identify which screening rubric categories are significant predictors of PGY1 interview offers.
Methods: This retrospective quality improvement project evaluates the scoring of PGY1 residency applications utilizing our institution’s ten category weighted rubric. Completed applications received through the online centralized application service by the cycle deadline of the Phase 1 Match from 2017-2024 are included and de-identified using a random number generator. The candidates invited for an interview serve as the test group and those not invited serve as the control group. A logistic regression analysis will be performed. The primary outcome will be the correlation of each individual rubric category score and whether or not the candidate was offered an interview. Secondary outcomes will include the correlation between interview invitation and the practice area of the reviewer and the pharmacy school location. We hypothesize the correlation analysis will determine how to best modify the screening rubric to improve efficiency while still offering interviews to highly qualified candidates.
Results: Our program received 441 completed applications from 2017 to 2024. We excluded 12 applications due to incorrect documentation of screening scores leaving 429 applications included in the final analysis. Of the 429 applicants, 209 (49%) received an interview offer. In the final multivariate logistic regression, the overall impression variable was excluded to address multicollinearity, while publications and other skills were removed due to a lack of statistical significance. The resulting model demonstrated a robust fit (McFadden’s R2= 0.32, p<0.001), identifying seven rubric categories as significant predictors of interview offers: grades, leadership, letter of intent, letter of recommendation, presentations, rotations, and work experience.
Conclusion: Based on our results and the relative importance of each category to our institution, the authors plan to modify the screening rubric for future PGY1 recruitment cycles. These changes aim to improve efficiency while continuing to identify highly qualified candidates.