Identifying Meaningful Patterns of Internal Medicine Clerkship Grading Distributions: Application of Data Science Techniques Across 135 U.S. Medical Schools. (17th February 2023) - Burk-Rafel, Jesse; Reinstein, Ilan; Park, Yoon Soo
Abstract : Problem: Residency program directors use clerkship grades for high-stakes selection decisions despite substantial variability in grading systems and distributions. The authors apply clustering techniques from data science to identify groups of schools for which grading distributions were statistically similar in the internal medicine clerkship. Approach: Grading systems (e.g., honors/pass/fail) and distributions (i.e., percent of students in each grade tier) were tabulated for the internal medicine clerkship at U.S. MD-granting medical schools by manually reviewing Medical Student Performance Evaluations (MSPEs) in the 2019 and 2020 residency application cycles. Grading distributions were analyzed using k-means cluster analysis, with the optimal number of clusters selected using model fit indices. Outcomes: Among the 145 medical schools with available MSPE data, 64 distinct grading systems were reported. Among the 135 schools reporting a grading distribution, the median percent of students receiving the highest and lowest tier grade was 32% (range: 2%–66%) and 2% (range: 0%–91%), respectively. Four clusters was the most optimal solution (η 2 = 0.8): cluster 1 (45% [highest grade tier]–45% [middle tier]–10% [lowest tier], n = 64 [47%] schools), cluster 2 (25%–30%–45%, n = 40 [30%] schools), cluster 3 (20%–75%–5%, n = 25 [19%] schools), and cluster 4 (15%–25%–25%–25%–10%, n = 6 [4%] schools). The findings suggest internal medicine clerkship grading systems may be more comparable across institutions than previously thought. Next Steps: The authors will prospectively review reported clerkship grading approaches across additional specialties and are conducting a mixed-methods analysis, incorporating a sequential explanatory model, to interview stakeholder groups on the use of the patterns identified.