Scholarly Concentration Program Development: A Generalizable, Data-Driven Approach

Abstract

Purpose: Scholarly concentration programs-also known as scholarly projects, pathways, tracks, or pursuits-are increasingly common in U.S. medical schools. However, systematic, data-driven program development methods have not been described.

Method: The authors examined scholarly concentration programs at U.S. medical schools that U.S. News & World Report ranked as top 25 for research or primary care (n = 43 institutions), coding concentrations and mission statements. Subsequently, the authors conducted a targeted needs assessment via a student-led, institution-wide survey, eliciting learners' preferences for 10 "Pathways" (i.e., concentrations) and 30 "Topics" (i.e., potential content) augmenting core curricula at their institution. Exploratory factor analysis (EFA) and a capacity optimization algorithm characterized best institutional options for learner-focused Pathway development.

Results: The authors identified scholarly concentration programs at 32 of 43 medical schools (74%), comprising 199 distinct concentrations (mean concentrations per program: 6.2, mode: 5, range: 1-16). Thematic analysis identified 10 content domains; most common were "Global/Public Health" (30 institutions; 94%) and "Clinical/Translational Research" (26 institutions; 81%). The institutional needs assessment (n = 468 medical students; response rate 60% overall, 97% among first-year students) demonstrated myriad student preferences for Pathways and Topics. EFA of Topic preferences identified eight factors, systematically related to Pathway preferences, informing content development. Capacity modeling indicated that offering six Pathways could guarantee 95% of first-year students (162/171) their first- or second-choice Pathway.

Conclusions: This study demonstrates a generalizable, data-driven approach to scholarly concentration program development that reflects student preferences and institutional strengths, while optimizing program diversity within capacity constraints.