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Scholarly Concentration Program Development: A Generalizable, Data-Driven Approach
Jesse Burk-Rafel1, Patricia B Mullan, Heather Wagenschutz
1J. Burk-Rafel is a fourth-year medical student, University of Michigan Medical School, Ann Arbor, Michigan. P.B. Mullan is professor of medical education, Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, Michigan. H. Wagenschutz is codirector, Paths of Excellence, and codirector for leadership, University of Michigan Medical School, Ann Arbor, Michigan. A. Pulst-Korenberg is a resident physician, Department of Emergency Medicine, University of Washington Medical Center, Seattle, Washington. E. Skye is codirector, Paths of Excellence, house director, M-Home Learning Community, and associate professor, University of Michigan Medical School, Ann Arbor, Michigan. M.M. Davis is professor of pediatrics, division head of Academic General Pediatrics, and director of the Smith Child Health Research Center, Ann and Robert H. Lurie Children's Hospital, Northwestern Feinberg School of Medicine, Chicago, Illinois.
Developing data-driven scholarly concentration programs in medical schools is essential. This study offers a method to align student interests with institutional capacity for effective program design.
Area of Science:
- Medical Education
- Curriculum Development
- Health Professions Education
Background:
- Scholarly concentration programs are increasingly prevalent in U.S. medical schools.
- Systematic, data-driven methodologies for developing these programs are lacking.
Purpose of the Study:
- To develop and validate a data-driven approach for designing scholarly concentration programs.
- To align program offerings with student preferences and institutional capabilities.
Main Methods:
- Analysis of scholarly concentration programs in top-ranked U.S. medical schools (n=43).
- Thematic coding of concentrations and mission statements.
- Student needs assessment survey (n=468) on preferences for program "Pathways" and content "Topics".
- Exploratory factor analysis (EFA) and capacity optimization modeling.
Main Results:
- Scholarly concentrations exist in 74% of surveyed schools, with an average of 6.2 per program.
- "Global/Public Health" and "Clinical/Translational Research" were the most common domains.
- Student survey revealed diverse preferences for Pathways and Topics.
- EFA identified eight factors influencing Topic preferences, linked to Pathway preferences.
- Capacity modeling suggested six Pathways could meet 95% of first-year students' top choices.
Conclusions:
- A generalizable, data-driven framework for scholarly concentration program development is presented.
- This approach integrates student preferences with institutional strengths and capacity.
- Optimizes program diversity and learner satisfaction within resource constraints.
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