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Updated: Feb 22, 2026

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques
Published on: December 9, 2022
Abordar la disminución de admisiones en farmacia a través de un sistema de asesoramiento y liderazgo pre-salud
1Department of Pharmaceutical and Biomedical Sciences, California Northstate University College of Pharmacy, 9700 W Taron Drive, Elk Grove, CA 95758, USA.
Abstract:
To enhance PharmD student leadership and advocacy skills, combat the paucity of trained pre-health advisors for pharmacy admissions, augment community relationships, and increase pharmacy admissions volume, we designed, implemented, and assessed PAALS, a Pre-health Academic Advising and Leadership System. PAALS was grounded in Astin's Theory of Student Involvement and evaluated using the RE-AIM implementation science framework. RE-AIM measured outcomes across Reach, Effectiveness, Adoption, Implementation, and Maintenance as indicators of PAALS's scale, fidelity, sustainability, and institutional embedding. Analysis of PAALS using the RE-AIM framework demonstrated the following outcomes: (1) Reach: 42 P1-P3 PharmD students participated as mentors; external partnerships expanded from 2 to 8 regional high schools and community programs; and more than 25 mentored learners successfully matriculated into the PharmD program. (2) Effectiveness: students enacted sustained leadership, advocacy, and mentoring roles. (3) Adoption: voluntary uptake of mentoring and governance roles by PharmD students occurred with repeated engagement by external partner institutions. (4) Implementation: Core program components were delivered consistently using existing institutional resources. (5) Maintenance: PAALS remained operational across five academic years despite student turnover, with leadership succession and institutional embedding sustained across cohorts. Our findings demonstrate that student-led advising and advocacy ecosystems address critical gaps in pharmacy-specific pre-health advising models.
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