Peer-Developed Modules on Basic Biostatistics and Evidence-Based Medicine Principles for Undergraduate Medical Education

MedEdPORTAL. 2020 Nov 24;16:11026. doi: 10.15766/mep_2374-8265.11026.


Introduction: Evidence-based medicine (EBM) is pivotal in shaping patient care, yet it is challenging to incorporate into undergraduate medical education (UME) due to a lack of dedicated resources within the preclinical curriculum. To address this challenge, we used a peer-led approach to explain difficult concepts through language that students can understand at their shared level of understanding.

Methods: Four second-year medical students trained in EBM over 18 months by facilitating monthly journal clubs, ultimately leading to their involvement as peer-instructors. With input from a faculty expert, peer-instructors designed integrative PowerPoint modules and interactive problem sets on basic biostatistics and EBM principles. Assessment included formative quizzes with multiple attempts to achieve at least 80% to demonstrate mastery of core learning objectives. Afterwards, students were invited to provide feedback using a 5-point Likert scale survey.

Results: Of second-year students who participated, all 151 demonstrated 80% competency on each quiz. Eighty-seven (58%) students completed the survey on which, 77% agreed/strongly agreed that their level of understanding of EBM improved after the peer-led sessions, 76% agreed/strongly agreed that the sessions were more conducive to learning compared to traditional lectures, and 94% agreed/strongly agreed that the material covered was relevant to the USMLE Step 1.

Discussion: This peer-led approach has been rated as effective by learners, improving their ability to critically appraise and apply clinical evidence. To promote integration of EBM into UME, we have prepared modules, problem sets, quizzes, and an outline of the problem-solving sessions for universal adoption.

Keywords: Biostatistics; Evidence-Based Medicine; Flipped Classroom; Peer-Learning; Problem-Based Learning; Self-Assessment; Self-Directed Learning; Self-Regulated Learning; Statistics.