![Sample our Medicine, Dentistry, Nursing & Allied Health journals, sign in here to start your FREE access for 14 days]({"type":"image" , "src" : "/sda/5944/TOC-Subject-Sample-2021-Medicine-Dentistry-Nursing-Allied-Health.jpg"})
Abstract
Effective medical educators can engage learners through self-reflection. However, little is known about the relationships between teaching effectiveness and self-reflection in continuing medical education (CME). We aimed to determine associations between presenter teaching effectiveness and participant self-reflection in conference-based CME. This cross-sectional study evaluated presenters and participants at a national CME course. Participants provided CME teaching effectiveness (CMETE) ratings and self-reflection scores for each presentation. Overall CMETE and CME self-reflection scores (five-point Likert scale with one as strongly disagree and five as strongly agree) were averaged for each presentation. Correlations were measured among self-reflection, CMETE, and presentation characteristics. In total, 624 participants returned 430 evaluations (response, 68.9%) for the 38 presentations. Correlation between CMETE and self-reflection was medium (Pearson correlation, 0.3–0.5) or large (0.5–1.0) for most presentations (n = 33, 86.9%). Higher mean (SD) CME reflection scores were associated with clinical cases (3.66 [0.12] vs. 3.48 [0.14]; p = 0.003) and audience response (3.66 [0.12] vs. 3.51 [0.14]; p = 0.005). To our knowledge, this is the first study to show a relationship between teaching effectiveness and participant self-reflection in conference-based CME. Presenters should consider using clinical cases and audience response systems to increase teaching effectiveness and promote self-reflection among CME learners.
Disclosure statement
The authors report no conflicts of interest. The authors alone are responsible for the content and writing of this article.
Glossary
Noticing The first step in self-reflection of learners when their frameworks and beliefs are challenged by an educational encounter
Macrocontext A complex, realistic situation for solving problems
Notes on contributors
Dr. John T. Ratelle, MD, is a senior Associate Consultant, Division of Hospital Internal Medicine, Mayo Clinic, Rochester, Minnesota, and assistant professor of medicine, Mayo Clinic College of Medicine and Science.
Dr. Sara L. Bonnes, MD, is a senior Associate Consultant and Assistant Professor, Division of Hospital Internal Medicine, Mayo Clinic, Rochester, Minnesota.
Dr. Amy T. Wang, MD, is an Assistant Professor, Division of General Internal Medicine, Harbor-UCLA Medical Center, Torrance, California.
Ms Saswati Mahapatra, MS, is a Research Coordinator, Division of General Internal Medicine, Mayo Clinic, Rochester, Minnesota.
Ms Cathy D. Schleck, BS, is a statistician, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota.
Dr. Jayawant N. Mandrekar, PhD, is a consultant, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota, and professor of biostatistics and neurology, Mayo Clinic College of Medicine and Science.
Dr. Karen F. Mauck, MD, MSc, is a consultant, Division of General Internal Medicine, Mayo Clinic, Rochester, Minnesota, and associate professor of medicine, Mayo Clinic College of Medicine and Science.
Dr. Thomas J. Beckman, MD, is a consultant, Division of General Internal Medicine, Mayo Clinic, Rochester, Minnesota, and professor of medical education and medicine, Mayo Clinic College of Medicine and Science.
Dr. Christopher M. Wittich, MD, PharmD, is a consultant, Division of General Internal Medicine, Mayo Clinic, Rochester, Minnesota, and associate professor of medicine, Mayo Clinic College of Medicine and Science.