Diversity, inclusion, and bias in Continuing Medical Education activities: lessons learned from participant evaluations

PURPOSE: Recommendations to ensure diverse, equitable, and inclusive content in Continuing Medical Education (CME) have been developed, however, learners' perception of these efforts are unknown. Learner recognition of biased or non-inclusive content and satisfaction with activity diversity provides insight into the success of bias mitigation efforts during CME planning and delivery. This study's objective was to evaluate the types of bias identified by learners, and to evaluate learners' perception of inclusivity and satisfaction with the diversity of CME activities. STUDY DESIGN: This study was a retrospective mixed methods analysis of post-activity evaluation comments from 210 CME activities and 5,284 evaluations at a large Accreditation Council for Continuing Medical Education (ACCME)-accredited academic healthcare system from September 1, 2022 to December 31, 2023. RESULTS: Learners were satisfied with speaker and content diversity in 98.9% of activities. The qualitative analysis included 967 comments and demonstrated four main categories of perceived bias or lack of diversity identified by the CME activity learners: 1) Bias related to social identity factors, of which racial, ethnic, and gender bias were the most common forms identified by learners; 2) Lack of diversity in speakers, content and delivery; 3) Resistance to bias and inclusion evaluation questions; and 4) Commercial/industry bias. Further, some learners noted the instructional design of certain activities was not inclusive of all learners. CONCLUSION: These findings suggest that some CME activity learners perceive various forms of bias and lack of inclusivity and diversity despite efforts to review and mitigate bias in the planning and delivery of CME. While most CME activity learners were satisfied with speaker and content diversity, the data can inform more targeted efforts during the CME planning phase that focus on speaker and content diversity and screening for bias that goes beyond traditional industry/commercial bias.