Dietitian Led Competency Training to Reduce Weight Bias in Health Professions Education

Objectives: Weight bias remains pervasive in healthcare and contributes to poorer patient experiences and inequitable care. This study aimed to evaluate the effectiveness of the Weight of Words competency-based educational model in improving learners’ attitudes, beliefs, and weight-sensitive communication skills. Methods: Medical students enrolled in an osteopathic medical school were invited to participate in this one-arm educational intervention. An interdisciplinary dietitian-led team implemented the Weight of Words model, consisting of content delivery, experiential learning, structured reflection, and behavioral competency assessment within four asynchronous weight bias education e-modules and a standardized patient case for AI-simulated behavioral encounters (AI SBEs). Participants completed pre- and post-intervention assessments using validated instruments. Weight bias was assessed using the Attitudes Towards Persons with Higher Weight Scale (ATOP-HW) and Beliefs About Obese Persons Scale (BAOP), and AI SBE performance was evaluated using the AI-scored Weight Sensitivity Instrument © (WSI). Paired statistical analyses evaluated changes in attitudes, beliefs, and communication competency. Results: Fifty-nine participants completed pre- and post-educational intervention assessments. The average age of participants was 25.9 ± 2.9; 69.5% were female and 27.1% were male. Significant improvements were observed in attitudes (t(58) = –2.76, p = .008) and beliefs (t(58) = –2.69, p = .009). WSI© communication scores also significantly improved (t(57) = –6.44, p < .001), indicating enhanced weight-sensitive communication skills. Conclusions: Weight bias education delivered through a model such as Weight of Words may be an effective and sustainable strategy to effectively improve learners’ attitudes, beliefs, and communication behaviors. Broader integration within health professions education, particularly dietetics training, may strengthen provider - patient communication and support more equitable care for individuals with higher weight. Funding Sources: The Independence Blue Cross Foundation supported this work. AI Disclosures: SimConverse, a generative AI driven simulation platform, was used to implement and score the AI SBEs after tests for human-AI interrater reliability.