How we used two social media tools to enhance aspects of active learning during lectures.

BACKGROUND: Medical education is evolving to include active learning approaches, yet some courses will remain lecture-based. Social media tools used by students may foster collaborative learning during lectures. AIM: We present preliminary results from a pilot study that integrated two 'social' technologies, Google Docs and SurveyMonkey, into 22 hour-long lectures for a course called "Social Influences on Health" attended by 154 students. METHODS: At the conclusion of the semester, we reviewed student usage patterns with both technologies and collected data from students via course evaluations that included a standard Likert Scale. We used thematic analysis to identify emergent themes from evaluations. RESULTS: On average, students contributed 6 comments/questions to the Google Doc in each lecture, and 35 students participated in SurveyMonkey. Engagement with both technologies increased throughout the semester and no unprofessional incidents were observed. The mean student rating for integration of Google Docs and SurveyMonkey was 3.4 or "above average" (SD = 1.17). Thematic analysis identified perceived strengths of this approach as well as areas for improvement. CONCLUSIONS: Social media such as Google Docs and SurveyMonkey can facilitate interaction and provide students with control over content and flow of lecture-based courses, but educators must be mindful of practical and conceptual limitations.

Culinary Medicine for Family Medicine Residents.

BACKGROUND: Physicians are inadequately trained to effectively provide lifestyle counseling to manage the obesity epidemic. ACTIVITY: Family and community medicine residents participated in a culinary medicine course taught by two Registered Dietitian Nutritionists (RDNs) in University Park, PA, in March 2020. RESULTS: All residents (n = 13) reported increased knowledge, 92% (n = 12) reported increased confidence, and 84.6% (n = 11) reported they intended to make changes to their practice based on the class. Most participating residents (85%, n = 11) would attend follow-up classes. DISCUSSION: Residents recognized the importance of nutrition in clinical care and found the information to be applicable to practice.

Machine Learning-Augmented Propensity Score-Adjusted Multilevel Mixed Effects Panel Analysis of Hands-On Cooking and Nutrition Education versus Traditional Curriculum for Medical Students as Preventive Cardiology: Multisite Cohort Study of 3,248 Trainees over 5 Years.

BACKGROUND: Cardiovascular disease (CVD) annually claims more lives and costs more dollars than any other disease globally amid widening health disparities, despite the known significant reductions in this burden by low cost dietary changes. The world's first medical school-based teaching kitchen therefore launched CHOP-Medical Students as the largest known multisite cohort study of hands-on cooking and nutrition education versus traditional curriculum for medical students. METHODS: This analysis provides a novel integration of artificial intelligence-based machine learning (ML) with causal inference statistics. 43 ML automated algorithms were tested, with the top performer compared to triply robust propensity score-adjusted multilevel mixed effects regression panel analysis of longitudinal data. Inverse-variance weighted fixed effects meta-analysis pooled the individual estimates for competencies. RESULTS: 3,248 unique medical trainees met study criteria from 20 medical schools nationally from August 1, 2012, to June 26, 2017, generating 4,026 completed validated surveys. ML analysis produced similar results to the causal inference statistics based on root mean squared error and accuracy. Hands-on cooking and nutrition education compared to traditional medical school curriculum significantly improved student competencies (OR 2.14, 95% CI 2.00-2.28, p < 0.001) and MedDiet adherence (OR 1.40, 95% CI 1.07-1.84, p = 0.015), while reducing trainees' soft drink consumption (OR 0.56, 95% CI 0.37-0.85, p = 0.007). Overall improved competencies were demonstrated from the initial study site through the scale-up of the intervention to 10 sites nationally (p < 0.001). DISCUSSION: This study provides the first machine learning-augmented causal inference analysis of a multisite cohort showing hands-on cooking and nutrition education for medical trainees improves their competencies counseling patients on nutrition, while improving students' own diets. This study suggests that the public health and medical sectors can unite population health management and precision medicine for a sustainable model of next-generation health systems providing effective, equitable, accessible care beginning with reversing the CVD epidemic.