12 research outputs found

    Using Garden-Based Service-Learning to Work Toward Food Justice, Better Educate Students, and Strengthen Campus-Community Ties

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    In this article, we present several approaches for using garden-based service-learning to work toward food justice, better educate undergraduate students, and strengthen campus-community ties. We begin by introducing several key concepts related to food justice, community gardens as a strategy for strengthening food security and community development, and service-learning as a pedagogical tool for educating students about social justice, civic engagement, and personal responsibility for positive social change. We then discuss three of our service-learning projects in depth from an interdisciplinary perspective: the Fairmount Community Garden, the North Side Garden Survey, and the Como Community Garden. We evaluate the success of our approaches using multiple measures and identify the benefits our approaches have provided for undergraduates, community partners, communities served by the gardens, educators, and our university. We also discuss lessons we have learned, offer suggestions for best practices to follow in developing future garden-based service-learning projects, and compare and contrast our pedagogy with that of critical service-learning

    Families and Community Partners Learning Together to Prevent Obesity

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    Reducing adult and childhood obesity is a shared community responsibility. Extension professionals can play an important role in developing collaborative, interdisciplinary partnerships with multiple community members to help reduce obesity and related chronic disease that disproportionately affect people with limited resources. This article describes how community partners learned to work together in implementing a family intervention pilot program in a low-income urban population at high risk for obesity and related chronic disease. Healthy Weigh/El camino saludable, a successful community-campus partnership, helps families learn to adopt healthy eating and physical activity patterns

    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

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    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

    Chelation Therapy

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