Are We Listening to Our Limited-resource Audiences? Engaging Parents and Caregivers with Nutrition Messaging in Extension Programs

Social marketing campaigns can be an effective method for reaching and engaging limited-resource participants in health messages related to nutrition and physical activity. The target audience should be engaged throughout the process of message identification and creation. This study included focus groups with limited-resource caregivers to help identify information sources utilized, nutrition messages most likely to resonate, and preferred communication channels. Nine focus groups with 108 limited-resource caregivers were conducted. Most participants were between the ages of 21 and 44, with a majority receiving SNAP benefits. All participants were female with over half identifying as white. Two researchers coded focus group transcripts and identified themes. Participants expressed interest in nutrition particularly as it impacted children in their care. Nutrition information sources included family, health care professionals, and television celebrities. Participants expressed interest in practical nutrition content delivered through simple messaging. Communication channels frequently utilized included social media, posters in community agencies, and television news. Findings offer insight for nutrition professionals in creating and marketing nutrition messages that are competitive, accessible, and resonate with limited-resource caregivers

An Innovative Approach for Community Engagement: Using an Audience Response System

Community-based participatory research methods allow for community engagement in the effort to reduce cancer health disparities. Community engagement involves health professionals becoming a part of the community in order to build trust, learn from the community and empower them to reduce disparities through their own initiatives and ideas. Audience Response Systems (ARS) are an innovative and engaging way to involve the community and obtain data for research purposes using keypads to report results via power point. The use of ARS within communities is very limited and serves to widen the disparity gap by not delivering new advances in medical knowledge and technology among all population groups. ARS was implemented at a community town hall event sponsored by a National Institute on Minority Health and Health Disparities Exploratory Center of Excellence, the Center for Equal Health. Participants appreciated being able to see how everyone else answered and felt included in the research process. ARS is beneficial because the community can answer truthfully and provides instant research results. Additionally, researchers can collect large amounts of data quickly, in a non-threatening way while tracking individual responses anonymously. Audience Response Systems proved to be an effective tool for successfully accomplishing community-based participatory research

Deep Reinforcement Learning for Approximate Policy Iteration: Convergence Analysis and a Post-Earthquake Disaster Response Case Study

Approximate Policy Iteration (API) is a Class of Reinforcement Learning (RL) Algorithms that Seek to Solve the Long-Run Discounted Reward Markov Decision Process (MDP), Via the Policy Iteration Paradigm, Without Learning the Transition Model in the Underlying Bellman Equation. Unfortunately, These Algorithms Suffer from a Defect Known as Chattering in Which the Solution (Policy) Delivered in Each Iteration of the Algorithm Oscillates between Improved and Worsened Policies, Leading to Sub-Optimal Behavior. Two Causes for This that Have Been Traced to the Crucial Policy Improvement Step Are: (I) the Inaccuracies in the Policy Improvement Function and (Ii) the Exploration/exploitation Tradeoff Integral to This Step, Which Generates Variability in Performance. Both of These Defects Are Amplified by Simulation Noise. Deep RL Belongs to a Newer Class of Algorithms in Which the Resolution of the Learning Process is Refined Via Mechanisms Such as Experience Replay And/or Deep Neural Networks for Improved Performance. in This Paper, a New Deep Learning Approach is Developed for API Which Employs a More Accurate Policy Improvement Function, Via an Enhanced Resolution Bellman Equation, Thereby Reducing Chattering and Eliminating the Need for Exploration in the Policy Improvement Step. Versions of the New Algorithm for Both the Long-Run Discounted MDP and Semi-MDP Are Presented. Convergence Properties of the New Algorithm Are Studied Mathematically, and a Post-Earthquake Disaster Response Case Study is Employed to Demonstrate Numerically the Algorithm\u27s Efficacy

A Spike-shaped Anchorage For Steel Reinforced Polymer (SRP)-strengthened Concrete Structures

Steel reinforced polymer (SRP) composite has recently emerged as an effective and economical solution for strengthening of reinforced concrete (RC) structures. Premature debonding failure of unanchored SRP at low load levels generally governs the performance of RC structures strengthened with externally bonded SRP. Therefore, a novel yet simple spike-shaped anchorage system was proposed in this study to prevent the debonding failure of SRP and to improve the interfacial shear capacity. Experimental investigation through single-lap shear tests of SRP-concrete joints showed that the anchorage system changed the failure mode from composite debonding to fiber rupture. In addition, the anchorage system substantially increased the peak load and reduced the interfacial slippage of the SRP-concrete joint compared to the unanchored condition. A numerical procedure based on the finite difference method was developed to predict the full-range load response, and results matched well with the full-range experimental responses of anchored and unanchored specimens. Parametric study of the test results and numerical simulation based on finite difference method both showed that the fiber rupture failure mode could be achieved for anchors in various positions along the bonded length. The closer the anchor is to the loaded end, the less global slip was obtained when the load reached the peak value

The activities of rural young people in Missouri : a survey of 2,297 young people attending high school

Publication authorized October 21, 1937.Digitized 2007 AES.Includes bibliographical references (pages 67-68)

Cost of Care Conversations: Perspectives from Rural Health Care Providers and Older Adult Patients

Older adults often struggle with health care costs. Cost of care (CoC) conversations are conversations between health care providers and patients to discuss direct and indirect costs associated with health care. These conversations have been found to increase patient compliance, but patients and health care providers often do not have these discussions. This article describes a project to provide Extension education to encourage CoC conversations for older adults and health care providers in rural counties in a southern state. To inform educational material development, 125 older adults and 51 health care providers completed surveys about their cost-related barriers to health care, attitudes and frequency of CoC conversations, and preferred educational methods. Older adults reported that they were most comfortable discussing health care costs with physicians and pharmacists but that health care providers rarely initiated these conversations. Health care providers indicated that they were comfortable talking about health care costs with patients and reported that they often initiate these conversations. Both older adults and health care providers indicated fact sheets as a top educational method. This project demonstrates how Extension educators can partner with health care providers to educate older adults about communicating cost-related challenges and needs

Rural women and the works progress program : a partial analysis of levels of living

Publication authorized April 15, 1937."The Agricultural Experiment Station, University of Missouri and the Rural Research Section, Division of Social Research, the Federal Works Progress Administration cooperating.