DOI: 10.3886/icpsr39478.v1
title: Creating Locally Relevant Health Solutions with the Appreciative Inquiry and Boot Camp Translation Method [Methods Study], Colorado, 2013-2018
title: Version 1

description: To address health problems in communities, healthcare providers usually look at evidence from studies about what has worked elsewhere. But sometimes these solutions don't fit local needs. A process called Boot Camp Translation, or BCT, brings community members and researchers together to look at research evidence and decide how to use it locally. BCT groups turn evidence into messages that make sense in their communities.
When no evidence exists, one way to find answers is to look for local stories of people who have overcome problems. These stories can provide ideas that other people can use to solve similar problems. Appreciative Inquiry, or AI, is a way to collect stories about how to overcome problems.
In this study, the research team combined AI and BCT in five communities around Colorado to find and share local solutions to health problems. Each community worked on a different health problem, such as getting mental health care or managing pain. The team wanted to identify lessons that other research teams can use.
description: Conduct 6 projects with underserved rural and urban Colorado communities using the Appreciative Inquiry/Boot Camp Translation (AI/BCT) method to select priority health topics, identify factors that facilitate successful health outcomes related to the topic, and translate local evidence-based recommendations into local solutions. The study aims were the following:

Identify and describe the components of the AI/BCT method essential to engaging patients and community members in patient-centered research.
Produce a training program for patients, health care professionals, and academic researchers to disseminate AI/BCT to improve patient engagement for patient-centered outcomes
description: This project combined two methodologies to generate locally tailored solutions to community health challenges and to translate those solutions into actionable health messages. AI solicits success stories from individuals who have overcome challenges to identify elements of success that others can replicate. In BCT, community members and researchers work together to create locally relevant messaging and dissemination strategies based on available evidence about solutions. Researchers piloted a combined approach in five projects in rural and urban communities in Colorado, using results from AI interviews when evidence from national sources was lacking. They evaluated the results of each project to identify essential components of the AI/BCT approach.
Researchers and practice-based research networks and community-based organizations worked together to identify topics for investigation, which were access to mental health support in urban and rural settings, chronic pain management, sleep apnea diagnosis and treatment, and implementation of a patient-centered medical home.
Researchers conducted and analyzed AI interviews with 102 community members and health professionals across the five topics to identify actionable themes.
The five BCT groups included 63 community members who were residents of rural and underserved communities. Each BCT group met 3 to 10 times over a period of five to nine months. After learning about the themes identified in AI and other relevant evidence, each BCT group created messages and dissemination strategies relevant to its community.
Researchers took extensive field notes on the AI/BCT approach and met monthly to identify strategies that led to collecting more detailed AI information or that facilitated message development in BCT.
description: Datasets: 
DS1: Creating Locally Relevant Health Solutions with the Appreciative Inquiry and Boot Camp Translation Method [Methods Study], Colorado, 2013-2018