Results Summary
What was the research about?
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.
What were the results?
Using AI, the research team identified themes that helped people succeed when facing each health problem. For example, it’s important for people with mental health needs to have someone who advocates for them to get care. Using BCT, each community came up with key messages about how to address their problem. They shared those messages in posters, brochures, and materials for doctors to use.
The team found five things that can help research teams use AI and BCT together:
- Choose topics that are important to the community and that are likely to have success stories.
- When gathering stories, focus on what worked for each person.
- Allow enough time—several months—to study all the stories.
- Present results from AI along with other evidence when bringing community members together for BCT.
- Make sure the research team includes someone with training in how to analyze stories from AI and make them useful for BCT.
Who was in the study?
The research team gathered stories from 102 community members and healthcare workers in Colorado for the AI process. In total, 63 community members took part in the five BCT groups. Communities included rural areas and Denver neighborhoods with few health resources.
What did the research team do?
The research team worked with local health research networks and community groups in Colorado to identify five projects. Two focused on access to mental health support. One was about how to set up a type of primary care called patient-centered medical homes. One was on managing chronic pain, and one was on sleep apnea.
For each project, the research team gathered stories using AI. They studied the stories to find common solutions to each problem. Then the team shared those solutions in the BCT group with other people from the community. Each BCT group came up with ways to share those solutions as messages that would make sense in their communities.
During the project, the research team took notes and had meetings about how each part of the process was working. They compiled their results into recommendations to help other research teams use AI and BCT to help other communities.
What were the limits of the study?
The team only tested the AI and BCT combination in five communities. This combination may work differently in other places.
How can people use the results?
Community leaders and health researchers can use results of this study to develop local solutions for health problems.
Professional Abstract
Objective
To pilot test a combined Appreciative Inquiry (AI) and Boot Camp Translation (BCT) approach to identify and translate local experience of success with various health issues into messages, calls to action, and locally relevant materials
Study Design
Design Elements | Description |
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Design | Observational, qualitative |
Data Sources and Data Sets | 5 pilot AI/BCT projects in rural and urban communities addressing mental health (2 sites), chronic pain, sleep apnea, and implementation of a patient-centered medical home |
Analytic Approach |
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Outcomes |
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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.
Results
Researchers identified themes from the AI interviews, such as the importance of having a safe venue for mental health care and the use of multiple strategies to manage and prevent pain. The BCT groups used these themes to develop community-specific messages. Groups also developed posters, materials for primary care providers, brochures, and a community program that trains lay people in mental health support to disseminate messaging.
Researchers identified five essential strategies from the combined AI/BCT approach:
- Choose topics that are important to the community and that are likely to have success stories although lacking significant evidence.
- Focus on what worked for each person in AI interviews.
- Allow enough time—several months—to analyze AI interviews.
- Present results from AI along with research evidence when convening community members for BCT.
- Include a qualitative analyst with training in AI and BCT on the research team.
Limitations
Researchers tested the AI/BCT approach on five topics in one state. Further testing might identify additional components for success or limitations of the AI/BCT approach.
Conclusions and Relevance
All AI/BCT projects elicited local solutions to healthcare problems, generated locally relevant messaging, and created dissemination strategies based on these solutions.
Future Research Needs
Future research could test whether the AI/BCT combined approach can be applied in other communities and for different topics.
Final Research Report
View this project's final research report.
Journal Citations
Related Journal Citations
Peer-Review Summary
Peer review of PCORI-funded research helps make sure the report presents complete, balanced, and useful information about the research. It also assesses how the project addressed PCORI’s Methodology Standards. During peer review, experts read a draft report of the research and provide comments about the report. These experts may include a scientist focused on the research topic, a specialist in research methods, a patient or caregiver, and a healthcare professional. These reviewers cannot have conflicts of interest with the study.
The peer reviewers point out where the draft report may need revision. For example, they may suggest ways to improve descriptions of the conduct of the study or to clarify the connection between results and conclusions. Sometimes, awardees revise their draft reports twice or more to address all of the reviewers’ comments.
Peer reviewers commented, and the researchers made changes or provided responses.
Overall, the reviewers found the report to be creative and innovative. The reviewers also accepted the researcher’s reasoning and conclusions in the report. Criticisms were primarily requests to expand or clarify the writing in sections throughout the report.
Conflict of Interest Disclosures
Project Information
Key Dates
Study Registration Information
^John Westfall, MD was the original principal investigator for this project.