Making the Random the Usual: Appreciative Inquiry/Boot Camp Translation—Developing Community-Oriented Evidence That Matters. (January 2020) - Nease, Donald E; Simpson, Matthew J.; Zittleman, Linda; Holtrop, Jodi Summers; Hall, Tristen L.; Fisher, Mary; Felzien, Maret; Westfall, John M.
Background: The evidence underlying clinical guidelines arising from typical scientific inquiry may not always match the needs and concerns of local communities. Our High Plains Research Network Community Advisory Council (HPRN CAC) identified a need for evidence regarding how to assist members of their community suffering from mental health issues to recognize their need for help and then obtain access to mental health care. The lack of evidence led our academic team to pursue linking Appreciative Inquiry with Boot Camp Translation (AI/BCT). This article describes the development and testing of this linked method.Method: We worked with the HPRN CAC and other communities affiliated with the State Networks of Colorado Ambulatory Practices and Partners (SNOCAP) practice-based research networks to identify 5 topics for testing of AI/BCT. For each topic, we developed AI interview recruitment strategies and guides with our community partners, conducted interviews, and analyzed the interview data. Resulting themes for each topic were then utilized by 5 groups with the BCT method to develop community relevant messages and materials to communicate the evidence generated in each AI set of interviews. At each stage for each topic, notes on adaptations, barriers, and successes were recorded by the project team.Results: Each topic successfully led to generation of community specific evidence, messages, and materials for dissemination using the AI/BCT method. Beyond this, 5 important lessons emerged regarding the AI/BCT method: Researchers must (1) first ensure whether the topic is a good fit for AI, (2) maintain a focus on "what works" throughout all stages, (3) recruit one or more experienced qualitative analysts, (4) ensure adequate time and resources for the extensive AI/BCT process, and (5) present AI findings to BCT participants in the context of existing evidence and the local community and allow time for community partners to ask questions and request additional data analyses to be done.Conclusions: AI/BCT represents an effective way of responding to a community's need for evidence around a specific topic where standard evidence and/or guidelines do not exist. AI/BCT is a method for turning the "random" successes of individuals into "usual" practice at a community level.