Dataset Information

Aligning Community-Engaged Research to Context.

ABSTRACT: Community-engaged research is understood as existing on a continuum from less to more community engagement, defined by participation and decision-making authority. It has been widely assumed that more is better than less engagement. However, we argue that what makes for good community engagement is not simply the extent but the fit or alignment between the intended approach and the various contexts shaping the research projects. This article draws on case studies from three Community Engagement Cores (CECs) of NIEHS-funded Environmental Health Science Core Centers (Harvard University, UC Davis and University of Arizona,) to illustrate the ways in which community engagement approaches have been fit to different contexts and the successes and challenges experienced in each case. We analyze the processes through which the CECs work with researchers and community leaders to develop place-based community engagement approaches and find that different strategies are called for to fit distinct contexts. We find that alignment of the scale and scope of the environmental health issue and related research project, the capacities and resources of the researchers and community leaders, and the influences of the sociopolitical environment are critical for understanding and designing effective and equitable engagement approaches. These cases demonstrate that the types and degrees of alignment in community-engaged research projects are dynamic and evolve over time. Based on this analysis, we recommend that CBPR scholars and practitioners select a range of project planning and management techniques for designing and implementing their collaborative research approaches and both expect and allow for the dynamic and changing nature of alignment.

SUBMITTER: London JK

PROVIDER: S-EPMC7068394 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Aligning Community-Engaged Research to Context.

London Jonathan K JK Haapanen Krista A KA Backus Ann A Mack Savannah M SM Lindsey Marti M Andrade Karen K

International journal of environmental research and public health 20200213 4

Community-engaged research is understood as existing on a continuum from less to more community engagement, defined by participation and decision-making authority. It has been widely assumed that more is better than less engagement. However, we argue that what makes for good community engagement is not simply the extent but the fit or alignment between the intended approach and the various contexts shaping the research projects. This article draws on case studies from three Community Engagement ...[more]

PMID: 32069817

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Project description:The degree to which artificial intelligence healthcare research is informed by data and stakeholders from community settings has not been previously described. As communities are the principal location of healthcare delivery, engaging them could represent an important opportunity to improve scientific quality. This scoping review systematically maps what is known and unknown about community-engaged artificial intelligence research and identifies opportunities to optimize the generalizability of these applications through involvement of community stakeholders and data throughout model development, validation, and implementation. Embase, PubMed, and MEDLINE databases were searched for articles describing artificial intelligence or machine learning healthcare applications with community involvement in model development, validation, or implementation. Model architecture and performance, the nature of community engagement, and barriers or facilitators to community engagement were reported according to PRISMA extension for Scoping Reviews guidelines. Of approximately 10,880 articles describing artificial intelligence healthcare applications, 21 (0.2%) described community involvement. All articles derived data from community settings, most commonly by leveraging existing datasets and sources that included community subjects, and often bolstered by internet-based data acquisition and subject recruitment. Only one article described inclusion of community stakeholders in designing an application-a natural language processing model that detected cases of likely child abuse with 90% accuracy using harmonized electronic health record notes from both hospital and community practice settings. The primary barrier to including community-derived data was small sample sizes, which may have affected 11 of the 21 studies (53%), introducing substantial risk for overfitting that threatens generalizability. Community engagement in artificial intelligence healthcare application development, validation, or implementation is rare. As healthcare delivery occurs primarily in community settings, investigators should consider engaging community stakeholders in user-centered design, usability, and clinical implementation studies to optimize generalizability.

Project description:Policy Points Community-engaged research (CEnR) engenders meaningful academic-community partnerships to improve research quality and health outcomes. CEnR has increasingly been adopted by health care systems, funders, and communities looking for solutions to intractable problems. It has been difficult to systematically measure CEnR's impact, as most evaluations focus on project-specific outcomes. Similarly, partners have struggled with identifying appropriate measures to assess outcomes of interest. To make a case for CEnR's value, we must demonstrate the impacts of CEnR over time. We compiled recent measures and developed an interactive data visualization to facilitate more consistent measurement of CEnR's theoretical domains.ContextCommunity-engaged research (CEnR) aims to engender meaningful academic-community partnerships to increase research quality and impact, improve individual and community health, and build capacity for uptake of evidence-based practices. Given the urgency to solve society's pressing public health problems and increasing competition for funding, it is important to demonstrate CEnR's value. Most evaluations focus on project-specific outcomes, making it difficult to demonstrate CEnR's broader impact. Moreover, it is challenging for partnerships to identify assessments of interest beyond process measures. We conducted a mapping review to help partnerships find and select measures to evaluate CEnR projects and to characterize areas where further development of measures is needed.MethodsWe searched electronic bibliographic databases using relevant search terms from 2009 to 2018 and scanned CEnR projects to identify unpublished measures. Through review and reduction, we found 69 measures of CEnR's context, process, or outcomes that are potentially generalizable beyond a specific health condition or population. We abstracted data from descriptions of each measure to catalog purpose, aim (context, process, or outcome), and specific domains being measured.FindingsWe identified 28 measures of the conditions under which CEnR is conducted and factors to support effective academic-community collaboration (context); 43 measures evaluating constructs such as group dynamics and trust (process); and 43 measures of impacts such as benefits and challenges of CEnR participation and system and capacity changes (outcomes).ConclusionsWe found substantial variation in how academic-community partnerships conceptualize and define even similar domains. Achieving more consistency in how partnerships evaluate key constructs could reduce measurement confusion apparent in the literature. A hybrid approach whereby partnerships discuss common metrics and develop locally important measures can address CEnR's multiple goals. Our accessible data visualization serves as a convenient resource to support partnerships' evaluation goals and may help to build the evidence base for CEnR through the use of common measures across studies.

Dataset Information

Aligning Community-Engaged Research to Context.

Publications

Aligning Community-Engaged Research to Context.

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