Dataset Information

CIRTification: Training in Human Research Protections for Community-Engaged Research Partners.

ABSTRACT: Human research ethics training should provide relevant, meaningful information and build skills. Compliance should not be the only goal; training should also enhance knowledge, skills, and capacity. However, most currently available human research ethics training programs are geared toward learners who already have some research experience and working knowledge of research methods (e.g., graduate students, junior researchers); many community partners, however, have little or no prior exposure to research. More important, standard training programs do not adequately address the unique context of community-engaged research (CEnR).This article describes the development process, final curricular materials, and suggestions for successful implementation of CIRTification, a human research ethics training program designed specifically for community research partners who will be working on the "frontlines" of research.Development of CIRTification involved an extensive literature review, consultation with stakeholders including community partners, academic researchers, and human research protection program personnel.The curriculum, as well as information and materials to help potential users promote acceptance of the curriculum by their local institutional review boards (IRBs), are freely available online at www.go.uic.edu/CIRT. Ideally, community research partners who complete CIRTification will not only learn about the importance of protecting research participants but also be empowered to substantially contribute to the ethical practices of their respective research collaborations.

SUBMITTER: Anderson EE

PROVIDER: S-EPMC4711932 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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CIRTification: Training in Human Research Protections for Community-Engaged Research Partners.

Anderson Emily E EE

Progress in community health partnerships : research, education, and action 20150101 2

<h4>Background</h4>Human research ethics training should provide relevant, meaningful information and build skills. Compliance should not be the only goal; training should also enhance knowledge, skills, and capacity. However, most currently available human research ethics training programs are geared toward learners who already have some research experience and working knowledge of research methods (e.g., graduate students, junior researchers); many community partners, however, have little or n ...[more]

PMID: 26412769

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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

CIRTification: Training in Human Research Protections for Community-Engaged Research Partners.

Publications

CIRTification: Training in Human Research Protections for Community-Engaged Research Partners.

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