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On using ethical principles of community-engaged research in translational science.

ABSTRACT: The transfer of new discoveries into both clinical practice and the wider community calls for reliance on interdisciplinary translational teams that include researchers with different areas of expertise, representatives of health care systems and community organizations, and patients. Engaging new stakeholders in research, however, calls for a reconsideration or expansion of the meaning of ethics in translational research. We explored expert opinion on the applicability of ethical principles commonly practiced in community-engaged research (CEnR) to translational research. To do so, we conducted 2 online, modified-Delphi panels with 63 expert stakeholders who iteratively rated and discussed 9 ethical principles commonly used in CEnR in terms of their importance and feasibility for use in translational research. The RAND/UCLA appropriateness method was used to analyze the data and determine agreement and disagreement among participating experts. Both panels agreed that ethical translational research should be "grounded in trust." Although the academic panel endorsed "culturally appropriate" and "forthcoming with community about study risks and benefits," the mixed academic-community panel endorsed "scientifically valid" and "ready to involve community in interpretation and dissemination" as important and feasible principles of ethical translational research. These findings suggest that in addition to protecting human subjects, contemporary translational science models need to account for the interests of, and owe ethical obligations to, members of the investigative team and the community at large.

SUBMITTER: Khodyakov D

PROVIDER: S-EPMC4833614 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

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Project description:PurposeThis qualitative study examined fiscal and administrative (i.e., pre- and post-award grants process) barriers and facilitators to community-engaged research among stakeholders across 4 Clinical and Translational Science Awards (CTSA) institutions.MethodA purposive sample of 24 key informants from 3 stakeholder groups-community partners, academic researchers, and research administrators-from the CTSA institutions at the University of North Carolina at Chapel Hill, Medical University of South Carolina, Vanderbilt University Medical Center, and Yale University participated. Semistructured interviews were conducted in March-July 2018, including questions about perceived challenges and best practices in fiscal and administrative processes in community-engaged research. Transcribed interviews were independently reviewed and analyzed using the Rapid Assessment Process to facilitate key theme and quote identification.ResultsCommunity partners were predominantly Black, academic researchers and research administrators were predominantly White, and women made up two-thirds of the overall sample. Five key themes were identified: level of partnership equity, partnership collaboration and communication, institutional policies and procedures, level of familiarity with varying fiscal and administrative processes, and financial management expectations. No stakeholders reported best practices for the institutional policies and procedures theme. Cross-cutting challenges included communication gaps between stakeholder groups; lack of or limits in supporting community partners' fiscal capacity; and lack of collective awareness of each stakeholder group's processes, procedures, and needs. Cross-cutting best practices centered on shared decision making and early and timely communication between all stakeholder groups in both pre- and post-award processes.ConclusionsFindings highlight the importance of equitable processes, triangulated communication, transparency, and recognizing and respecting different financial management cultures within community-engaged research. This work can be a springboard used by CTSA institutions to build on available resources that facilitate co-learning and discussions between community partners, academic researchers, and research administrators on fiscal readiness and administrative processes for improved community-engaged research partnerships.

Project description:BackgroundCommunity Advisory Boards (CABs) have been frequently used to engage diverse partners to inform research projects. Yet, evaluating the quality of engagement has not been routine. We describe a multi-method ethnographic approach documenting and assessing partner engagement in two "virtual" CABs, for which we conducted all meetings remotely.MethodsTwo research projects for increasing equitable COVID-19 testing, vaccination, and clinical trial participation for underserved communities involved remote CAB meetings. Thirty-three partners representing 17 community groups participated in 15 sessions across the two CABs facilitated by a social change organization. We developed ethnographic documentation forms to assess multiple aspects of CAB member engagement (e.g., time spent speaking, modality used, types of interactions). Documenters were trained to observe CAB sub-groups via virtual sessions. Debriefing with the documentation team after CAB meetings supported quality assurance and process refinement. CAB members completed a brief validated survey after each meeting to assess the quality and frequency of engagement. Content and rapid thematic analysis were used to analyze documentation data. Quantitative data were summarized as frequencies and means. Qualitative and quantitative findings were triangulated.ResultsA total of 4,540 interactions were identified across 15 meetings. The most frequent interaction was providing information (44%), followed by responding (37-38%). The quality and frequency of stakeholder engagement were rated favorably (average 4.7 of 5). Most CAB members (96%) reported good/excellent engagement. Specific comments included appreciation for the diversity of perspectives represented by the CAB members and suggestions for improved live interpretation. Debriefing sessions led to several methodological refinements for the documentation process and forms.ConclusionWe highlight key strategies for documenting and assessing community engagement. Our methods allowed for rich ethnographic data collection that refined our work with community partners. We recommend ongoing trainings, including debriefing sessions and routinely reviewed assessment of data to strengthen meaningful community engagement.

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.

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On using ethical principles of community-engaged research in translational science.

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