Towards a learning healthcare community in the Bronx: evaluating the Bridging Research, Accurate Information and Dialogue (BRAID) model DOI Creative Commons
Sarah McNeilly, Katherine Wang,

Sarah A. Jacobs

et al.

Health Research Policy and Systems, Journal Year: 2025, Volume and Issue: 23(1)

Published: Feb. 19, 2025

Learning healthcare communities (LHCCs) have been proposed as a next-generation evolution of learning health systems that can advance equity; however, practical mechanism for enabling the active and continuous community engagement required one has not yet described in literature. Recognizing community-based participatory research (CBPR) could potentially meet this need, team at Montefiore Medical Center/Albert Einstein College Medicine designed novel evidence-based CBPR model – Bridging Research, Accurate Information Dialogue (BRAID) initiates meaningful, longitudinal dialogues to foster bidirectional trust between researchers, clinicians, scientists communities. A mixed-methods cohort study two BRAID cohorts was conducted 2022 2023. Eligible participants were recruited from Bronx, New York, United States convened series conversation circles. Multimodal data collected all participants, including quantitative pre- post-series surveys same-day circle feedback forms. Surveys administered using SurveyMonkey descriptive statistics completed Excel SPSS. total 42 enrolled, most whom people colour who had participated before. Among them, 40 provided least response form, which reflected consistently positive experiences with BRAID. This consistent evidence survey, every 36 respondents stated they would either definitely (83.3%, N = 30/36) or probably (16.7%, 6/36) recommend participation someone like them. Of note, 91.7% (N 33/36) already disseminated information learned through downstream 84.4% 27/32) indicated strengthened their science research, highlighting unique distinguishing features model. Our suggests is effective, efficient scalable, experiential supporting it reproducible. These factors suggest implementation facilitate rapid, sharing builds organizations laid groundwork an LHCC potential be adopted by elsewhere.

Language: Английский

Towards a learning healthcare community in the Bronx: evaluating the Bridging Research, Accurate Information and Dialogue (BRAID) model DOI Creative Commons
Sarah McNeilly, Katherine Wang,

Sarah A. Jacobs

et al.

Health Research Policy and Systems, Journal Year: 2025, Volume and Issue: 23(1)

Published: Feb. 19, 2025

Learning healthcare communities (LHCCs) have been proposed as a next-generation evolution of learning health systems that can advance equity; however, practical mechanism for enabling the active and continuous community engagement required one has not yet described in literature. Recognizing community-based participatory research (CBPR) could potentially meet this need, team at Montefiore Medical Center/Albert Einstein College Medicine designed novel evidence-based CBPR model – Bridging Research, Accurate Information Dialogue (BRAID) initiates meaningful, longitudinal dialogues to foster bidirectional trust between researchers, clinicians, scientists communities. A mixed-methods cohort study two BRAID cohorts was conducted 2022 2023. Eligible participants were recruited from Bronx, New York, United States convened series conversation circles. Multimodal data collected all participants, including quantitative pre- post-series surveys same-day circle feedback forms. Surveys administered using SurveyMonkey descriptive statistics completed Excel SPSS. total 42 enrolled, most whom people colour who had participated before. Among them, 40 provided least response form, which reflected consistently positive experiences with BRAID. This consistent evidence survey, every 36 respondents stated they would either definitely (83.3%, N = 30/36) or probably (16.7%, 6/36) recommend participation someone like them. Of note, 91.7% (N 33/36) already disseminated information learned through downstream 84.4% 27/32) indicated strengthened their science research, highlighting unique distinguishing features model. Our suggests is effective, efficient scalable, experiential supporting it reproducible. These factors suggest implementation facilitate rapid, sharing builds organizations laid groundwork an LHCC potential be adopted by elsewhere.

Language: Английский

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