ArticleJournal of clinical and translational science2022
Digital and virtual strategies to advance community stakeholder engagement in research during COVID-19 pandemic.
Article in Journal of clinical and translational science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Coproducing a conceptual understanding of unmet palliative care needs: stakeholder workshops using modified nominal group technique.BMC palliative care · 2025Article
- Researcher and community partner perspectives on community-engaged research during the COVID-19 pandemic.Journal of clinical and translational science · 2025Article
- Exploring the Role of Technology in the Implementation of Social Accountability in Medical Education: A Brief Review.Journal of pharmacy & bioallied sciences · 2024Review
- Community-driven partnerships with Community-Engaged Research teams bring resources and reliable information to Baltimore residents.Journal of clinical and translational science · 2024Article
- Predictors of willingness to participate in COVID-19 clinical trials among Black and Latino adults.Journal of clinical and translational science · 2024Article
- Developing adaptive capacity and preparedness in clinical and translational science.Journal of clinical and translational science · 2023Article
- Integrating community voices in the research continuum: Perspectives on a consultation service.Journal of clinical and translational science · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
Despite the adversity presented by COVID-19 pandemic, it also pushed for experimenting with innovative strategies for community engagement. The Community Research Advisory Council (C-RAC) at Johns Hopkins University (JHU), is an initiative to promote community engagement in research. COVID-19 rendered it impossible for C-RAC to conduct its meetings all of which have historically been in person. We describe the experience of advancing the work of the C-RAC during COVID-19 using digital and virtual strategies. Since March 2020, C-RAC transitioned from in person to virtual meetings. The needs assessment was conducted among C-RAC members, and individualized solutions provided for a successful virtual engagement. The usual working schedule was altered to respond to COVID-19 and promote community engaged research. Attendance to C-RAC meetings before and after the transition to virtual operation increased from 69% to 76% among C-RAC members from the community. In addition, the C-RAC launched new initiatives and in eighteen months since January 2020, it conducted 50 highly rated research reviews for 20 research teams. The experience of the C-RAC demonstrates that when community needs are assessed and addressed, and technical support is provided, digital strategies can lead to greater community collaborations.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.