The role of trust in engaging community-based task forces and agencies among minoritized communities during a public health emergency
Bibliographic record
Abstract
OBJECTIVES: To investigate how task forces, networks, and community agencies engaged with faith-based, and ethnoracial communities to improve vaccine confidence and uptake of COVID-19 vaccines, and to understand the perceived enablers and barriers to the implementation of vaccine confidence and uptake in the Peel Region and Toronto, Ontario. METHODS: Between June 2023 and March 2024, we conducted ten online focus groups with three task forces and six community agencies. We conducted four interviews with representatives from two task forces and one network. We used thematic analysis to explore respondents' perceptions and experiences. RESULTS: The data revealed that trust operated at interpersonal and organizational levels, which are mutually reinforcing. At the interpersonal level, members of the task forces, network, and ambassadors from community agencies drew on relationships with members of minoritized communities by addressing community concerns on their terms and using in-person, online, regular contact, and active listening approaches. At the organizational level, trust was facilitated through conducting outreach (i.e., vaccine promotion) at trusted and familiar locations (e.g., faith-based organizations). COVID-related information was better received from community representatives who were already known and trusted among community members. Common outreach strategies included door-to-door outreach; informational videos and sessions; mass awareness-raising campaigns; townhalls; and ethnic media and social media. CONCLUSION: Community leaders play an instrumental role in establishing and sustaining trust in vaccine promotion among community members. Trust established among community leaders and ambassadors enabled vaccine promotion efforts among minoritized communities. These findings may help to further strengthen community engagement for future public health emergency responses.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".