Community Mobilization to Promote Vaccine Confidence During a Global Public Health Emergency: Insights from Peel Region and Toronto (Ontario, Canada) a Qualitative Study
Bibliographic record
Abstract
Background: The Ontario government launched the High Priority Communities Strategy (HPCS) in December 2020, funding community agencies operating in neighborhoods disproportionately affected by COVID-19 in Durham, Peel, Toronto, York, and Ottawa. Community-led task forces and networks also formed with the aim to increase vaccine confidence and uptake among minoritized communities. Objectives: To explore how community-led task forces, networks and agencies mobilized and engaged faith-based and ethno-racial communities in Peel Region and Toronto to improve vaccine confidence and uptake, including perceived facilitators and barriers. Design: Multi-method qualitative study. Methods: Between June 2023 and March 2024, we conducted ten online focus groups with three task forces and six HPCS-funded community agencies, as well as four key-informant interviews with representatives from two task forces and one network. We used thematic analysis to explore respondents' perceptions and experiences. Results: Three key themes emerged. First, community-led task forces, the network and agencies used community mobilization strategies, such as tailored outreach, mitigating vaccine access barriers and leveraging trusted community voices, to improve vaccine confidence and uptake. Second, fostering a sense of community was central to their work, enabled through member engagement and power (knowledge and resource) sharing for collective impact. Third, sustaining community-led efforts was a challenge. The volunteer-driven task forces and network lacked the capacity to formally evaluate their activities or long-term infrastructure, and most disbanded post-pandemic. However, community agencies pivoted to preventative and primary care initiatives under HPCS. Conclusion: Community-led structures contributed to promoting vaccine uptake among ethno-racial and faith-based communities in hotspot areas. Facilitators included the use of trusted messengers and power sharing, while barriers included short-term funding and challenges sustaining efforts over time. Long-term sustainability of these efforts requires continued investment, sustained infrastructure, and strong community partnerships. Lessons from these findings can help strengthen community-led responses to future public health emergencies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".