How have Ontario Public Health units engaged with faith-based organizations to build confidence in COVID-19 vaccines among ethno-racial communities
Bibliographic record
Abstract
In Ontario, collaborations between Public Health Units (PHUs) and faith-based organizations (FBOs) and other community organizations were implemented to deliver interventions aimed at building trust in vaccines among ethnoracial communities. This research sought to explore the processes of PHU engagement with FBOs, and challenges encountered. A qualitative research study based on in-depth interviews was conducted with 18 of the 34 Ontario PHUs who expressed an interest. Braun and Clarke's "experiential" approach was used to explore the realities of PHUs' contextual experiences and perspectives. PHUs developed a two-phased process for engaging with FBOs and ethnoracial communities. First, PHUs created internal frameworks for dialogue to use available data to better understand the diverse needs of these equity-seeking groups. The second phase involved a three-stage engagement process:1) Consultation and information sharing was employed to facilitate early and open dialogue. 2) Work with FBOs and interested communities to plan vaccine deployment strategies to meet the needs of different faith and ethno-racial groups, and jointly plan the implementation of vaccination clinics. 3) Share roles and responsibilities with FBOs to roll out vaccine confidence strategies. The PHUs' openness to honest dialogue with FBOs, commitment to building relationships based on respect for different beliefs and opinions about vaccines, and previous experience working together all facilitated engagement. Lessons learned from this research can guide the implementation of future vaccination programs. Ensuring early and regular engagement with FBOs a priority strategy and devoting substantial resources (human, financial and duration) are both necessary to improve vaccine confidence and promote equity for ethno-racial groups.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".