Perceived Effectiveness of Public Health Unit Partnerships With Faith-Based and Other Community-Based Organizations to Promote COVID-19 Vaccination Among Ethnoracial Communities
Bibliographic record
Abstract
Objectives: The objective of this study was to explore how Ontario Public Health Units (PHUs) partnered with faith-based organizations (FBOs) and other community-based organizations (CBOs) to promote COVID-19 vaccination among ethnoracial groups made structurally vulnerable during the pandemic, and to understand how PHUs perceive the effectiveness of these partnerships with these organizations. Methods: Between June to December 2022, we distributed a cross-sectional survey to 34 PHUs in Ontario to explore how PHUs were engaging and partnering with FBOs and CBOs. Results: Responses were received from 28 of 34 (82.5%) public health units. Across Ontario, 23 (82.1%) respondent PHUs worked with FBOs during the COVID-19 vaccine rollout with activities ranging from informing FBOs of vaccine availability, to using places of worship as sites for vaccine administration and co-creating educational materials on immunization that were faith- and culturally sensitive. Conclusion: FBOs can be a valuable community partner as PHUs work to increase the reach and uptake of public health interventions. Ongoing monitoring and evaluation of the impact of FBO engagement on vaccine confidence and uptake among ethnoracial communities is needed to inform future community engaged vaccine programming in Ontario.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".