Equity-informed strategies to promote COVID-19 vaccination uptake undertaken by public health units in Ontario, Canada
Bibliographic record
Abstract
This paper examined local public health strategies to promote COVID-19 vaccination equity in Ontario, Canada between 2020 and 2023. Our descriptive mixed-methods study gathered data from local public health units (PHUs) in Ontario on public health's COVID-19 equity-driven vaccination efforts. PHUs are responsible for delivering local public health services in the province and are provincially mandated to address health equity in their geographic boundaries. Data was collected through a seven-item questionnaire. Open-ended questions were analyzed through an inductive content analysis. From all 34 PHUs, 25 agreed to participate. Eighty-percent of the participating PHUs reported provision of information, community engagement, and improved physical accessibility to COVID-19 vaccines were high/very high priorities. Only 39.1% of PHUs indicated social determinants of health-related data collection as a high/very high priority. Overall, 90% of PHUs reported prioritizing groups, including those with lower socioeconomic status, Indigenous populations, and people experiencing homelessness or precarious housing, in their vaccination response. Four emergent themes included: (1) defining priority populations with data-driven and health equity-oriented processes; (2) promoting vaccination through health equity-informed strategies; (3) prioritizing Indigenous health and sovereignty in vaccination efforts; and (4) recommendations to address health equity-related barriers to COVID-19 vaccination. Examples of recommendations included: adopting culturally-sensitive and context-relevant communication strategies; enabling data linkage of data sources; and providing guidance on the development of intersectoral collaborations. Health equity-informed efforts in COVID-19 vaccination decision-making and actions were broadly adopted to better respond to local needs. Recommendations made by PHUs to address the gaps hindering COVID-19 vaccine equity promotion should be considered before future health crises. Supplementary Information: The online version contains supplementary material available at 10.1186/s12982-025-01317-8.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".