Local Public Health Unit Priorities, Actions, and Barriers in Addressing Inequities During the COVID-19 Pandemic in Ontario, Canada
Bibliographic record
Abstract
CONTEXT: The COVID-19 pandemic required a major response from local public health to reduce infections and manage inequities in COVID-19 outcomes. It is important to consider lessons learned from the COVID-19 pandemic response across local public health in order to prepare for future public health emergencies. OBJECTIVE: This study examined how local public health units (PHUs) in Ontario, Canada, addressed inequities in COVID-19 outcomes in their pandemic response. DESIGN: We contacted all 34 Ontario PHUs to participate in a survey on priorities, actions, and barriers to address health inequities in their COVID-19 response and conducted inductive content analysis on responses to identify themes. SETTING: Ontario, Canada. PARTICIPANTS: A total of 25 out of 34 local Ontario PHUs completed the survey. MAIN OUTCOME MEASURE: Public health unit-reported priorities and actions in addressing health inequities in their COVID-19 response. RESULTS: PHUs reported varied priorities in addressing health inequities. PHUs played a key role in coordinating an equity-focused response, including data analysis and reporting, engaging with community groups, and making cross-sector connections. However, important barriers remained, including a lack of community trust, frequent changes in guidance, lack of access to data on the social determinants of health, and a shortage of staffing and resources. CONCLUSION: Findings suggest ways to prioritize health equity in a public health emergency, including through properly resourcing PHUs, early planning and trust building, and improved equity-based data collection.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".