The influence of public health organization on response to the COVID-19 pandemic in four Canadian provinces: A comparative qualitative analysis
Bibliographic record
Abstract
Background: Studies of COVID-19 pandemic responses reveal shortcomings that may relate to the organization of public health systems. Objective: This study uncovers the organizational factors that may strengthen pandemic responses in high-income countries through a comparative analysis of four Canadian provinces. Methods: We undertook a qualitative multiple case study, collecting data through document review and 103 interviews with government and non-governmental actors involved in pandemic response. Analysis explored how differences in the organization of provincial public health systems influenced decision-making, advisory, coordination and adaptation processes. Results: The scale of the pandemic positioned the Premier as legitimate decision-maker in all provinces regardless of the distribution of authority in their public health systems. Capacity for generating public health advice was increased through existing or new organizations and highlighted the advantage of links to university expertise. All public health systems relied on healthcare resources for testing programs despite differences in the integration of public health under healthcare governance structures; centralization of healthcare governance was a facilitator. Adapting pandemic control measures to population needs was supported by linkages between organizations capable of apprehending needs and organizations that made decisions. Conclusions: This study builds on the literature of pandemic responses across high-income countries and uncovers organizational factors that may enhance agility to rapidly expand capacities, connect actors for emergency responses, and strengthen public health systems.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.023 | 0.008 |
| Scholarly communication | 0.004 | 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".