Promise and peril: how health system reforms impacted public health in three Canadian provinces
Bibliographic record
Abstract
OBJECTIVES: Several Canadian provinces and territories have reformed their health systems by centralizing power, resources, and responsibilities. Our study explored motivating factors and perceived impacts of centralization reforms on public health systems and essential operations. METHODS: A multiple case study design was used to examine three Canadian provinces that have undergone, or are in the process of undergoing, health system reform. Semi-structured interviews were conducted with 58 participants within public health at strategic and operational levels, from Alberta, Ontario, and Québec. Data were analyzed using a thematic analytical approach to iteratively conceptualize and refine themes. RESULTS: Three major themes were developed to describe the context and impacts of health system centralization reforms on public health: (1) promising "value for money" and consolidating authority; (2) impacting intersectoral and community-level collaboration; and (3) deprioritizing public health operations and contributing to workforce precarity. Centralization highlighted concerns about the prioritization of healthcare sectors. Some core public health functions were reported to operate more efficiently, with less duplication of services, and improvements in program consistency and quality, particularly in Alberta. Reforms were also reported to have diverted funding and human resources away from core essential functions, and diminished the public health workforce. CONCLUSION: Our study highlighted that stakeholder priorities and a limited understanding about public health systems influenced how reforms were implemented. Our findings support calls for modernized and inclusive governance, stable public health funding, and investment in the public health workforce, which may help inform future reforms.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.032 | 0.012 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".