Hospital funding reforms in Canada: a narrative review of Ontario and Quebec strategies
Bibliographic record
Abstract
BACKGROUND: In the early 2000s, Ontario and Quebec, two provinces of Canada, began to introduce hospital payment reforms to improve quality and access to care. This paper (1) critically reviews patient-based funding (PBF) implementation approaches used by Quebec and Ontario over 15 years, and (2) identifies factors that support or limit PBF implementation to inform future decisions regarding the use of PBF models in both provinces. METHODS: We adopted a narrative review approach to document and critically analyse Quebec and Ontario experiences with the implementation of patient-based funding. We searched for documents in the scientific and grey literature and contacted key stakeholders to identify relevant policy documents. RESULTS: Both provinces targeted similar hospital services-aligned with nationwide policy goals-fulfilling in part patient-based funding programmes' objectives. We identified four factors that played a role in ensuring the successful-or not-implementation of these strategies: (1) adoption supports, (2) alignment with programme objectives, (3) funding incentives and (4) stakeholder engagement. CONCLUSIONS: This review provides lessons in the complexity of implementing hospital payment reforms. Implementation is enabled by adoption supports and funding incentives that align with policy objectives and by engaging stakeholders in the design of incentives.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.024 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".