Priority Setting and Resource Allocation in Healthcare: Learning from Local Government
Bibliographic record
Abstract
Introduction : Although seemingly disparate in nature, healthcare and local governments share a commitment to serving the public - as citizens and as patients - by ensuring their health and long-term wellbeing. Beyond this mutual interest, both areas face growing scarcity of resources while demands for services continue to rise. As a result, leaders in both disciplines must face difficult decisions related to setting priorities and allocating resources. Method : From an established position within healthcare including decades of understanding and application of health economics, a team of Canadian Health Economists and Health Services researchers launched an exploratory literature review of priority setting and resource allocation (PSRA) in local government. Searches of six databases and multiple grey literature resources were conducted – 565 papers were identified, 121 papers were left for full review, and 38 were used to inform the research topic. Results : Budgeting theory and practice in local government were identified as drivers for PSRA. Budgeting theories included: Incrementalism and Rationalism, and budgeting practice included: Line-item, Program, Performance, Outcome, Participatory, and Priority Based Budgeting. Each theory and practice is outlined in the article as well as key comparisons to current practice in healthcare. Ultimately, the intention is to create stronger links between healthcare and local government by highlighting common challenges and approached to this critical area of study and practice.
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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 | 0.014 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".