Introducing Priority Setting and Resource Allocation in Home and Community Care Programs
Bibliographic record
Abstract
OBJECTIVE: To use evidence from research to identify and implement priority setting and resource allocation that incorporates both ethical practices and economic principles. METHOD: Program budgeting and marginal analysis (PBMA) is based on two key economic principles: opportunity cost (i.e. doing one thing instead of another) and the margin (i.e. resource allocation should result in maximum benefit for available resources). An ethical framework for priority setting and resource allocation known as Accountability for Reasonableness (A4R) focuses on making sure that resource allocations are based on a fair decision-making process. It includes the following four conditions: publicity; relevance; appeals; and enforcement. More recent literature on the topic suggests that a fifth condition, that of empowerment, should be added to the Framework. The 2007-08 operating budget for Home and Community Care, excluding the residential sector, was developed using PBMA and incorporating the A4R conditions. RESULTS: Recommendations developed using PBMA were forwarded to the Executive Committee, approved and implemented for the 2007-08 fiscal year operating budget. In addition there were two projects approved for approximately $200,000. CONCLUSION: PBMA is an improvement over previous practice. Managers of Home and Community Care are committed to using the process for the 2008-09 fiscal year operating budget and expanding its use to include mental health and addictions services. In addition, managers of public health prevention and promotion services are considering using the process.
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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 | 0.277 | 0.327 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".