Priority Setting in a Canadian Long-Term Care Setting: A Case Study Using Program Budgeting and Marginal Analysis
Bibliographic record
Abstract
ABSTRACT Canadian health regions are required to set priorities and allocate resources within a limited funding envelope. Program budgeting and marginal analysis (PBMA) was piloted in continuing care in Claresholm, Alberta, with the aim of improving overall benefit from available resources. A marginal-analysis expert panel was used to assess options for continuing-care delivery. Inputs into the decision-making process included evidence from the literature, regional and provincial reports, program budgeting information, and local knowledge. Recommendations included implementing adult, day-and-night support programs and converting long-term beds to convalescent beds. Changes were funded through allocating provincial Broda funding and altering nursing assistant and physiotherapy activity. PBMA was demonstrated to be an effective framework in aiding decision makers with redesigning services in Claresholm. This case study is one of several which indicate PBMA to be a valuable aid to priority setting in health care service provision.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".