From the trenches: views from decision-makers on health services priority setting
Bibliographic record
Abstract
Due to resource scarcity, health organizations worldwide must decide what services to fund and, conversely, what services not to fund. One approach to priority setting, which has been widely used in Britain, Australia, New Zealand and Canada, is programme budgeting and marginal analysis (PBMA). To date, such activity has primarily been based at a micro level, within programmes of care. In order to institute and refine the PBMA framework at a macro level across major service areas within a single health authority, researchers and decision-makers in Alberta embarked on a participatory action research project together. This paper identifies key issues of importance to decision-makers in a real-world priority-setting context. Themes discussed include making comparisons across disparate patient groups, dealing with political factors, using relevant forms of evidence, recognizing innovations and involving the public. The in-depth insight gained through this qualitative analysis will enable future refinement of PBMA at a macro level in the health authority under study, and should also serve to inform priority-setting activity in regionalized contexts elsewhere. In identifying aspects of priority setting that are important to decision-makers, researchers can also be better informed with respect to real-world processes.
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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 | 0.123 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.024 | 0.073 |
| Scholarly communication | 0.034 | 0.025 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.017 | 0.032 |
| 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".