Tools of the trade: a comparative analysis of approaches to priority setting in healthcare
Bibliographic record
Abstract
In many countries, local managers and clinicians have been given responsibility to set health priorities and allocate resources accordingly. Although tools have been suggested for use in aiding this process, knowledge of these tools within health regions is lacking and comparative analysis in the literature is limited. Several approaches to priority setting are critiqued from both practical and theoretical perspectives, and a tangible way forward for such activity is provided. The approaches analysed include: needs assessment, core services, economic evaluation including quality-adjusted life year league tables, and programme budgeting and marginal analysis (PBMA). Needs assessment fails to recognize underlying economic principles of opportunity cost and the margin, while core services ignores the margin and has had limited impact in practice. Economic evaluations can consider marginal costs and benefits, but cannot always be used to inform decisions in a timely manner. PBMA is based on underlying economic principles and can pragmatically respond to objectives related to both efficiency and equity. Although PBMA is not without challenges, from an economic perspective, it does seem to "get the thinking right", and, importantly, as a process, can incorporate some of the other approaches to priority setting discussed in this paper.
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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 | 0.109 | 0.220 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.019 | 0.032 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.011 | 0.023 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".