Priority setting in healthcare: towards guidelines for the program budgeting and marginal analysis framework
Bibliographic record
Abstract
Economists' approaches to priority setting focus on the principles of opportunity cost, marginal analysis and choice under scarcity. These approaches are based on the premise that it is possible to design a rational priority setting system that will produce legitimate changes in resource allocation. However, beyond issuing guidance at the national level, economic approaches to priority setting have had only a moderate impact in practice. In particular, local health service organizations - such as health authorities, health maintenance organizations, hospitals and healthcare trusts - have had difficulty implementing evidence from economic appraisals. Yet, in the context of making decisions between competing claims on scarce health service resources, economic tools and thinking have much to offer. The purpose of this article is to describe and discuss ten evidence-based guidelines for the successful design and implementation of a program budgeting and marginal analysis (PBMA) priority setting exercise. PBMA is a framework that explicitly recognizes the need to balance pragmatic and ethical considerations with economic rationality when making resource allocation decisions. While the ten guidelines are drawn from the PBMA framework, they may be generalized across a range of economic approaches to priority setting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.234 | 0.197 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.012 | 0.007 |
| Research integrity | 0.012 | 0.026 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".