Priority setting for environmentally sustainable health care: emerging approaches to fair resource allocation
Bibliographic record
Abstract
Priority setting in health care is a research and practice area at the intersection of medicine, ethics, and economics, which aims to systematically and transparently evaluate the value for money of health services to support fair resource allocation. Three widely accepted principles for fair priority setting are cost-effectiveness, priority to the worse off, and financial risk protection, with a wide range of other contested criteria. Conceptualising and navigating potential synergies and trade-offs between competing goals, and clearly communicating the values at stake, are the central tasks of priority setting. It is now increasingly clear that health care systems have substantial environmental effects that have been largely overlooked, and that the growing movement towards high-quality, low-polluting, and climate-resilient health systems has potentially far-reaching implications for resource allocation. This Personal View explores how priority setting tools can facilitate the transition to environmentally sustainable health care. We outline the key principles of priority setting in health care and explore how environmental sustainability can be incorporated into resource allocation tools, such as health technology assessment and multicriteria decision analysis, as well as budgetary processes, such as programme budgeting and marginal analysis. We conclude with some implications for wider health system transformation.
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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 | 0.149 | 0.143 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.006 | 0.075 |
| Scholarly communication | 0.020 | 0.020 |
| Open science | 0.008 | 0.018 |
| Research integrity | 0.012 | 0.021 |
| Insufficient payload (model declined to judge) | 0.009 | 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".