Priority Setting in the Context of Planetary Healthcare
Bibliographic record
Abstract
The realities of ecosystem breakdown and climate change pose a significant threat to the health of individuals around the world, disproportionately affecting poor and vulnerable populations. Every sector in society, including healthcare, needs to be engaged in the tremendous collective effort and transformational change needed to limit global warming. We see priority setting as having a key role to play in reallocating existing budgets within healthcare systems whilst at the same time being used to facilitate sustainable and more efficient resource allocation across countries. Priority setting looks to fairly distribute resources with the goal of improving patient and population health outcomes. However, these goals can be broadened to include consideration of environmental impact based on our understanding of the necessity of emissions reduction to address the climate crisis and promote population health. In this paper, we introduce key concepts of priority setting and identify the interplay between priority setting and the realities of resource scarcity in the realm of planetary healthcare. We propose that applying priority-setting principles could serve at least three goals; (1) protect and improve health outcomes; (2) limit unnecessary and marginal care; and (3) facilitate a just transition to a sustainable healthcare system.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".