Public values and plurality in health priority setting: What to do when people disagree and why we should care about reasons as well as choices
Bibliographic record
Abstract
CONTEXT: 'What does 'The Public' think?' is a question often posed by researchers and policy makers, and public values are regularly invoked to justify policy decisions. Over time there has been a participatory turn in the social and health sciences, including health technology assessment and priority setting in health, towards citizen participation such that public policies reflect public values. It is one thing to agree that public values are important, however, and another to agree on how public values should be elicited, deliberated upon and integrated into decision-making. Surveys of public values rarely deliver unanimity, and preference heterogeneity, or plurality, is to be expected. METHODS: This paper examines the role of public values in health policy and how to elicit, analyse, and present values, in the face of plurality. We delineate the strengths and weaknesses of aggregative and deliberative methods before setting out a new empirical framework, drawing on Sunstein's Incompletely Theorised Agreements, based on three levels: principles, policies and patients. The framework is illustrated using a recognised policy dilemma - the provision of high cost, limited-effect medicines intended to extend life for people with terminal illnesses. FINDINGS: Application of the multi-level framework to public values permits transparent consideration of plurality, including analysis of coherence and consensus, in a way that offers routes to policy recommendations that are based on public values and justified in those terms. CONCLUSIONS: Using the new framework and eliciting quantitative and qualitative data across levels of abstraction has the potential to inform policy recommendations grounded in public values, where values are plural. This is not to suggest that one solution will magically emerge, but rather that choices between policies can be explicitly justified in relation to the properties of public values, and a much clearer understanding of (in)consistencies and areas of consensus.
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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 | 0.174 | 0.184 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.018 | 0.118 |
| Scholarly communication | 0.026 | 0.039 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.014 | 0.019 |
| Insufficient payload (model declined to judge) | 0.004 | 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".