An Approach to Reconciling Competing Ethical Principles in Aggregating Heterogeneous Health Preferences
Bibliographic record
Abstract
BACKGROUND: Health-related quality of life (HRQL) scores are used extensively to quantify the effectiveness of medical interventions. Societal preference-based HRQL scores aim to produce societal valuations of health by aggregating valuations from individuals in the general population, where each aggregation procedure embodies different ethical principles, as explained in social choice theory. METHODS: Using the Health Utilities Index as an exemplar, we evaluate societal preference-based HRQL measures in the social choice theory framework. RESULTS: We find that current preference aggregation procedures are typically justified in terms of social choice theory. However, by convention, they use only one of many possible aggregation procedures (the mean). Central to the choice of aggregation procedure is how to treat preference heterogeneity, which can affect analyses that rely on HRQL scores, such as cost-effectiveness analyses. We propose an analytical-deliberative framework for choosing one (or a set of) aggregation procedure(s) in a socially credible way, which we believe to be analytically sound and empirically tractable, but leave open the institutional mechanism needed to implement it. CONCLUSIONS: Socially acceptable decisions about aggregating heterogeneous preferences require eliciting stakeholders' preferences among the set of analytically sound procedures, representing different ethical principles. We describe a framework for eliciting such preferences for the creation of HRQL scores, informed by social choice theory and behavioral decision research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.055 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".