Societal Preferences for Distributive Justice in the Allocation of Health Care Resources
Why this work is in the frame
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Bibliographic record
Abstract
Economic theory suggests that resources should be allocated in a way that produces the greatest outputs, on the grounds that maximizing output allows for a redistribution that could benefit everyone. In health care, this is known as QALY (quality-adjusted life-year) maximization. This justification for QALY maximization may not hold, though, as it is difficult to reallocate health. Therefore, the allocation of health care should be seen as a matter of distributive justice as well as efficiency. A discrete choice experiment was undertaken to test consistency with the principles of QALY maximization and to quantify the willingness to trade life-year gains for distributive justice. An empirical ethics process was used to identify attributes that appeared relevant and ethically justified: patient age, severity (decomposed into initial quality and life expectancy), final health state, duration of benefit, and distributional concerns. Only 3% of respondents maximized QALYs with every choice, but scenarios with larger aggregate QALY gains were chosen more often and a majority of respondents maximized QALYs in a majority of their choices. However, respondents also appeared willing to prioritize smaller gains to preferred groups over larger gains to less preferred groups. Marginal analyses found a statistically significant preference for younger patients and a wider distribution of gains, as well as an aversion to patients with the shortest life expectancy or a poor final health state. These results support the existence of an equity-efficiency tradeoff and suggest that well-being could be enhanced by giving priority to programs that best satisfy societal preferences. Societal preferences could be incorporated through the use of explicit equity weights, although more research is required before such weights can be used in priority setting.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it