Is There a General Preference for the Worse Off? A Discrete Choice Experiment Using Individually Calibrated Health State Valuations
Bibliographic record
Abstract
OBJECTIVES: We aimed to develop and demonstrate a method for examining public preferences for priority setting in healthcare, using EQ-5D-5L health states valued at an individual level. We examine the Norwegian population's preferences for prioritizing the "worse off" in terms of health at a cost to health maximization. This intuition is a part of healthcare priority setting frameworks in several countries. Preference studies help explore the legitimacy of such policies, but most studies do not use individual-level health state values, which may distort their conclusions. METHODS: Task-based face-to-face interviews were conducted in the Norwegian general population. First, respondents valued a set of EQ-5D-5L health states using time trade-off. Second, discrete choice tasks were administered, describing health state improvements that contrasted utility maximization and benefit to the worse off, according to each respondent's valuation. Analysis used mixed-effects models with choice and respondent properties. RESULTS: We conducted 606 interviews, of which 468 respondents were included in the priority setting exercise. Respondents' choices were influenced by the size of the health improvement, but not by an option being worse in terms of a lower pretreatment health state value. Choice behavior was influenced by respondents' gender, religion, and age. On average, the choices indicate a preference for prioritizing the better off. CONCLUSIONS: Our two-stage design using individual health state values is feasible, although there are trade-offs and room for development with such a design. We found no support for a preference for prioritizing the worse off when using individual-level health state valuations.
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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 | 0.018 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".