If the price is right: vagueness and values clarification in contingent valuation
Bibliographic record
Abstract
The use of willingness to pay to value the benefits of health care is increasing. Much of this work assumes that health preferences are well formed or 'complete' and readily revealed if the right question is asked in the right way. We examined this assumption, seeking evidence in a mixed-methods study that explored the meaning and implications of vague responses to a payment-scale based willingness to pay exercise.One-half of the sample said that their vagueness meant that their maximum willingness to pay was actually greater than the amount that they had previously said it was. Thirty percent agreed that they would probably pay pound 10 more than a sum that they had previously said they would most definitely not pay, if they found this to be the cost of the vaccine. Interview data supported the view that the payment scale had failed to elicit the maximum willingness to pay and that some participants used the information on cost to help clarify their values, in contrast to the theory underpinning willingness to pay. The results suggest a need to consider values-clarification in health economic evaluations.
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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 | 0.002 | 0.000 |
| 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) | 0.001 | 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 teacher head, 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".