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 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 | 0.126 | 0.419 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.033 |
| Scholarly communication | 0.009 | 0.028 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".