Eliciting patients’ values by use of ‘willingness to pay’: letting the theory drive the method
Bibliographic record
Abstract
OBJECTIVE: To describe the three different ways in which 'willingness to pay' (WTP) has been used to elicit patients' values of alternative interventions. DESIGN: For each of the three approaches a survey of patients or the public was undertaken. SETTING, PARTICIPANTS AND INTERVENTIONS: studied For two surveys, the setting was Aberdeen Maternity Hospital, where pregnant women were asked about their WTP for different methods of prenatal screening for cystic fibrosis. In the third survey, parents of primary and secondary schoolchildren were asked about their WTP for different ways of providing child health services. MAIN OUTCOME MEASURES: Ability of WTP to discriminate between options (i.e. to say whether one option is 'better' than another) and the consistency of WTP with stated preferences. RESULTS: Experience with some methods shows that, despite the apparent logic of the technique, it is difficult to elicit consistent responses whereby WTP values derived match the rankings of interventions compared. The most promising technique, the 'marginal approach', happens to conform more with economic theory than other approaches. Potential limitations of WTP, such as its association with ability to pay, are discussed, as are approaches to dealing with such problems. Finally, if patients prefer an intervention that is more costly than the status quo, logic dictates that those extra resources will have to be obtained from another health-care programme. In such contexts, to aid decision-making, values derived from members of the community for different programmes may be more relevant than values derived from patients. Initial studies in the use of WTP in this broader context of eliciting community values are also outlined. CONCLUSIONS: WTP has potential, but its application, and interpretation, are not straightforward. More testing of the 'marginal approach' is required and greater use of qualitative research, to assess the validity of the approach, should be made in this area.
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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 | 0.101 | 0.278 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".