What Drives Responses to Willingness-to-pay Questions? A Methodological Inquiry in the Context of Hypertension Self-management
Bibliographic record
Abstract
Background: The use of economic evaluation to determine the cost-effectiveness of health interventions is recommended by decision-making bodies internationally. Understanding factors that explain variations in costs and benefits is important for policy makers. Objective: This work aimed to test a priori hypotheses defining the relationship between benefits of using self-management equipment (measured using the willingness-to-pay (WTP) approach) and a number of demographic and other patient factors. Methods: Data for this study were collected as part of the first major randomised controlled trial of self-monitoring combined with self-titration in hypertension (TASMINH2). A contingent valuation framework was used with patients asked to indicate how much they were willing to pay for equipment used for self-managing hypertension. Descriptive statistics, simple statistical tests of differences and multivariate regression were used to test six a priori hypotheses. Results: Data for this study were collected as part of the first major randomised controlled trial of self-monitoring combined with self-titration in hypertension (TASMINH2). A contingent valuation framework was used with patients asked to indicate how much they were willing to pay for equipment used for self-managing hypertension. Descriptive statistics, simple statistical tests of differences and multivariate regression were used to test six a priori hypotheses. Conclusion: The majority of hypertensive patients who had taken part in a self-management study were prepared to purchase the self-monitoring equipment using their own funds, more so for men, those with higher incomes and those with greater satisfaction. Further research based on bigger and more diverse populations is recommended.
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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 | 0.387 | 0.654 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".