Who Answers ‘Willingness to Pay’ Questions?
Bibliographic record
Abstract
OBJECTIVES: The objectives of this study were twofold. The first was to compare characteristics of responders and non-responders to a survey of women attending a bone mineral density screening service in Aberdeen concerned with the screening process which contained questions on attenders' willingness to pay (WTP) and willingness to wait (WTW) for screening. The second objective was to compare the characteristics of those responding to either the WTP or the WTW questions relative to those who responded to both. METHODS: After receiving a scan, women completed the questionnaire at the clinic or returned it by post. Logistic regression analysis was used to compare the characteristics of the responders and non-responders. RESULTS: Those who smoked were less likely to return the questionnaire, whilst those who drank alcohol were more likely to return it. The majority of respondents answered both WTP and WTW questions. The proportions responding to the WTW and WTP questions were 93.2% and 81.5% for the two questions, respectively (95% confidence interval of difference = 9.4% to 13.9%). The only result which was statistically significant at the 1% level showed that, relative to those who answered the WTW but not the WTP questions, those who answered both were more likely to be older when they left full-time education. A weaker statistical association (at the 5% level) revealed that those who were older when leaving full-time education were more likely to answer a WTP question than not. CONCLUSIONS: WTP questions seem to be less acceptable to those who leave full-time education earlier. Analysts may need to account for this in future studies. Whether such results can be replicated and reasons for non-response should be investigated.
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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 | 0.012 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".