Assessing households’ willingness to pay for an immediate pandemic influenza vaccination programme
Bibliographic record
Abstract
AIMS: This study sought to contribute to the existing literature on pandemic influenza vaccination studies by providing additional evidences of households' willingness to pay (WTP) for protection against influenza during a pandemic situation from North America. METHODS: A standard dichotomous-choice contingent valuation survey was designed and completed in a sample of 306 individuals living in the Greater Toronto Area, Ontario, Canada. RESULTS: This study shows that, on average, households are willing to pay $417.35 for immediate pandemic influenza (H1N1) vaccination. Results show that the vaccine price, age, gender, occupation, organisation, annual family income, receiving annual flu shot, having additional insurance, having someone with a serious illness in the house, knowledge about pandemics, trusting official information on pandemics, supporting government expenditure, and rating government pandemic planning have significant effects on the decision to accept the vaccine bids. CONCLUSIONS: The results reconfirm the findings of similar studies that influenza vaccine programmes are highly cost-effective despite the high programme cost, because people's WTP (benefits) for this programme is much higher than the actual costs. Pandemic influenza vaccination programmes should consider the demographic and economic status of the target population as such characteristics have significant impacts on the benefits that people place on such programmes.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".