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Record W2089755762 · doi:10.1177/1403494812453884

Assessing households’ willingness to pay for an immediate pandemic influenza vaccination programme

2012· article· en· W2089755762 on OpenAlexaffabout
Ali Asgary

Bibliographic record

VenueScandinavian Journal of Public Health · 2012
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsYork University
Fundersnot available
KeywordsPandemicWillingness to payVaccinationContingent valuationGovernment (linguistics)PopulationEnvironmental healthBusinessSocioeconomicsMedicineEconomicsCoronavirus disease 2019 (COVID-19)Infectious disease (medical specialty)DiseaseImmunology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.352
Threshold uncertainty score0.634

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.429
GPT teacher head0.507
Teacher spread0.078 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2012
Admission routes2
Has abstractyes

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