MétaCan
Menu
Back to cohort
Record W3094048255 · doi:10.1002/hec.4181

Parental beliefs and willingness to pay for reduction in their child's asthma symptoms: A joint estimation approach

2020· article· en· W3094048255 on OpenAlexaff
Irene Mussio, Sylvia Brandt, W. Michael Hanemann

Bibliographic record

VenueHealth Economics · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsMcMaster University
Fundersnot available
KeywordsWillingness to payAsthmaEndogeneityContingent valuationValuation (finance)UnobservableMedicineRevealed preferenceEconomicsWorryAmenityActuarial scienceEconometricsPsychiatryMicroeconomicsAnxiety

Abstract

fetched live from OpenAlex

Many aspects of asthma-in particular the relationship between beliefs, averting behaviors, and symptoms-are not directly observable from market data. An approach that combines observable market data with nonmarket valuation to gather data on unobservable aspects of the illness can improve efforts to quantify the burden of asthma if it accounts for the endogeneity in the system. Such approaches are used in the valuation of recreation but have not been widely used to value the burden of a chronic illness. We estimate parents' willingness to pay (WTP) to reduce their child's asthma symptoms using a three-equation model that combines revealed preference, contingent valuation, and burden of asthma, increasing the efficiency of estimation and correcting for endogeneity. WTP for a device that reduces a child's asthma symptoms by 50% is $125/month (s.d. $20). Parents' valuations are driven by beliefs about asthma and by their degree of worry about asthma between episodes. There is a nonlinear relationship between the number of days with symptoms and WTP per symptom day. The experience of living with asthma affects families' responses to a contingent valuation scenario, because it influences willingness to spend money to manage the illness and their subjective perceptions and beliefs about the illness itself.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.287
Threshold uncertainty score0.750

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.101
GPT teacher head0.233
Teacher spread0.132 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueHealth EconomicsSame topicEconomic and Environmental ValuationFrench-language works237,207