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What cigarette price is required for smokers to attempt to quit smoking? Findings from the ITC Korea Waves 2 and 3 Survey

2015· article· en· W2139844340 on OpenAlexafffund
E.-J. Park, Susan Park, Sung‐Il Cho, Yong Ho Kim, Han Gil Seo, Pete Driezen, Anne C K Quah, Geoffrey T. Fong

Bibliographic record

VenueTobacco Control · 2015
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsOntario Institute for Cancer ResearchUniversity of Waterloo
FundersNational Cancer InstituteCanadian Cancer Society Research InstituteCanadian Institutes of Health ResearchOntario Institute for Cancer Research
KeywordsQuit smokingTobit modelTobacco controlLogistic regressionTax policyMedicineEconomicsPublic economicsSmoking cessationEnvironmental healthDemographic economicsAdvertisingBusinessTax reformEconometricsPublic healthNursing

Abstract

fetched live from OpenAlex

OBJECTIVES: We assess the cigarette price that would motivate smokers to quit. We also explore the factors associated with the required price, including exposures to non-tax tobacco control policies. METHODS: Cross-sectional analysis was conducted on data from 1257 male smokers, who participated in either Wave 2 or 3 of the ITC Korea Survey. Information was obtained on what cigarette price per pack would make them try to quit ('price to quit'). Tobit regression on log-transformed price and logistic regression on non-quitting were conducted to identify associated factors. RESULTS: The median price to quit was KRW5854 (US$5.31)/pack, given the current price of KRW2500 (US$2.27)/pack. Younger age, higher education, lack of concern about the health effects of smoking, lack of quit attempts and more cigarettes consumed per day were related to a higher price needed for a quit attempt. Exposures to combinations of non-tax policies were significantly associated with lower price levels to be motivated to quit. CONCLUSIONS: Considering the large price increase required for quit attempts, tax policy needs to be combined with other policies, particularly for certain groups, such as heavy smokers. Strengthening non-tax policies is likely to facilitate greater responsiveness to tax policy.

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)0.001
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 score0.041
Threshold uncertainty score0.649

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.062
GPT teacher head0.316
Teacher spread0.255 · 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

Citations12
Published2015
Admission routes2
Has abstractyes

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