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Record W2038364745 · doi:10.1080/14622200802023841

How does a failed quit attempt among regular smokers affect their cigarette consumption? Findings from the International Tobacco Control Four-Country Survey (ITC-4)

2008· article· en· W2038364745 on OpenAlexfundaboutno aff
Hua‐Hie Yong, Ron Borland, Andrew Hyland, Mohammad Siahpush

Bibliographic record

VenueNicotine & Tobacco Research · 2008
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsnot available
FundersNational Cancer InstituteCancer Council VictoriaUniversity of Illinois at ChicagoUniversity of WaterlooCancer Research UK
KeywordsTobacco controlConsumption (sociology)Telephone surveyMedicineQuit smokingEnvironmental healthDemographySmoking cessationAffect (linguistics)CohortPsychologyPublic healthAdvertisingBusinessInternal medicine

Abstract

fetched live from OpenAlex

Recent cross-sectional data suggests that smokers tend to reduce smoking following a failed self-initiated quit attempt, possibly motivated by the need to reduce harms or to facilitate future quitting or both. This study prospectively examined changes in cigarette consumption among adult smokers who relapsed from a quit attempt. It uses data from the first three waves of the International Tobacco Control Four-Country Survey (ITC-4), a random digit-dialed telephone survey of a cohort of over 9,000 adult smokers from the United Kingdom, United States, Canada, and Australia, followed up annually. Compared with those who did not make a quit attempt, relapsers were more likely to reduce consumption (average reduction of 0.7 vs. 3.4, respectively) over a mean period of 7 months between waves 1 and 2. Of the relapsers, 52% reduced their consumption by 5% or more, but 22% increased it. Smokers who smoked heavily at baseline, whose last quit attempt ended more recently, was of longer duration, and quit via a gradual cut-down method were all independently associated with reducing smoking following a failed attempt. These findings were similar across all four countries and were successfully replicated using waves 2-3 data. Change in consumption between waves 1 and 2 (whether increase or decrease) was maintained by a substantial number a year later (wave 3), but change did not undermine nor promote quitting between waves 2 and 3.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.105
GPT teacher head0.340
Teacher spread0.236 · 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.

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

Citations15
Published2008
Admission routes2
Has abstractyes

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