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Smokers’ reactions to the new larger health warning labels on plain cigarette packs in Australia: findings from the ITC Australia project

2015· article· en· W2017141922 on OpenAlexafffund
Hua‐Hie Yong, Ron Borland, David Hammond, James F. Thrasher, K. Michael Cummings, 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 InstituteCancer Council VictoriaUniversity of WaterlooCancer Research UK
KeywordsTobacco controlPsychologyCognitionEnvironmental healthCohortSocial psychologyMedicinePublic healthPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVES: This study examined whether larger sized Australian cigarette health warning labels (HWLs) with plain packaging (PP) were associated with increased desirable reactions towards the HWLs postimplementation. METHODS: Data were from the International Tobacco Control (ITC) longitudinal cohort survey assessing Australian smokers one wave prior to the policy change in 2011 (n=1104) and another wave after the policy change in 2013 (n=1093). We assessed initial attentional orientation (AO) to or away from warnings, plus other reactions, including cognitive reactions towards the HWLs and quit intentions. RESULTS: As expected, AO towards the HWLs and reported frequency of noticing warnings increased significantly after the policy change, but not more reading. Smokers also thought more about the harms of smoking and avoided the HWLs more after the policy change, but frequency of forgoing cigarettes did not change. The subgroup that switched from initially focusing away to focusing on the HWLs following the policy change noticed and read the HWLs more, and also thought more about the harmful effects of smoking, whereas the subgroup (5.4%) that changed to focusing away from the HWLs showed opposite effects. We tested the mediational model of Yong et al and confirmed it for predicting quit intentions, with larger effects post-policy. CONCLUSIONS: Increasing the size of HWLs and introducing them on PP in Australia appears to have led to an overall increase in desired levels and strength of some reactions, but evidence of reactance was among a small minority.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.141
Threshold uncertainty score0.977

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 integrity0.0000.001
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 head0.112
GPT teacher head0.374
Teacher spread0.261 · 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

Citations50
Published2015
Admission routes2
Has abstractyes

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