MétaCan
Menu
Back to cohort

Path analysis of warning label effects on negative emotions and quit attempts: A longitudinal study of smokers in Australia, Canada, Mexico, and the US

2017· article· en· W2765311366 on OpenAlex
Yoo Jin Cho, James F. Thrasher, Hua‐Hie Yong, André Salem Szklo, Richard J. O’Connor, Maansi Bansal‐Travers, David Hammond, Geoffrey T. Fong, James W. Hardin

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueSocial Science & Medicine · 2017
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsOntario Institute for Cancer ResearchUniversity of Waterloo
FundersNational Cancer Institute
KeywordsWorryDisgustPsychologyDemographyFeelingGeneralized estimating equationPath analysis (statistics)Social psychologyClinical psychologyMedicineAngerPsychiatryAnxiety

Abstract

fetched live from OpenAlex

BACKGROUND: Cigarette pack health warning labels can elicit negative emotions among smokers, yet little is known about how these negative emotions influence behavior change. OBJECTIVE: Guided by psychological theories emphasizing the role of emotions on risk concern and behavior change, we investigated whether smokers who reported stronger negative emotional responses when viewing warnings reported stronger responses to warnings in daily life and were more likely to try to quit at follow-up. METHODS: We analyzed data from 5439 adult smokers from Australia, Canada, Mexico, and the US, who were surveyed every four months from September 2012 to September 2014. Participants were shown warnings already implemented on packs in their country and reported negative emotional responses (i.e., fear, disgust, worry), which were averaged (range = 1 to 9). Country-stratified logistic and linear generalized estimating equations were used to analyze the effect of negative emotional responses on self-reported responses to warnings in daily life (i.e., attention, risk concern, avoidance of warnings, forgoing planned cigarettes) and quit attempts at follow-up. Models were adjusted for socio-demographic and smoking-related characteristics, survey wave, and the number of prior surveys answered. RESULTS: Smokers who reported stronger negative emotions were more likely to make quit attempts at follow-up (Adjusted ORs ranged from 1.09 [95% CI 1.04 to 1.14] to 1.17 [95% CI 1.12 to 1.23]; p < .001) than those who reported lower negative emotions. This relationship was mediated through attention to warnings and behavioral responses to warnings. There was no significant interaction of negative emotions with self-efficacy or nicotine dependence. CONCLUSION: Negative emotions elicited by warnings encourage behavior change, promoting attention to warnings and behavioral responses that positively predict quit attempts.

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.

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 score0.369
Threshold uncertainty score0.779

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
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 head0.059
GPT teacher head0.382
Teacher spread0.323 · 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