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Record W2642373416 · doi:10.1093/her/cyx032

Smokers’ perceptions of sources of advice about quitting: findings from the Australian arm of the ITC 4-country survey

2017· article· en· W2642373416 on OpenAlexfundno aff
James Balmford

Bibliographic record

VenueHealth Education Research · 2017
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsnot available
FundersNational Cancer InstituteCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsHarmQuit smokingPerceptionSmoking cessationPsychologyMedicineSocial psychology

Abstract

fetched live from OpenAlex

Smokers are exposed to advice about quitting from numerous sources. Within the 2013 ITC 4-Country Survey, 1211 Australian smokers or recent ex-smokers rated the perceived importance of eight sources of advice, categorized into evidence-based, non evidence-based, personal experience and vicarious experience (two items each), and also rated their intention to quit, nicotine dependence, use of quit medication, health concerns and harm beliefs. The eight items were all positively correlated. Respondents who placed greater importance on their experiences (either personal or vicarious) were more likely to agree that the evidence for smoking-related harm is exaggerated, and although not more likely to intend to quit overall, these responses were most strongly related to quit intention. Notably, of those responding that all sources were 'not at all important' (or don't know), only 3.2% reported any interest in quitting in the next 6 months (compared to 36.0% among those who endorsed any), 12.8% were often concerned about smoking's effect on their health (compared with 60.4%), and 73.7% agreed that 'smoking is no more risky than other things' (compared with 34.5%). There was no evidence that rejecting evidence-based sources (medical or governmental) in favour of other sources was associated with lower quit intentions or behaviour.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score0.892

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.193
GPT teacher head0.507
Teacher spread0.314 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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