Smokers’ reactions to the new larger health warning labels on plain cigarette packs in Australia: findings from the ITC Australia project
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".