The Effect of Cigarette Packaging and Illness Sensitivity on Attention to Graphic Health Warnings: A Controlled Study
Bibliographic record
Abstract
INTRODUCTION: The social and health care costs of smoking are immense. To reduce these costs, several tobacco control policies have been introduced (eg, graphic health warnings [GHWs] on cigarette packs). Previous research has found plain packaging (a homogenized form of packaging), in comparison to branded packaging, effectively increases attention to GHWs using UK packaging prototypes. Past studies have also found that illness sensitivity (IS) protects against health-impairing behaviors. Building on this evidence, the goal of the current study was to assess the effect of packaging type (plain vs. branded), IS level, and their interaction on attention to GHWs on cigarette packages using proposed Canadian prototypes. AIMS AND METHODS: We assessed the dwell time and fixations on the GHW component of 40 cigarette pack stimuli (20 branded; 20 plain). Stimuli were presented in random order to 50 smokers (60.8% male; mean age = 33.1; 92.2% daily smokers) using the EyeLink 1000 system. Participants were divided into low IS (n = 25) and high IS (n = 25) groups based on scores on the Illness Sensitivity Index. RESULTS: Overall, plain packaging relative to branded packaging increased fixations (but not dwell time) on GHWs. Moreover, low IS (but not high IS) smokers showed more fixations to GHWs on plain versus branded packages. CONCLUSIONS: These findings demonstrate that plain packaging is a promising intervention for daily smokers, particularly those low in IS, and contribute evidence in support of impending implementation of plain packaging in Canada. IMPLICATIONS: Our findings have three important implications. First, our study provides controlled experimental evidence that plain packaging is a promising intervention for daily smokers. Second, the findings of this study contribute supportive evidence for the impending plain packaging policy in Canada, and can therefore aid in defense against anticipated challenges from the tobacco industry upon its implementation. Third, given its effects in increasing attention to GHWs, plain packaging is an intervention likely to provide smokers enhanced incentive for smoking cessation, particularly among those low in IS who may otherwise be less interested in seeking treatment for tobacco dependence.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".