Over-Time Impacts of Pictorial Health Warning Labels and their Differences across Smoker Subgroups: Results from Adult Smokers in Canada and Australia
Bibliographic record
Abstract
Introduction: This study examines patterns of change in different smoker subgroups' responses to new pictorial health warning labels (HWLs) over the initial, two year post-implementation period in Canada, where HWLs include package inserts with cessation messages, and Australia, where "plain" packaging (i.e., prohibition of brand imagery) was also implemented. Methods: Data were collected from online consumer panels in Canada (nsmokers = 3153; nobservations = 5826) and Australia (nsmokers = 2699; nobservations = 5818) from September 2012 to September 2014, with approximately 1000 adult smokers surveyed in each country every four months, using replenishment to maintain sample size. Data were analyzed using generalized estimating equation models where main effects and interactions among time, country, and socio-demographic factors on HWL responses (i.e., attention to HWLs; cognitive and behavioral responses to HWLs) were examined. Results: Over time, attention to HWLs declined but cognitive and forgoing responses to HWLs increased, in both Canada and Australia. In both countries, compared to smokers with low income and/or education, smokers with high income and/or education showed an increase over time in attention and cognitive responses to HWLs (p < .05). In Australia only, compared to older smokers, younger smokers showed less decline over time in attention and greater increase in cognitive and forgoing responses to HWLs (p < .001). Conclusions: Novel HWL policies in Canada and Australia appear effective in staving off "wear out" over the first 2 years after implementation, particularly amongst smokers who are from higher SES groups and, in Australia, who are younger. Implications: Previous research shows that the effects of health warning label (HWL) on smokers decline over time, but no studies to date have evaluated whether trends differ across socio-demographic groups. This study suggests that innovative policy configurations that combine prominent pictorial HWLs with inserts (Canada) and with "plain" packaging (Australia) may delay wear out over the first 2 years after implementation. While this study found evidence for wear out in attention to HWLs, other HWL responses (cognitive responses, forgoing cigarettes) actually increased over time, with greater increases amongst smokers with higher income and/or education.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".