Exposure to and perceptions of health warning labels on nicotine vaping products: findings from the 2016 International Tobacco Control Four Country Smoking and Vaping Survey
Bibliographic record
Abstract
BACKGROUND AND AIMS: The presence and content of health warning labels (HWLs) on nicotine vaping products (NVPs), such as electronic cigarettes, varies by country and manufacturer. We compared proportions of people who report (i) noticing HWLs on NVPs and (ii) feeling concerned having noticed HWLs, by country and by smoking or vaping status. We also examined recall of HWL content and whether this varies by country. DESIGN: Cross-sectional survey. SETTING: Australia (AU), Canada (CA), England (EN) and the United States (US). At the time of data collection, HWLs on NVPs were only mandatory in EN. PARTICIPANTS: A total of 11 561 respondents from the following samples in the 2016 International Tobacco Control Four Country Project: (1) re-contacted smokers and quitters who had participated in the previous wave of the project; (2) newly recruited current smokers and recent quitters; and (3) newly recruited current vapers from CA, EN and US. MEASUREMENTS: Outcomes included: (1) having noticed HWLs on NVPs, (2) feeling concerned having noticed HWLs, and (3) recall of HWL message content. FINDINGS: Compared with respondents in EN, respondents in CA were more likely to report having noticed HWLs [odds ratio (OR) = 1.58, P = 0.02], whereas respondents in AU (OR = 0.76, P = 1.00) and the US (OR = 1.54, P = 0.09) were not significantly more or less likely to report having noticed HWLs. Compared with concurrent smokers and vapers, daily smokers, non-daily smokers and quitters were less likely to report having noticed HWLs (ORs = 0.21, 0.33 and 0.19, respectively, all P < 0.001). There were no significant differences in reports of noticing HWLs when comparing concurrent smokers and vapers with daily (OR = 1.62, P = 0.91) or non-daily (OR = 1.15, P = 1.00) vapers. There were no significant differences by country in reporting that HWLs made them concerned about using NVPs. Daily vapers were less likely to report feeling concerned than concurrent users (OR = 0.11, P = 0.017). Among those who reported reading HWLs (n = 688), there was little evidence of differences in recall of the HWL content. CONCLUSIONS: Respondents in England, where health warning labels on nicotine vaping products are mandatory, were not significantly more likely to report having noticed such warnings than those in Australia, Canada and the United States where warnings are not mandatory.
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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 | 0.000 |
| 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 | 0.000 |
| 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".