Baseline assessment of noticing e-cigarette health warnings among youth and young adults in the United States, Canada and England, and associations with harm perceptions, nicotine awareness and warning recall
Bibliographic record
Abstract
Health warnings on tobacco products can inform users of potential risks. However, little is known about young people's exposure to health warnings on e-cigarette products. This baseline assessment of young people's noticing e-cigarette warnings uses nationally representative data from three countries. Data were collected under Wave 1 of the ITC Youth Tobacco and E-cigarette Survey, conducted in Canada, England, and the US. Online surveys were completed by 16–19-year-olds in July/August 2017 (n = 12,064), when warnings were either newly required (England) or voluntarily carried by some manufacturers (US, Canada). Analyses examined prevalence and correlates of noticing warnings and associations between noticing warnings and product perceptions, adjusting for country, sex, age, race/ethnicity, and cigarette/e-cigarette use status. About 12% reported noticing warnings on e-cigarette packaging in the past 30 days. Noticing warnings was significantly more likely among youth in England (AOR = 1.3, p < .01) and the US (AOR = 1.3, p < .01) versus Canada, and was most likely among dual e-cigarette/cigarette users (AOR = 4.69, p < .001) versus nonusers. Unaided recall of the keyword “nicotine” was low among those who noticed warnings (7.5%). However, ever e-cigarette users who noticed warnings had higher odds of knowing whether e-cigarettes contained nicotine (AOR = 2.26, p < .001). Noticing warnings was significantly associated with higher odds of believing e-cigarettes cause at least some harm to users (AOR = 1.19), are as harmful as cigarettes (AOR = 1.45), and can be addictive (AOR = 1.43). Baseline assessment reveals that youth's noticing of e-cigarette warnings and recall of nicotine-addiction messages was low. Research should track exposure over time as warning requirements are implemented across different countries.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".