Noticing education campaigns or public health messages about vaping among youth in the United States, Canada and England from 2018 to 2022
Bibliographic record
Abstract
Public health campaigns have the potential to correct vaping misperceptions. However, campaigns highlighting vaping harms to youth may increase misperceptions that vaping is equally/more harmful than smoking. Vaping campaigns have been implemented in the United States and Canada since 2018 and in England since 2017 but with differing focus: youth vaping prevention (United States/Canada) and smoking cessation (England). We therefore examined country differences and trends in noticing vaping campaigns among youth and, using 2022 data only, perceived valence of campaigns and associations with harm perceptions. Seven repeated cross-sectional surveys of 16-19 year-olds in United States, Canada and England (2018-2022, n = 92 339). Over half of youth reported noticing vaping campaigns, and noticing increased from August 2018 to February 2020 (United States: 55.2% to 74.6%, AOR = 1.21, 95% CI = 1.18-1.24; Canada: 52.6% to 64.5%, AOR = 1.13, 1.11-1.16; England: 48.0% to 53.0%, AOR = 1.05, 1.02-1.08) before decreasing (Canada) or plateauing (England/United States) to August 2022. Increases were most pronounced in the United States, then Canada. Noticing was most common on websites/social media, school and television/radio. In 2022 only, most campaigns were perceived to negatively portray vaping and this was associated with accurately perceiving vaping as less harmful than smoking among youth who exclusively vaped (AOR = 1.46, 1.09-1.97). Consistent with implementation of youth vaping prevention campaigns in the United States and Canada, most youth reported noticing vaping campaigns/messages, and most were perceived to negatively portray vaping.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".