Cross-Country Comparison of Bans on Internet Tobacco Advertising, and Search Interest in Vaping Products
Bibliographic record
Abstract
Background: The rise in vaping use across high-income countries is driven partly by marketing, advertising, and flavors promoted by e-cigarette producer firms. The current study aim is to examine the potential variation in the relative research volume on vaping products between countries with highest level of tobacco banning internet advertising and those with no ban on tobacco internet advertising. Method: We used weekly relative search volume (RSV) data produced by Google Trends (GT). A total of eight countries included in the study, Saudi Arabia, United Arab Emirates, United States, Ireland, New Zealand, Canada, United Kingdom, and Australia. The countries were regrouped into ban in internet advertising countries, and the no bans in internet advertising countries. Results: The trend test indicates a statistically significant upward trend in GT vaping search volume across all the included countries. However, the percentage increase of search volume for the ads no ban group was higher than ban group. There is a statistically significant difference in median between the two group, ( P < 0.0007), (median, 6.25 [IQR, 0.103] for the ban in internet advertising countries vs (median 7.5 Interquartile Ranges (IQR), [0.542] for the no bans on internet advertising countries. Conclusion: Countries with stringent bans on online tobacco advertising have shown lower levels of vaping-related search interest, indicating the potential effectiveness of such regulations. Our research underscores the importance of emphasizing comprehensive bans on tobacco advertising and sales online could help mitigate the upward trend in vaping interest.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".