Trends in E-Cigarette Awareness, Trial, and Use Under the Different Regulatory Environments of Australia and the United Kingdom
Bibliographic record
Abstract
INTRODUCTION: E-cigarettes (ECs) have gained significant attention in recent years. They have been introduced in jurisdictions with divergent existing laws that affect their legality. This provides the opportunity for natural experiments to assess effects of such laws in some cases independent of any formulated government policy. We compare patterns of EC awareness and use over a 3 year period in Australia where laws severely restrict EC availability, with awareness and use in the United Kingdom where ECs are readily available. METHODS: Data analyzed come from Waves 8 and 9 (collected in 2010 and 2013, respectively) of the International Tobacco Control surveys in Australia and the United Kingdom (approximately 1,500 respondents per wave per country). RESULTS: Across both waves, EC awareness, trial, and use among current and former smokers were significantly greater in the United Kingdom than in Australia, but all 3 of these measures increased significantly between 2010 and 2013 in both countries, and the rate of increase was equivalent between countries. Seventy-three percent of U.K. respondents reported that their current brands contained nicotine as did 43% in Australia even though sale, possession and/or use of nicotine-containing ECs without a permit are illegal in Australia. EC use was greater among smokers in both countries, at least in part due to less uptake by ex-smokers. CONCLUSIONS: EC awareness and use have risen rapidly between 2010 and 2013 among current and former smokers in both Australia and the United Kingdom despite different EC regulatory environments. Substantial numbers in both countries are using ECs that contain nicotine.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".