Where Do Vapers Buy Their Vaping Supplies? Findings from the International Tobacco Control (ITC) 4 Country Smoking and Vaping Survey
Bibliographic record
Abstract
Aim: This study examines where vapers purchase their vaping refills in countries having different regulations over such devices, Canada (CA), the United States (US), England (EN), and Australia (AU). Methods: Data were available from 1899 current adult daily and weekly vapers who participated in the 2016 (Wave 1) International Tobacco Control Four Country Smoking and Vaping. The outcome was purchase location of vaping supplies (online, vape shop, other). Adjusted odds ratios and 95% confidence intervals were reported for between country comparisons. Results: Overall, 41.4% of current vapers bought their vaping products from vape shops, 27.5% bought them online, and 31.1% from other retail locations. The vast majority of vapers (91.1%) reported using nicotine-containing e-liquids. In AU, vapers were more likely to buy online vs other locations compared to CA (OR = 6.4, 2.3–17.9), the US (OR = 4.1, 1.54–10.7), and EN (OR = 7.9, 2.9–21.8). In the US, they were more likely to buy from vape shops (OR = 3.3, 1.8–6.2) or online (OR = 1.9, 1.0–3.8) vs other retail locations when compared to those in EN. In CA, vapers were more likely to purchase at vape shops than at other retail locations when compared to vapers in EN (5.9, 3.2–10.9) and the US (1.87, 1.0–3.1). Conclusions: The regulatory environment and enforcement of such regulations appear to influence the location where vapers buy their vaping products. In AU, banning the retail sale of nicotine vaping products has led vapers to rely mainly on online purchasing sources, whereas the lack of enforcement of the same regulation in CA has allowed specialty vape shops to flourish.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".