Characteristics and changes over time of nicotine vaping products used by vapers in the 2016 and 2018 ITC Four Country Smoking and Vaping Surveys
Bibliographic record
Abstract
OBJECTIVES: on types of NVPs used and examined changes in NVP features used over 18 months in four countries with differing regulatory environments. DESIGN: Data are from 4734 adult current vapers in Australia, Canada, England and the USA from Waves 1 (2016) and 2 (2018) of the International Tobacco Control Four Country Smoking and Vaping Survey. NVP characteristics included device description, adjustable voltage, nicotine content and tank size. Longitudinal analyses (n=1058) assessed movement towards or away from more complex/modifiable NVPs. A logistic regression was used to examine factors associated with changes in device description from 2016 to 2018. RESULTS: Like 2016, box-tanks were the most popular NVP (37.3%) in all four countries in 2018. Over 80% of vapers continued using the same NVP and nicotine content between waves, though movement tended towards more complex/modifiable devices (14.4% of vapers). Box-tank users, exclusive daily vapers and older vapers were most likely to continue using the same device description. Certain NVPs and features differed by country, such as higher nicotine contents in the USA (11.5% use 21+ mg/mL) and greater device stability over time in Australia (90.8% stability). CONCLUSIONS: Most vapers continued using the same vaping device and features over 18 months. Differences in NVP types and features were observed between countries, suggesting that differing NVP regulations affect consumer choices regarding the type of vaping device to use.
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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 | 0.000 | 0.000 |
| 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".