An examination of quitting smoking as a reason for vaping by the type of nicotine vaping device used most often among adults who smoke and vape: Findings from the Canada, England and the United States 2020 ITC Smoking and Vaping Survey
Bibliographic record
Abstract
Several nicotine vaping product (NVP) device types are available to consumers, and many people who smoke report vaping to help them quit. This study included data from the Wave 3 (2020) ITC Smoking and Vaping Survey in the US, Canada, and England and included 2324 adults who were smoking cigarettes and vaping at least weekly. Device types currently used most often (disposables, cartridges/pods, or tank systems) were assessed using weighted descriptive statistics. Multivariable regression analyses were used to compare differences between respondents who reported vaping to quit smoking ('yes' vs. 'no/don't know') by device type, overall and by country. Overall, 71.3% of respondents reported vaping to help them quit smoking, with no country differences (p = 0.12). Those using tanks (78.7%, p < 0.001) and cartridges/pods (69.5%, p = 0.02) were more likely to report this reason for vaping than those using disposables (59.3%); respondents using tanks were also more likely than those using cartridges/pods (p = 0.001) to report this reason. By country, respondents in England using cartridges/pods or tanks (vs. disposables) were more likely to report vaping to quit smoking (with no difference between cartridges/pods and tanks). In Canada, respondents using tanks were more likely to report vaping to quit smoking than those using cartridges/pods or disposables (no difference between disposables and cartridges/pods). No significant differences by device type were found in the US. In conclusion, most adult respondents who smoked and vaped reported using either cartridges/pods or tanks, which were associated with greater odds of vaping for the purpose of quitting smoking versus disposables, with some country variations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".