A new classification system for describing concurrent use of nicotine vaping products alongside cigarettes (so‐called ‘dual use’): findings from the ITC‐4 Country Smoking and Vaping wave 1 Survey
Bibliographic record
Abstract
AIMS: To determine whether a simple combination of level of smoking and level of vaping results in a useful typology for characterizing smoking and vaping behaviours. METHODS: Cross-sectional data from adults (≥ 18 years) in the 2016 wave 1 ITC Four Country Smoking and Vaping Survey in the United States (n = 2291), England (n = 3591), Australia (n = 1376) and Canada (n = 2784) were used. Participants who either smoked, vaped or concurrently used both at least monthly were included and divided into eight groups based on use frequency of each product (daily, non-daily, no current use). This resulted in four concurrent use groups (predominant smokers, dual daily users, predominant vapers and concurrent non-daily users). These groups were compared with each other and with the four exclusive use groups, on socio-demographics, nicotine dependence, beliefs and attitudes about both products, and quit-related measures using data weighted to reference population surveys in each country. RESULTS: Of the sample, 10.8% were concurrent users, with daily smokers vaping non-daily (predominant smokers), constituting 51.6% of this group. All eight categories differed from other categories on at least some measures. Concurrent daily nicotine users reported higher levels of indicators of nicotine dependence, and generally more positive attitudes toward both smoking and vaping than concurrent non-daily users. Among daily nicotine users, compared with exclusive daily smokers, reports of interest in quitting were higher in all concurrent use groups. Dual daily users had the most positive attitudes about smoking overall, and saw it as the least denormalized, and at the same time were equally interested in quitting as other concurrent users and were most likely to report intending to continue vaping. CONCLUSIONS: In Australia, Canada, England and the United States in 2016, daily nicotine users differed considerably from non-daily nicotine users. Among daily nicotine users, dual daily users (those who smoke and vape concurrently) should be treated as a distinct grouping when studying relationships between smoking and vaping. The eight-level typology characterizing concurrent and exclusive use of smoking and vaping should be considered when studying both products.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".