Factors associated with future intentions to use personal vaporisers among those with some experience of vaping
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Personal vaporisers (PV), including e-cigarettes, may be a harm reduction strategy for tobacco control. This study aims to identify factors associated with future intentions to vape among smokers and ex-smokers in Australia and the UK. DESIGN AND METHODS: Cross-sectional data of smokers and ex-smokers (n = 1199, mean age = 45.3 years, 44.8% male), collected in 2014/2015 and divided into four subgroups: smoking past vapers (SPV), smoking vapers (SV), ex-smoking past vapers (ESPV) and ex-smoking vapers (ESV), from the International Tobacco Control Australia and UK surveys were analysed by using regression models. RESULTS: Higher vaping satisfaction increased vaping intentions for all groups except ESPV. Perceiving PVs as less harmful predicted intentions to vape for all groups except ESV. The importance of PVs for stopping smoking predicted lower intentions to continue vaping for SV, but higher intentions to initiate vaping for SPV. The importance of PVs for cutting down smoking was a positive predictor only for SPV. Among ex-smokers, importance for maintaining not smoking was a positive predictor for ESPV, but not for ESV. The importance of perceiving vapour being less harmful also depended on vaping status for ex-smokers. The only country interaction was that only in the UK was perceiving PVs as less harmful associated with intention among SPV. DISCUSSION AND CONCLUSION: Factors influencing intentions vary by smoking and/or vaping status, with greater differences between the ex-smoker subgroups. This is consistent with PVs being seen as a way of managing smoking, rather than something that has intrinsic value, for all except the ex-smoking vapers. [Ma BH, Yong H-H, Borland R, McNeill A, Hitchman SC. Factors associated with future intentions to use personal vaporisers among those with some experience of vaping. Drug Alcohol Rev 2017;00:000-000].
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".