Patterns of Use of Vaping Products among Smokers: Findings from the 2016–2018 International Tobacco Control (ITC) New Zealand Surveys
Bibliographic record
Abstract
Alternative nicotine products like e-cigarettes could help achieve an end to the epidemic of ill health and death caused by smoking. However, in-depth information about their use is often limited. Our study investigated patterns of use of e-cigarettes and attitudes and beliefs among smokers and ex-smokers in New Zealand (NZ), a country with an 'endgame' goal for smoked tobacco. Data came from smokers and ex-smokers in Waves 1 and 2 of the International Tobacco Control (ITC) NZ Survey (Wave 1 August 2016-April 2017, 1155 participants; Wave 2, June-December 2018, 1020 participants). Trial, current and daily use of e-cigarettes was common: daily use was 7.9% among smokers and 22.6% among ex-smokers in Wave 2, and increased between surveys. Use was commonest among 18-24 years and ex-smokers, but was similar among Māori and non-Māori participants, and by socio-economic status. Most participants used e-cigarettes to help them quit or reduce their smoking. The most common motivating factor for use was cost and the most common barrier to use cited was that e-cigarettes were less satisfying than smoking. The findings could inform developing interventions in order to maximise the contribution of e-cigarettes to achieving an equitable smoke-free Aotearoa, and to minimise any potential adverse impacts.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".