Patterns and experiences of smoking, electronic cigarettes (vapes) and heated tobacco use among people who smoke or who recently quit
Bibliographic record
Abstract
AIM: The aim of this study is to understand patterns and experiences of smoking and electronic cigarette use, as well as related attitudes and behaviours among adults in Aotearoa New Zealand who smoke or recently stopped smoking. METHODS: We analysed data from the Evidence for Achieving Smokefree Aotearoa Equitably/International Tobacco Control New Zealand Survey (N=1,230), conducted between November 2020 and February 2021. RESULTS: Among people who smoked, 77.5% (95% confidence interval [CI] 74.0-80.8%) reported regretting having started smoking, 73.6% (95% CI 69.5-77.4) intended to quit, 87.3% (95% CI 84.1-89.9) reported being addicted to smoking and 86.3% (95% CI 83.3-88.8) had tried to quit smoking in the past. Among people who smoked, 24.8% (95% CI 21.3-28.6) used electronic cigarettes (ECs) daily and 4.6% (95% CI 3.3-6.6) used heated tobacco products (HTPs) daily. Among people who had recently stopped smoking, 33.4% (95% CI 25.6-42.2) used ECs daily and less than 1% used HTPs daily. CONCLUSION: High levels of regret for starting smoking, addiction and intent to quit smoking highlight the importance of implementing effective and equitable smokefree measures to prevent people from starting to smoke and to support people to stop smoking.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".