Has growth in electronic cigarette use by smokers been responsible for the decline in use of licensed nicotine products? Findings from repeated cross-sectional surveys
Bibliographic record
Abstract
BACKGROUND: The rise in electronic cigarette use by smokers may be responsible for the decreased use of licensed nicotine products and/or increased overall use of non-tobacco nicotine-containing products. This paper reports findings from the Smoking Toolkit Study (STS) tracking use of electronic cigarettes and licensed nicotine products to address this issue. METHODS: Data were obtained from monthly surveys involving 14 502 cigarette smokers in England between March 2011 and November 2014. Smokers were asked about their use of electronic cigarettes and licensed nicotine products. RESULTS: Prevalence of electronic cigarette use increased rapidly from 2.2% (95% CI 1.4% to 3.2%) in quarter 2 of 2011 to 20.8% (95% CI 18.3% to 23.4%) in quarter 3 of 2013, after which there was no change. Prevalence of licensed nicotine product use in smokers remained stable from quarter 2 of 2011 (17.4%, 95% CI 15.3% to 19.8%) to quarter 3 of 2013 (17.9%, 95% CI 15.62% to 20.5%), and thereafter declined steadily to 7.9% (95% CI 6.0% to 10.4%). Prevalence of use of any product was stable to quarter 1 of 2012, after which it increased from 18.5% (95% CI 16.3% to 21.0%) to 33.3% (95% CI 30.4% to 36.3%) in quarter 3 of 2013, and then decreased to 22.7% (95% CI 19.3% to 26.3%). CONCLUSIONS: The shapes of trajectories since 2011 suggest that electronic cigarettes are probably not responsible for the decline in use of licensed nicotine products. Electronic cigarettes appear to have increased the total market for use of non-tobacco nicotine-containing 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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".