The Regulatory Environment and Cost of Electronic Cigarettes in Italy, 2014-2015, Influenced their Use for Quitting
Bibliographic record
Abstract
Yong et al.1 found that in 2010–2014 smokers who used e-cigarettes (ECs) for quitting in less restrictive EC policy environments were more likely (OR = 1.95; p < .01) to report sustained 30-day abstinence compared to unassisted quitting, whereas in more restrictive regulatory environments, EC users were less likely compared to unaided (OR = 0.36; p < .01), and users of approved therapies were more likely to report abstinence compared to EC users. In this letter we will discuss the EC regulatory environment in Italy, taking into account recent findings on EC use to quit in a representative sample of Italian population, 2014–2015.2 The EC regulatory environment was not restrictive up to 2014: ECs were and are still sold as consumer products, no rules on EC use in smoke-free public areas were implemented; advertising was allowed.3 Then a EC ban in schools, and a sale ban to minors were introduced at the end of 2013. The environment dramatically changed from 2015 onwards; tax on e-liquid became €4.50 per 10 ml.4 The cost for consumers increased by 150%, and EC market in Italy dropped from €450 million with 4500 stores in 2013, to about 190 million with 1500 stores in 2015. Consumers tried to circumvent tax increase purchasing e-liquid online.4 Current EC prevalence in Italy decreased from 2.2% in the first 2014 quarter to 1.3% in the second 2015 quarter (p < .01), and then it stabilized at 1.6% at the end of 2015: about 700000 Italian adults regularly used ECs in 2015.5
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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 | 0.006 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".