E-cigarette prevalence among Malaysian adults and types and flavors of e-cigarette products used by cigarette smokers who vape: Findings from the 2020 ITC Malaysia Survey
Bibliographic record
Abstract
INTRODUCTION: E-cigarettes (ECs) have become increasingly common in many countries, including Malaysia. The prevalence of EC use increased in Malaysia from 0.8% in 2011 to 4.9% in 2019. Three quarters of Malaysian EC users also smoke combustible cigarettes, and the prevalence of EC use among Malaysian smokers in 2014 was consistent with the prevalence of use among smokers from Canada and the US in 2016. The purpose of this study was to estimate the prevalence of EC use among Malaysian adults aged ≥18 years in 2020 and the types of EC products and flavors used by cigarette smokers who also used ECs at least monthly. METHODS: Data came from 1253 adults aged ≥18 years who participated in the 2020 International Tobacco Control Malaysia Wave 1 Survey. Weighted descriptive statistics were used to estimate the prevalence of adults who reported ever using ECs and the prevalence who used ECs either monthly, weekly, or daily. The types of EC products and flavors used were compared by frequency of EC use among current smokers who used ECs at least monthly (n=459). RESULTS: Overall, 5.4% (95% CI: 3.7-7.5) of Malaysian adults reported using ECs on a daily basis in 2020. Among current cigarette smokers who used ECs daily, 81.0% (95% CI: 72.5-87.7) used nicotine in their ECs, 46.2% (95% CI: 37.8-54.7) used pre-filled ECs, and 60.4% (95% CI: 51.9-68.6) reported being somewhat/very addicted to ECs. The most common EC flavors were fruit, coffee, and menthol/ mint. CONCLUSIONS: Continued surveillance of EC use is necessary to monitor EC use in non-tobacco using populations while longitudinal research is needed to determine the extent to which ECs are, or are not, related to quitting smoking.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".