Reasons for using e-cigarettes and support for e-cigarette regulations: Findings from the 2020 ITC Malaysia Survey
Bibliographic record
Abstract
INTRODUCTION: Malaysia has the largest e-cigarette (EC) market in Southeast Asia, and it has been estimated that 17% of adult daily cigarette smokers also used ECs on a daily basis in 2020. However, few studies have examined the reasons people use ECs in Malaysia. This cross-sectional study of adult cigarette smokers from Malaysia assessed reasons for EC use and their support for key proposed EC regulations. METHODS: Data are from the 2020 International Tobacco Control (ITC) Malaysia Wave 1 Survey of adult (aged ≥18 years) smokers who reported that they used ECs at least monthly (N=459 out of 1047 smokers). Weighted analyses were conducted on EC users' reasons for using ECs and their support for various EC regulations. RESULTS: Smokers who used ECs at least monthly were more likely to be male, aged 25-39 years, of Malay ethnicity, married, more highly educated, and living in Peninsular Malaysia. Smokers who used ECs daily reported using ECs to reduce the number of cigarettes smoked (91.3%), pleasant taste (90.1%), to quit smoking (87.9%), and enjoyment (87.5%). Smokers who used ECs less than daily reported using ECs for their pleasant taste (weekly 89.4%, monthly 87.5%), curiosity (weekly 79.5%, monthly 88.8%), being offered EC by someone (weekly 76.3%, monthly 81.6%), and to reduce the number of cigarettes smoked (weekly 76.2%, monthly 77.6%). Smokers who also used ECs were most likely to support EC regulations requiring a minimum purchasing age (88.3%) and limiting nicotine concentration (79.6%), and least likely to support regulations banning EC fruit and candy flavors (27.1%). CONCLUSIONS: The most prevalent reasons for using ECs in Malaysia are comparable to those of other ITC countries, including Canada, US, England, and Australia. An understanding of use patterns of ECs, especially their interaction with cigarettes, are important in developing evidence-based regulations in Malaysia.
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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 | 0.004 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".