Perceived safety and effectiveness of electronic cigarettes among Malaysian adults and public support for regulation
Bibliographic record
Abstract
Introduction: Electronic cigarettes have been used as a harm reduction method towards tobacco cessation. Malaysian government has enforced a strict policy to regulate the sale of electronic cigarette products because its liquid contains nicotine. Objectives: This study aimed to explore the general public’s perception towards electronic cigarette use. Public support towards electronic cigarette regulation was also examined. Materials and Methods: Data were obtained from the National E-Cigarette Survey (NECS) 2016, which used a multi-stage stratified cluster sampling household survey representing all Malaysian adults aged 18 years old. A cross-sectional survey was conducted among a total of 4,288 adults. Results: Majority were aged 25–44 years old (44%), completed at least secondary education (69%), of Malay ethnicity (73%), and married (68%). Majority (88.1%) have never used electronic cigarette. A quarter (25.5%) perceived electronic cigarette helps people quit cigarette smoking, while 20.3% perceived electronic cigarette helps people to maintain cigarette abstinence. About 85% believed that electronic cigarette use do not help in improving breathing and coughing. Majority (91.8%) disagreed that electronic cigarettes should be allowed in places where tobacco smoking is banned. Thus, 63.4% agreed that electronic cigarette should be banned completely rather than regulated. Conclusion: Majority of general public had negative perception about electronic cigarette use.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".