Self-Reported of Awareness, Trial and Current Use of Electronic Cigarettes (ECS) Among of Adult Smokers in Malaysia
Bibliographic record
Abstract
Background: In many parts of the world, E-cigarette (EC) devices are becoming popular and an increasing trend in its usage especially among young people and adult smokers. Aim: To examine the self-reported awareness, trial and current use of ECs among current smokers and to determine the predictors associated with the outcomes. Methods: A total of 40,000 current smokers aged 18 years and above were recruited through intercept face-to-face interview in wave 1 (May-September 2016), wave 2 (December 2016-April 2017) and wave 3 (May-August 2017). The samples were drawn from 5 streets randomly (two in hotspot and three in nonhotspot) within stratified urban and rural areas in 14 states of Malaysia. Respondents were recruited using systematic sampling. Every fifth person passing an interview station in each street was approached. Descriptive analysis and multivariate logistic regression were applied by using PASW 18. Results: Overall, 93% were aware of ECs, 19.9% were ECs trials and 14.6% were current users. Multivariate logistic regression shows that those states with no ban of ECs sales were more likely and significantly associated with these outcomes compared with those in states that this device has already been banned. Those who believe that ECs are “less harmful” (AOR=6.28; 95% CI=5.79-6.81, P < 0.001; AOR=4.84; 95% CI=4.12-5.69, P < 0.001) and “equally harmful” (AOR=2.06; 95% CI=1.91-2.22, P < 0.001; AOR=2.25; 95% CI=1.93-2.62, P < 0.001) were significantly associated with EC trials and current use of ECs respectively. In addition, intention to quit smoking (AOR=2.91; 95% CI=2.72-3.13) was also directly associated with EC trials. Conclusion: Awareness, trials and current use of ECs are likely due to the banning regulation implemented in specific states and strong belief that ECs is less harmful to health. Hence, this should be considered in the formulation of ECs policy in Malaysia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".