Motivations for Current E-Cigarette Use Among Adult Smokers in Malaysia
Bibliographic record
Abstract
Background: Recommendations from WHO regarding the comprehensive ban on tobacco advertisements, promotions and scholarships (TAPS) have recently caused tobacco companies in shifting their market strategies to focus on promoting unregulated devices such as electronic cigarettes (ECs) and vapes. Aim: This study aims to explore the motivations of EC use among dual users and its associations with sociodemographic factors and smoking related characteristics under different regulatory environments in Malaysia. Methods: A total of 5823 dual users was collected using a multistage sampling study design. Data collection was conducted through intercept face-to-face interviews within 3 waves; 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 locations) within stratified urban and rural areas in 14 states of Malaysia. Every fifth person passing an interview station in each street was approached. All statistical tests were conducted using PASW 18. Results: The top four self-reported motivations of using EC by dual users included “tasted better than conventional cigarettes” (85.1%), “to reduce the number of cigarettes smoked” (72.9%), “to quit smoking” (63.5%) and “increase price of cigarette taxes” (62.9%). Based on a multivariate analysis, respondents from states that have no current restrictions toward the sale and usage of ECs and who use ECs less than weekly (Adj.OR=2.54; 95% CI=1.93-3.34), weekly (Adj.OR=2.22; 95% CI=1.72-2.85) and daily users (Adj.OR= 1.77; 95% CI=1.36-2.31) were more likely to use ECs due to its better taste as compared with those who use ECs less than once a month. In states that have banned the sales and usage of ECs, there was a significant association between daily users of ECs and the four primary motivations. It was found that an increasing amount of cigarettes smoked per day (Adj.OR=2.46; 95% CI=1.59-3.81) had significantly influenced smokers in these states to more likely use ECs due to the increase price of cigarette tax. Conclusion: This study was conducted to show associations between smoking characteristics, EC use and self-reported motivations under different regulatory environments in Malaysia. Frequency of EC use was significantly associated with these self-reported motivations. Further research should be conducted to monitor EC use by Malaysians as well as to contribute to the formulation of EC 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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".