Who Are More Likely to Have Quit Intentions among Malaysian Adult Smokers? Findings from the 2020 ITC Malaysia Survey
Bibliographic record
Abstract
Increasing quitting among smokers is essential to reduce the population burden of smoking-related diseases. Smokers' intentions to quit smoking are among the strongest predictors of future quit attempts. It is therefore important to understand factors associated with intentions to quit, and this is particularly important in low- and middle-income countries, where there have been few studies on quit intentions. The present study was conducted to identify factors associated with quit intentions among smokers in Malaysia. Data came from the 2020 International Tobacco Control (ITC) Malaysia Survey, a self-administered online survey of 1047 adult (18+) Malaysian smokers. Smokers who reported that they planned to quit smoking in the next month, within the next six months, or sometime beyond six months were classified as having intentions to quit smoking. Factors associated with quit intentions were examined by using multivariable logistic regression. Most smokers (85.2%) intended to quit smoking. Smokers were more likely to have quit intentions if they were of Malay ethnicity vs. other ethnicities (adjusted odds ratio (AOR) = 1.82, 95% confidence interval (CI) = 1.03-3.20), of moderate (AOR = 2.11, 95% CI = 1.12-3.99) or high level of education vs. low level of education (AOR = 1.97, 95% CI = 1.04-3.75), if they had ever tried to quit smoking vs. no quit attempt (AOR = 8.81, 95% CI = 5.09-15.27), if they received advice to quit from a healthcare provider vs. not receiving any quit advice (AOR = 3.78, 95% CI = 1.62-8.83), and if they reported worrying about future health because of smoking (AOR = 3.11, 95% CI = 1.35-7.15 (a little worried/moderately worried vs. not worried); AOR = 7.35, 95% CI = 2.47-21.83 (very worried vs. not worried)). The factors associated with intentions to quit smoking among Malaysian smokers were consistent with those identified in other countries. A better understanding of the factors influencing intentions to quit can strengthen existing cessation programs and guide the development of more effective smoking-cessation programs 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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".