Comparison between Smokers and Smokeless Tobacco Users in Their Past Attempts and Intentions to Quit: Analysis of Two Rounds of a National Survey
Bibliographic record
Abstract
This study compares current tobacco smokers and smokeless tobacco (SLT) users in terms of their past quitting attempts and intentions to quit in the future, and identifies approaches used in their recent quitting attempts. Data (n = 14,498) of current tobacco users from two rounds of the Global Adult Tobacco Survey Bangladesh were analysed. Poisson regressions with robust variance were used to examine associations between the study factor and the two outcome variables. About half of smokers and a quarter of SLT users tried to quit during the 12 months before the survey. About two-thirds of smokers and half of SLT users intended to quit in the future. Smokers were more likely (adjusted prevalence ratio (aPR): 1.38, 95%CI: 1.24–1.53) than SLT users to have attempted to quit during the 12 months before the survey and to intend to quit in the future (aPR: 1.09, 95%CI: 1.02–1.16). The corresponding aPRs were even higher for dual users (smoked tobacco and used SLT). Future intention to quit for both smokers (aPR: 1.45, 95%CI: 1.38–1.53) and SLT users (aPR: 1.87, 95%CI: 1.76–1.98) was significantly associated with their past quitting attempts. Most of those who had attempted to quit did not receive any treatment. Proactive and tailored interventions to promote quitting and expansion of tobacco cessation methods are recommended.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".