Daily users of both smoked and smokeless tobacco and their efforts to quit
Bibliographic record
Abstract
Objective This study estimates the prevalence of daily dual users (DDUs) (i.e., used both smoked and smokeless tobacco (SLT) daily) of tobacco in Bangladesh and identifies associated factors ; examines approaches they used to quit tobacco use and identifies past attempts and future intention to quit.Methods Data of current tobacco users (n=9,370) collected in two rounds of the Global Adult Tobacco Survey were analyzed. Logistic regressions were used to identify factors associated with DDUs, and Poisson regressions to identify DDUs’ previous attempts and future intentions to quit.Results Among the current users, the prevalence of DDUs decreased from 10.7% in 2009 to 6.4% in 2017. Almost half of the DDUs tried to quit smoking and a quarter to quit SLT during 12-month before the survey. DDUs were more likely than non-DDUs to make a quit attempt (adjusted prevalence ratio (aPR):1.28, 95%Confidence interval (95%CI):1.09–1.50) and intend to quit in the future (aPR:1.13, 95%CI:1.03–2.25). Most of those who attempted to quit received no cessation treatment, and some started using SLT.Conclusions The prevalence of DDUs decreased recently. DDUs were more likely than non-DDUs to report past attempts and future intentions to quit. Cessation treatments should be made available and affordable to expedite tobacco control measures.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".