Determinants of intentions to quit smoking among adult smokers in Bangladesh: findings from the International Tobacco Control (ITC) Bangladesh wave 2 survey
Bibliographic record
Abstract
BACKGROUND: With about 22 million adult smokers, Bangladesh needs strong measures that would promote smoking cessation. Using data from Wave 2 of the International Tobacco Control (ITC) Survey, this study examined the factors associated with intention to quit smoking among Bangladeshi smokers. METHODS: Data from Wave 2 of the International Tobacco Control (ITC) Survey in Bangladesh, a face to face survey of adult smokers, were analysed. In the ITC survey, households were sampled using a stratified multistage design and interviewed using a structured questionnaire. RESULTS: = 2982), most were male (96 %), married (80 %), and Muslim (83 %); 33 % were illiterate and 54 % were aged below 40. Almost two-thirds were from areas outside Dhaka, 78 % smoked cigarettes exclusively; and 36 % had an intention to quit smoking in the future. This study identified several predictors, comparable to other international studies, of intention to quit smoking: area of residence, number of cigarettes smoked daily, previous quit attempt, visiting a doctor in the past, having child aged 5 or below at home, perceived benefit from quitting, being worried about own health, knowledge of SHS, not enjoying smoking and workplace smoking policy. CONCLUSIONS: These findings suggest that the prevalence of intention to quit smoking is lower among Bangladeshi smokers than those among smokers in developed countries. However, the factors relating to quit intentions among Bangladeshi smokers are comparable to those found in Western countries. Population based tobacco control programs and policies should consider these predictors in the design of interventions to increase quitting among smokers in Bangladesh.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".