Awareness of Tobacco-Related Health Harms among Vulnerable Populations in Bangladesh: Findings from the International Tobacco Control (ITC) Bangladesh Survey
Bibliographic record
Abstract
This study assessed the knowledge of the harmful effects of tobacco use among vulnerable populations in Bangladesh and whether vulnerability was associated with the presence of complete home smoking bans. Data came from Wave 3 (2011-2012) of the International Tobacco Control (ITC) Bangladesh Survey, a nationally-representative survey of 3131 tobacco users and 2147 non-users. Socio-demographic measures of disadvantage were used as proxy measures of vulnerability, including sex, residential location, education and income. Outcome measures were awareness of the harmful effects of (a) cigarette smoking and (b) smokeless tobacco use and (c) whether respondents had complete smoking bans in their homes. Logistic regression was used to examine whether the adjusted prevalence of each outcome differed by socio-demographic proxies of vulnerability. Smaller percentages of women, the illiterate, urban slum residents and low-income Bangladeshis were aware of the health harms of tobacco. These vulnerable groups generally had lower odds of awareness compared to the least disadvantaged groups. Incomplete knowledge of tobacco's harms may prevent vulnerable groups from taking steps to protect their health. Development goals, such as increasing literacy rates and empowering women, can complement the goals of WHO's Framework Convention on Tobacco Control.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".