Socioeconomic Differences in Exposure to Tobacco Smoke Pollution (TSP) in Bangladeshi Households with Children: Findings from the International Tobacco Control (ITC) Bangladesh Survey
Bibliographic record
Abstract
This study assessed the pattern of exposure to tobacco smoke pollution (TSP; also known as, secondhand smoke) in Bangladeshi households with children and examined the variations in household smoking restrictions and perception of risk for children's exposure to TSP by socioeconomic status. We interviewed 1,947 respondents from Bangladeshi households with children from the first wave (2009) of the International Tobacco Control (ITC) Bangladesh Survey. 43.5% of the respondents had complete smoking restrictions at home and 39.7% were very or extremely concerned about TSP risk to children's health. Participants with lower level of education were significantly less likely to be concerned about the risk of TSP exposure to children's health and less likely to adopt complete smoking restrictions at home. Logistic regression revealed that the predictors of concern for TSP exposure risk were educational attainment of 1 to 8 years (OR = 1.94) or 9 years or more (OR = 4.07) and being a smoker (OR = 0.24). The predictors of having complete household smoking restrictions were: urban residence (OR = 1.64), attaining education of 9 years or more (OR = 1.94), being a smoker (OR = 0.40) and being concerned about TSP exposure risk to children (OR = 3.25). The findings show that a high proportion of adults with children at home smoke tobacco at home and their perceptions of risk about TSP exposure to children's health were low. These behaviours were more prevalent among rural smokers who were illiterate. There is a need for targeted intervention, customized for low educated public, on TSP risk to children's health and tobacco control policy with specific focus on smoke-free home.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".