Can visual nudges reduce smoking tobacco expenditure? Evidence from a clustered randomized controlled trial in rural Bangladesh
Bibliographic record
Abstract
Abstract Introduction A household-level constant visual deterrent advocacy campaign to reduce tobacco intake was conducted in rural Bangladesh. Aims To evaluate smoking tobacco expenditure by campaign components. Methods We conducted a single-blind clustered randomized controlled trial on 630 adult male household heads from 16 chars (riverine islands) in rural northern Bangladesh, between November 2018 and January 2019. Intervention allotment was randomized at the char level to minimize spillovers, with 8 chars in treatment and control groups each. The intervention provided households in treatment chars (n = 323) with two visual warning posters, detailing the health effects of tobacco on oneself and external actors, to be hung inside the household for 4 weeks. Households in control chars (n = 307) received nothing. Reported daily smoking (log) tobacco expenditure values were the primary outcome of interest. Results Final analysis was conducted using 251 and 210 smokers in treatment and control chars respectively. The intervention reduced relative smoking tobacco expenditure by 12.8% (95% CI −31.45 to 5.81) but was not statistically significant (P-value = 0.163). Weak to moderate emotional reactions to the posters was identified as a reason for the statistical insignificance. Conclusion For a visual anti-tobacco intervention to have a substantial impact, it must induce strong emotional responses.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".