A single-blind clustered randomised controlled trial of daily record-keeping for reducing smoking tobacco expenditure among adult male household heads in rural Bangladesh
Bibliographic record
Abstract
Abstract Introduction This study aims to assess the impact of a behavioural intervention, in the form of a self-monitoring record-keeping logbook, in reducing smoking tobacco expenditure amongst adult male household heads in rural Bangladesh. Method The experiment was designed as a single-blind clustered randomised controlled trial utilising two-stage random sampling. A total of 650 adult male household heads were sampled from 16 chars (riverine islands) from Gaibandha, Northern Bangladesh, with eight chars in treatment and control groups each, between November 2018 and January 2019. The intervention consisted of a logbook to record daily smoking tobacco intake for 4 weeks provided only to participants in treatment chars (n = 332) while households in control chars received nothing (n = 318). Results Final analysis was conducted using 222 and 210 households in the treatment and control chars respectively. The logbook intervention had a significant impact (P-value = 0.040) on reducing daily tobacco expenditure by 14% (α = 95%; CI: −0.273, −0.008) for the treatment group relative to the control group based on a difference-in-difference estimator. This is equivalent to a reduction of 20 cigarettes or 140 bidis smoked in a month. Conclusion Our minimal contact intervention successfully induced a reduction in smoking tobacco expenditure, which could effectively be incorporated with existing programs in the char regions of 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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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) | 0.008 | 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".