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Record W3046061358 · doi:10.1017/jsc.2020.20

Can visual nudges reduce smoking tobacco expenditure? Evidence from a clustered randomized controlled trial in rural Bangladesh

2020· article· en· W3046061358 on OpenAlexaff
Adnan Fakir, Afraim Karim, Mutasim Billah Mubde, Mustahsin Aziz, Azraf Uddin Ahmad

Bibliographic record

VenueThe Journal of Smoking Cessation · 2020
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsTobacco controlRandomized controlled trialMedicineIntervention (counseling)Environmental healthPublic healthPsychiatryNursingSurgery

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.628

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.043
GPT teacher head0.326
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2020
Admission routes1
Has abstractyes

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