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Record W4367045638 · doi:10.1371/journal.pone.0283650

Compliance with smoke-free legislation in public places: An observational study in a northeast city of Bangladesh

2023· article· en· W4367045638 on OpenAlexaff
Saifur Rahman Chowdhury, Tachlima Chowdhury Sunna, Dipak Chandra Das, Mahfuzur Rahman Chowdhury, Howlader Mohammad Miraz Mahmud, Ahmed Hossain

Bibliographic record

VenuePLoS ONE · 2023
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsMcMaster UniversityImpact
FundersBloomberg PhilanthropiesJohns Hopkins University
KeywordsEnvironmental healthTobacco controlChecklistLegislationPublic healthObservational studyTobacco smokeSmokeSmokeless tobaccoCross-sectional studyMedicineGeographySocioeconomicsBusinessTobacco useLawPopulationPsychologyPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Bangladesh is one of the highest tobacco-consuming countries in the world, with a large number of adult users of a variety of smoked and/or smokeless tobacco products. Bangladesh tobacco control act prohibits smoking in public places and requires the owners of public places to display 'no smoking' signages. OBJECTIVES: The objective of this study was to assess the level of compliance with the tobacco control act (smoke-free laws) in public places in a northeast city of Bangladesh. METHODS: This cross-sectional study was conducted between June 1 and August 25, 2020, across 673 public places in Sylhet city, Bangladesh. The data was collected using a structured observational checklist that included variables such as the presence of active smoking, the presence of designated smoking areas, the display of 'no smoking' signages, evidence of recent smoking such as ashes, butts/bidi ends, and the presence of smoking aids. RESULTS: Among 673 public places, a total of 635 indoor locations and 313 outdoor locations were observed. Only 70 (11%) indoor locations were found to be in good compliance, and 388 (61.1%) indoor locations were found to be in moderate compliance with smoke-free laws. On the other hand, only 5 (1.6%) outdoor locations were in good compliance, and 63 (20.1%) outdoor locations were in moderate compliance with smoke-free laws. The overall compliance with smoke-free laws at indoor locations was 52.7%, and at outdoor locations was 26.5%. The highest compliance was observed at healthcare facilities (58.6%) and the least at transit points (35.7%) for indoor locations. In outdoor locations, the highest compliance was observed at offices and workplaces (37.1%) and the least at transit points (2.2%). Higher active smoking was observed in public places where there was an absence of 'no smoking' signage and the presence of points of sale (POSs) (p-value <0.05). Further, higher active smoking was observed in places where any smoking aids, cigarette butts, bidi ends, or ashes were present (p-value <0.05). CONCLUSION: This study found moderate compliance at indoor locations and very low compliance at outdoor locations. The government should focus more on implementing smoke-free laws in all kinds of public places, particularly at most frequently visited places and transit sites. 'No smoking' signages should be displayed per legislation across all public places. Policymakers should consider the prohibition of POS in/around a public place as it has a positive effect on smoking.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score0.150

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.476
GPT teacher head0.359
Teacher spread0.117 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations16
Published2023
Admission routes1
Has abstractyes

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