Adherence to smoke-free laws at retail points-of-sale and associated factors in 10 cities in Ethiopia
Bibliographic record
Abstract
OBJECTIVES: The objective of this study was to assess adherence to smoke-free laws at points-of-sale (PoS), that is, retail outlets that sell various goods including tobacco products, and to identify predictors of low adherence. METHODS: This cross-sectional observational study was conducted in December 2022 across 10 Ethiopian cities covering 1323 PoS such as regular shops, permanent kiosks, khat shops, supermarkets and minimarkets. Sampling was performed using a two-stage cluster design, with random selection of PoS. Data were collected using checklists through covert observations. Logistic regression identified predictors of low adherence. RESULTS: More than half of PoS (52.5%) showed good adherence, 23.2% moderate, 20.8% poor or none and only 3.4% met full adherence. Supermarkets/minimarkets had the highest rates of good or full adherence (83.9%), while kiosks and khat shops had the lowest (40.7% and 35.4%, respectively) rates of good or full adherence. Low adherence was higher in kiosks (adjusted OR (aOR)=7.02, 95% CI: 3.76 to 13.13) and khat shops (aOR=6.26, 95% CI: 3.48 to 11.26). Low adherence was also observed in Semera-Logia (aOR=21.27, 95% CI: 13.26 to 34.12) and Gambella (aOR=12.07, 95% CI: 7.64 to 19.08). Predictors of indoor smoking included being a khat shop (aOR=3.13, 95% CI: 1.29 to 7.60), being located in Semera-Logia (aOR=8.47, 95% CI: 3.49 to 26.54), presence of outdoor smoking (aOR=3.38, 95% CI: 2.07 to 5.51) and lighters (aOR=5.26, 95% CI: 3.05 to 9.06). CONCLUSIONS: The study highlights enforcement gaps at PoS, particularly in khat shops, kiosks, and in Semera-Logia and Gambella cities. Strengthening smoke-free law implementation requires region-specific interventions for high-risk areas and retail outlets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".