Knowledge, opinions and compliance related to the 100% smoke-free law in hospitality venues in Kampala, Uganda: cross-sectional results from the <i>KOMPLY Project</i>
Bibliographic record
Abstract
Objective This study evaluated knowledge, opinions and compliance related to Uganda’s comprehensive smoke-free law among hospitality venues in Kampala Uganda. Design This multi-method study presents cross-sectional findings of the extent of compliance in the early phase of Uganda’s comprehensive smoke-free law (2 months postimplementation; pre-enforcement). Setting Bars, pubs and restaurants in Kampala Uganda. Procedure and participants A two-stage stratified cluster sampling procedure was used to select hospitality sites stratified by all five divisions in Kampala. A total of 222 establishments were selected for the study. One hospitality representative from each of the visited sites agreed to take part in a face-to-face administered questionnaire. A subsample of hospitality venues were randomly selected for tobacco air quality testing (n=108). Data were collected between June and August 2016. Outcome measures Knowledge and opinions of the smoke-free law among hospitality venue staff and owners. The level of compliance with the smoke-free law in hospitality venues through: (1) systematic objective observations (eg, active smoking, the presence of designated smoking areas, ‘no smoking’ signage) and (2) air quality by measuring the levels of tobacco particulate matter (PM 2.5 ) in both indoor and outdoor venues. Results Active smoking was observed in 18% of venues, 31% had visible ‘no smoking’ signage and 47% had visible cigarette remains. Among interviewed respondents, 57% agreed that they had not been adequately informed about the smoke-free law; however, 90% were supportive of the ban. Nearly all respondents (97%) agreed that the law will protect workers’ health, but 32% believed that the law would cause financial losses at their establishment. Indoor PM 2.5 levels were hazardous (267.6 µg/m 3 ) in venues that allowed smoking and moderate (29.6 µg/m 3 ) in smoke-free establishments. Conclusions In the early phase of Uganda’s smoke-free law, the level of compliance in hospitality venues settings in Kampala was suboptimal. Civil society and the media have strong potential to inform and educate the hospitality industry and smokers of the benefits and requirements of the smoke-free law.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".