Implementation of 100% smoke-free law in Uganda: a qualitative study exploring civil society’s perspective
Bibliographic record
Abstract
BACKGROUND: In 2016, Uganda became one of few sub-Saharan African countries to implement comprehensive national smoke-free legislation. Since the World Health Organisation recommends Civil Society Organisation's (CSO) involvement to support compliance with smoke-free laws, we explored CSOs' perceptions of law implementation in Kampala, Uganda, and the challenges and opportunities for achieving compliance. Since hospitality workers tend to have the greatest level of exposure to second-hand smoke, we focussed on implementation in respect to hospitality venues (bars/pubs and restaurants). METHODS: In August 2016, three months after law implementation, we invited key Kampala-based CSOs to participate in face-to-face semi-structured interviews. Interviews probed participants' perceptions about law implementation, barriers impeding compliance, opportunities to enhance compliance, and the role of CSOs in supporting law implementation. Interviews were recorded and transcribed. Qualitative content analysis was conducted using the interview transcripts. RESULTS: Fourteen individuals, comprising mainly senior managers from CSOs, participated and reported poor compliance with the smoke-free law in hospitality venues. Respondents noted that contributing factors included low awareness of the law amongst the general public and hospitality staff, limited implementation activities due to scarce resources and lack of coordinated enforcement. Opportunities for improving compliance included capacity building for enforcement agency staff, routine monitoring, rigorous enactment of penalties, and education about the smoke-free law aimed at hospitality venue staff and the general public. Allegations of tobacco industry misinformation were said to have undermined compliance. Civil Society Organisations saw their role as supporting law implementation through education, stakeholder engagement, and evidence-based advocacy. CONCLUSIONS: This study suggests that the process of smoke-free law implementation in Uganda has not aligned with World Health Organisation (WHO) guidelines for implementing smoke-free laws, and highlights that low-income countries may need additional support to enable them to effectively plan for policy implementation and resist industry interference.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".