Facilitators and barriers in the formulation and implementation of tobacco control policies in Kenya: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Tobacco use has serious public health implications for both smokers and non-smokers and significant economic implications on health care spending for governments. Tobacco-related deaths are preventable through well-formulated and implemented tobacco control policies. Using tobacco policy as a case study, we aim to describe the tobacco control policy formulation and implementation and the associated facilitators and barriers in Kenya. METHOD: We used a case-study methodology to integrate two sources of data: a document review of relevant policy documents, published articles and reports between 2004 and 2015 (N = 24 documents) and in-depth interviews (N = 39). Participants were from sectors relevant to tobacco control: research and academia, government, private industry, civil society and non-governmental organizations. Thematic analysis was used to analyze all data. RESULTS: Kenya developed a comprehensive tobacco policy in 2007. The main facilitators to the policy formulation and implementation process were (1) political commitment and strong leadership, (2) the presence of a coordination mechanism, (3) stakeholder passion and commitment, (4) resources and (5) constitutional requirement for inclusion of stakeholders. The main barriers to policy formulation and implementation were (1) industry interference, (2) resources, (3) poor enforcement and (4) lack of clear roles. CONCLUSION: Although the process for formulating a tobacco control policy in Kenya was protracted, the current policy aligns well with current global efforts. The implementation is still weak and this can be enhanced by provision of necessary resources and continued engagement of all relevant stakeholders. There is a need for continued engagement with political leadership and continuous international information exchange on how policy-makers can address and counter industry interference in tobacco control efforts.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".