Navigating institutional complexity in the health sector: lessons from tobacco control in Kenya
Bibliographic record
Abstract
INTRODUCTION: This research examines the institutional dynamics of tobacco control following the establishment of Kenya's 2007 landmark tobacco control legislation. Our analysis focuses specifically on coordination challenges within the health sector. METHODS: We conducted semi-structured interviews with key informants (n = 17) involved in tobacco regulation and control in Kenya. We recruited participants from different offices and sectors of government and non-governmental organizations. RESULTS: We find that the main challenges toward successful implementation of tobacco control are a lack of coordination and clarity of mandate of the principal institutions involved in tobacco control efforts. In a related development, the passage of a new constitution in 2010 created structural changes that have affected the successful implementation of the country's tobacco control legislation. DISCUSSION: We discuss how proponents of tobacco control navigated these two overarching institutional challenges. These findings point to the institutional factors that influence policy implementation extending beyond the traditional focus on the dynamic between government and the tobacco industry. These findings specifically point to the intragovernmental challenges that bear on policy implementation. The findings suggest that for effective implementation of tobacco control legislation and regulation, there is need for increased cooperation among institutions charged with tobacco control, particularly within or involving the Ministry of Health. Decisive leadership was also widely presented as a component of successful institutional reform. CONCLUSION: This study points to the importance of coordinating policy development and implementation across levels of government and the need for leadership and clear mandates to guide cooperation within the health sector. The Kenyan experience offers useful lessons in the pitfalls of institutional incoherence, but more importantly, the value of investing in and then promoting well-functioning institutions.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.016 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".