Fiscal policies and regulations for healthy diets in Sri Lanka: an analysis of the political economy of taxation and traffic light labelling for sugar-sweetened beverages
Bibliographic record
Abstract
BACKGROUND: Unhealthy dietary patterns significantly contribute to rising non-communicable diseases (NCDs) in Sri Lanka. The government has implemented policy measures to promote healthy dietary patterns, including the traffic light labelling (TLL) system for sugar-sweetened beverages (SSBs) in 2016 and taxation on SSBs in 2017. OBJECTIVES: To analyse how ideas, institutions, and power dynamics influence the formulation and implementation of these two interventions, and to identify strategies for public health actors to advocate for more effective food environment policies in Sri Lanka. METHODS: This study drew on Kingdon's theory of agenda-setting and Campbell's institutionalist approach to develop the theoretical framework. We examined the political economy at the policy development and implementation stages, adopting a deductive framework approach for data collection and analysis. Data were collected from documents and key informants. RESULTS: NCDs and nutrition are recognised and framed as important policy issues in health-sector policy documents, and the SSB tax and TLL system are seen as means of improving diets and health. Sri Lanka's commitment to addressing NCDs and nutrition-related issues is evident through these policies. The Ministry of Health led policy development, and key stakeholders were involved. However, there are opportunities to learn and strengthen policy in Sri Lanka and elsewhere. Limited involvement and commitment of some stakeholders in developing national policies, industry interferences, and other gaps resulted in weaker policy design. Gender considerations were also given minimal attention in policy formulation and implementation. CONCLUSIONS: To enhance the effectiveness of the policies and regulations to promote healthy diets in Sri Lanka, comprehensive policy coverage, multistakeholder involvement and commitment to national policies, balanced power dynamics, technical feasibility, government commitment backed with high-level political support, awareness, and knowledge creation, managing industry interferences, integrating gender considerations are crucial factors.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".