Policy Processes in Multisectoral Tobacco Control in India: The Role of Institutional Architecture, Political Engagement and Legal Interventions
Bibliographic record
Abstract
BACKGROUND: The development and implementation of health policy have become more overt in the era of Sustainable Development Goals, with expectations for greater inclusivity and comprehensiveness in addressing health holistically. Such challenges are more marked in low- and middle-income countries (LMICs), where policy contexts, actor interests and participation mechanisms are not always well-researched. In this analysis of a multisectoral policy, the Tobacco Control Program in India, our objective was to understand the processes involved in policy formulation and adoption, describing context, enablers, and key drivers, as well as highlight the challenges of policy. METHODS: We used a qualitative case study methodology, drawing on the health policy triangle, and a deliberative policy analysis approach. We conducted document review and in-depth interviews with diverse stakeholders (n = 17) and anlayzed the data thematically. RESULTS: The policy context was framed by national law in India, the signing of a global treaty, and the adoption of a dedicated national program. Key actors included the national Ministry of Health and Family Welfare (MoHFW), State Health Departments, technical support organizations, research organizations, non-governmental bodies, citizenry and media, engaged in collaborative and, at times, overlapping roles. Lobbying groups, in particular the tobacco industry, were strong opponents with negative implications for policy adoption. The state-level implementation relied on creating an enabling politico-administrative framework and providing institutional structure and resources to take concrete action. CONCLUSION: Key drivers in this collaborative governance process were institutional mechanisms for collaboration, multi-level and effective cross-sectoral leadership, as well as political prioritization and social mobilization. A stronger legal framework, continued engagement, and action to address policy incoherence issues can lead to better uptake of multisectoral policies. As the impetus for multisectoral policy grows, research needs to map, understand stakeholders' incentives and interests to engage with policy, and inform systems design for joint action.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".