Using social network analysis to understand multisectoral governance in district-level tobacco control programme implementation in India
Bibliographic record
Abstract
INTRODUCTION: Interest in multisectoral policies has increased, particularly in the context of low-income and middle-income countries and efforts towards Sustainable Development Goals, with greater attention to understand effective strategies for implementation and governance. The study aimed to explore and map the composition and structure of a multisectoral initiative in tobacco control, identifying key factors engaged in policy implementation and their patterns of relationships in local-level networks in two districts in the state of Karnataka, India. METHODS: Social network analysis (SNA) was used to examine the structure of two district tobacco control networks with differences in compliance with the India's national tobacco control law. The survey was administered to 108 respondents (n=51 and 57) in two districts, producing three distinct network maps about interaction, information-seeking and decision-making patterns within each district. The network measures of centrality, density, reciprocity, centralisation and E-I index were used to understand and compare across the two districts. RESULTS: Members from the department of health, especially those in the District Tobacco Control Cell, were the most frequently consulted actors for information as they led district-level networks. The most common departments engaged beyond health were education, police and municipal. District 1's network displayed high centralisation, with a district nodal officer who exercised a central role with the highest in-degree centrality. The district also exhibited greater density and reciprocity. District 2 showed a more dispersed pattern, where subdistrict health managers had higher betweenness centrality and acted as brokers in the network. CONCLUSION: Collaboration and cooperation among sectors and departments are essential components of multisectoral policy. SNA provides a mechanism to uncover the nature of relationships and key actors in collaborative dynamics. It can be used as a visual learning tool for policy planners and implementers to understand the structure of actual implementation and concentrate their efforts to improve and enhance collaboration.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".