Assessment of the multi-sectoral approach to tobacco control policies in South Africa and Togo
Bibliographic record
Abstract
BACKGROUND: Tobacco use is the world's leading preventable cause of illness and death and the most important risk factor for non-communicable diseases (NCDs), particularly cardiovascular and chronic respiratory diseases (heart attack, stroke, congestive obstructive pulmonary disease, and lung cancer). Tobacco control is one of the World Health Organization's "best-buys" interventions to prevent NCDs. This study assessed the use of a multi-sectoral approach (MSA) in developing and implementing tobacco control policies in South Africa and Togo. METHODS: This two-country case study consisted of a document review of tobacco control policies and of key informant interviews (N = 56) about the content, context, stakeholders, and strategies employed throughout policy formulation and implementation in South Africa and Togo. To guide our analysis, we used the Comprehensive Framework for Multi-Sectoral Approach to Health Policy, which is built around four major constructs of context, content, stakeholders and strategies. RESULTS: The findings show that the formulation of tobacco control policies in both countries was driven locally by the political, historical, social and economic contexts, and globally by the adoption WHO Framework Convention on Tobacco Control (FCTC). In both countries, the health department led policy formulation and implementation. The stakeholders involved in South Africa were more diverse, proactive and dynamic than those in Togo, whereas the strategies employed were more straightforward in Togo than in South Africa. The extent of understanding and use of MSA in both countries consisted of an inter-sectoral action for health, whereby the health department strove to collaborate with other sectors within and outside the government. Consequently, information sharing was identified as the main outcome of the interactions between institutions and interest groups within and across three critical sectors of the state, namely the public (government), the private and the civil society. CONCLUSION: Tobacco control policies in South Africa and Togo were formulated and implemented from an inter-sectoral approach perspective, which relied heavily on information transfer between stakeholders and less on collaborative problem-solving approach. Incorporation of multiple stakeholders allowed both countries to formulate policies to meet FCTC goals for tobacco control and NCD reduction.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".