Multi-Sectoral Approach to Noncommunicable Disease Prevention Policy in Sub-Saharan Africa: A Conceptual Framework for Analysis
Bibliographic record
Abstract
Conceptual frameworks for health policy analysis guide investigations into interactions between institutions, interests, and ideas to identify how to improve policy decisions and outcomes. This review assessed constructs from current frameworks and theories of health policy analysis to (1) develop a preliminary synthesis of findings from selected frameworks and theories; (2) analyze relationships between elements of those frameworks and theories to construct an overarching framework for health policy analysis; and then, (3) apply that overarching framework to analyze tobacco control policies in Togo and in South Africa. This Comprehensive Framework for Multi-Sectoral Approach to Health Policy Analysis has 4 main constructs: context, content, stakeholders, and strategies. When applied to analyze tobacco control policy processes in Togo and in South Africa, it identified a shared goal in both countries to have a policy content that is compliant with the provisions of international tobacco treaties and differences in strategic interactions between institutions (e.g., tobacco industry, government structures) and in the political context of tobacco control policy process. These findings highlight the need for context-specific political mapping identifying the interests of all stakeholders and strategies for interaction between health and other sectors when planning policy formulation or implementation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".