Multi-sectoral action in non-communicable disease prevention policy development in five African countries
Bibliographic record
Abstract
BACKGROUND: The rise of non-communicable diseases (NCDs) in Africa requires a multi-sectoral action (MSA) in their prevention and control. This study aimed to generate evidence on the extent of MSA application in NCD prevention policy development in five sub-Saharan African countries (Kenya, South Africa, Cameroon, Nigeria and Malawi) focusing on policies around the major NCD risk factors. METHODS: The broader study applied a multiple case study design to capture rich descriptions of policy contents, processes and actors as well as contextual factors related to the policies around the major NCD risk factors at single- and multi-country levels. Data were collected through document reviews and key informant interviews with decision-makers and implementers in various sectors. Further consultations were conducted with NCD experts on MSA application in NCD prevention policies in the region. For this paper, we report on how MSA was applied in the policy process. RESULTS: The findings revealed some degree of application of MSA in NCD prevention policy development in these countries. However, the level of sector engagement varies across different NCD policies, from passive participation to active engagement, and by country. There was higher engagement of sectors in developing tobacco policies across the countries, followed by alcohol policies. Multi-sectoral action for tobacco and to some extent, alcohol, was enabled through established structures at national levels including inter-ministerial and parliamentary committees. More often coordination was enabled through expert or technical working groups driven by the health sectors. The main barriers to multi-sectoral action included lack of awareness by various sectors about their potential contribution, weak political will, coordination complexity and inadequate resources. CONCLUSION: MSA is possible in NCD prevention policy development in African countries. However, the findings illustrate various challenges in bringing sectors together to develop policies to address the increasing NCD burden in the region. Stronger coordination mechanisms with clear guidelines for sector engagement are required for effective MSA in NCD prevention. Such a mechanisms should include approaches for capacity building and resource generation to enable multi-sectoral action in NCD policy formulation, implementation and monitoring of outcomes.
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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 | 0.023 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| 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".