Non-communicable disease prevention policy process in five African countries authors
Bibliographic record
Abstract
BACKGROUND: The increasing burden of non-communicable diseases (NCDs) in sub-Saharan Africa is causing further burden to the health care systems that are least equipped to deal with the challenge. Countries are developing policies to address major NCD risk factors including tobacco use, unhealthy diets, harmful alcohol consumption and physical inactivity. This paper describes NCD prevention policy development process in five African countries (Kenya, South Africa, Cameroon, Nigeria, Malawi), including the extent to which WHO "best buy" interventions for NCD prevention have been implemented. METHODS: The study applied a multiple case study design, with each country as a separate case study. Data were collected through document reviews and key informant interviews with national-level decision-makers in various sectors. Data were coded and analyzed thematically, guided by Walt and Gilson policy analysis framework that examines the context, content, processes and actors in policy development. RESULTS: Country-level policy process has been relatively slow and uneven. Policy process for tobacco has moved faster, especially in South Africa but was delayed in others. Alcohol policy process has been slow in Nigeria and Malawi. Existing tobacco and alcohol policies address the WHO "best buy" interventions to some extent. Food-security and nutrition policies exist in almost all the countries, but the "best buy" interventions for unhealthy diet have not received adequate attention in all countries except South Africa. Physical activity policies are not well developed in any study countries. All have recently developed NCD strategic plans consistent with WHO global NCD Action Plan but these policies have not been adequately implemented due to inadequate political commitment, inadequate resources and technical capacity as well as industry influence. CONCLUSION: NCD prevention policy process in many African countries has been influenced both by global and local factors. Countries have the will to develop NCD prevention policies but they face implementation gaps and need enhanced country-level commitment to support policy NCD prevention policy development for all risk factors and establish mechanisms to attain better policy outcomes while considering other local contextual factors that may influence policy implementation such as political support, resource allocation and availability of local data for monitoring impacts.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".