Analysis of Non-communicable disease prevention policies in five Sub-Saharan African countries: Study protocol
Bibliographic record
Abstract
BACKGROUND: The burden of non-communicable diseases (NCDs) and their risk factors is increasing in sub-Saharan Africa, and there have been calls for adopting a multi-sectoral approach in developing policies and programs to address this burden. Evidence exists largely from high-income countries on the success (and lack thereof) of multi-sectoral approach in improving population level health outcomes. In sub-Saharan Africa, there is limited research on the application and success of multi-sectoral approach in the formulation and implementation of policies aimed at prevention of non-communicable diseases. Therefore, this protocol describes a study that aims to primarily generate evidence on the extent to which multi-sectoral approach has been applied in developing policies to prevent non-communicable disease in six countries in sub-Saharan Africa -Kenya, Malawi, Nigeria, Cameroon, Togo and South Africa. METHODS/DESIGN: The study applies a multiple case study design. Data will be collated mainly through document reviews and key informant interviews with the relevant decision makers in various sectors. In each country, a detailed case study analysis will be undertaken of any policy/policies developed, adopted and implemented, aimed at implementing the World Health Organization recommended "best buys" for non-communicable disease prevention. These case studies will be conducted by research teams in each country; each team includes a senior research fellow supported by a doctoral student, and research assistants. DISCUSSION: Uptake of the evidence generated from the case studies will be ensured by systematic engagement with policy makers in each country throughout the research process. Ultimately, a forum of experts will be convened to generate actionable recommendations on the use of multi-sectoral approach in non-communicable disease prevention policies in the region.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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".