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 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 | 0.059 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.065 | 0.009 |
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".