Study design: policy landscape analysis for sugar-sweetened beverage taxation in seven sub-Saharan African countries
Bibliographic record
Abstract
This paper reports on the design of a study to examine the policy landscape relevant to sugar-sweetened beverage taxation in seven sub-Saharan African countries. The study responds to the need for strong policy to address the rising burden of non-communicable diseases in the region. Sugar-sweetened beverage taxation has been widely recommended as a key component of a comprehensive policy approach to NCD prevention. However, it has proved a contentious policy intervention, with industry strongly opposing the introduction of such taxes. The aim was to identify opportunities to strengthen sugar-sweetened beverage taxation-related policy for the prevention of nutrition-related NCDs in a subset of Eastern and Southern African countries: Kenya, Tanzania, Botswana, Rwanda, Namibia, Zambia, Uganda. The study was conducted as a collaboration by researchers from nine institutions; including the seven study countries, South Africa, and Australia. The research protocol was collaboratively developed, drawing on theories of the policy process to examine the existing availability of evidence, policy context, and stakeholder interests and influence. This paper describes the development of a method for a policy landscape analysis to strengthen policies relevant to NCD prevention, and specifically sugar-sweetened beverage taxation. This takes the form of a prospective policy analysis, based on systematic documentary analysis supplemented by consultations with policy actors, that is feasible in low-resource settings. Data were collected from policy documents, government and industry reports, survey documentation, webpages, and academic literature. Consultations were conducted to verify the completeness of the policy-relevant data collection. We analysed the frames and beliefs regarding the policy 'problems', the existing policy context and understandings of sugar-sweetened beverage taxation as a potential policy intervention, and the political context across relevant sectors, including industry interests and influence in the policy process. This study design will provide insights to inform public health action to support sugar-sweetened beverage taxation 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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".