Making food-related health taxes palatable in sub-Saharan Africa: lessons from Ghana
Bibliographic record
Abstract
Amidst high burden of infectious diseases, undernutrition and micronutrient deficiencies, non-communicable diseases (NCDs) are predicted to become the leading cause of death in Ghana by 2030. NCDs are driven, to a large extent, by unhealthy food environments. Concerned, the Ghana Ministry of Health (MOH) has since 2012 sought to garner the support of all to address this challenge. We aimed to support the MOH to address the challenge through public health policy measures, but would soon be reminded that longstanding challenges to policy development such as data poverty, and policy inertia needed to be addressed. To do this, the we generated the needed evidence, curated the evidence, and availed the evidence to Ghanaian policymakers, researchers and civil society actors. Thus, we addressed the problem of data poverty using context-relevant research, and policy inertia through advocacy and scholar activism. In this paper, we share how a public interest coalition used context-relevant research, evidence-informed advocacy and scholar activism to valorise and increase demand for healthy food policy (including food-related health taxes) in Ghana.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".