Barriers and Facilitators to the Development and Implementation of Public Policies Addressing Food Systems in Five Sub-Saharan African Countries and Five of Their Cities
Bibliographic record
Abstract
BACKGROUND: There is increasing recognition of the role governments play in addressing the health and environmental sustainability challenges within current food systems. This study seeks to understand food system policies designed and/or implemented by selected national and local governments in Africa, and the barriers and facilitators faced when designing or implementing policies to create healthy and environmentally sustainable food systems. METHODS: From an evidence-based list of proposed policies with double- or triple-duty potential to achieve healthy and environmentally sustainable food systems, a policy mapping was performed in five African countries (Benin, Côte d'Ivoire, Senegal, Togo, and Uganda) and one city in each of these countries (Ouidah, Bouaké, Saint-Louis, Sokodé, and Mbale). Semi-structured interviews were then conducted with policy stakeholders. The interview data were analysed in NVivo 14 using the thematic framework analysis approach, informed by the Health Policy Triangle (HPT). RESULTS: The mapping showed that African countries have designed and implemented policies that simultaneously address food insecurity and climate change, mainly through food production policies. Within food environments, countries are focussing on interventions to prevent obesity, mainly food provision or food pricing policies. However, many policy gaps remain. Several technical and political barriers were commonly experienced when designing and implementing food system policies, regardless of the jurisdiction, context or region, such as insufficient financial resources, lack of political will, limited data, and inadequate monitoring and enforcement mechanisms. The major facilitators perceived were supportive public opinion and awareness, international agreements, sound agenda-setting, multi-sector and multi-stakeholder consultations and partnerships, availability of both financial resources and data, and solid political will. CONCLUSION: This article gives an overview of policies designed and implemented to achieve sustainable food systems, highlighting a strong focus through agriculture on undernutrition and climate change objectives. It also identifies their potential legislative, financial, and practical barriers and facilitators.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".