Capacity needs assessment and challenges for multisectoral implementation of nutrition in Burkina Faso: A guide for the formulation of a capacity development plan
Bibliographic record
Abstract
INTRODUCTION: Achieving nutritional goals depends on individual, organisational and environmental capacities. The aim of this study was to analyse and identify capacity gaps among the coordination platforms and networks, and the key technical institutions related to nutrition in Burkina Faso for a capacity development plan formulation. METHODS: Using the new Nutrition Capacity Framework developed by the United Nations Network, information were collected using the Nutrition Stakeholder Mapping and Analysis tool, and the Checklist for Capacity Areas. Capacity needs were analysed in terms of Human resource and infrastructure, functional, organisational, coordination and partnership, and financial and resource mobilisation. RESULTS: Limited human resource capacity in nutrition was highlighted in most cases by the structures, and the nutrition coordination structure and more than 4/5 of the technical structures are faced with the unavailability of working materials, tools and basic Internet connection. Only 10 among the 30 structures have a unit or service for exchange on nutrition, and only three of them have integrated nutrition actions. Shortfalls were noted in terms of functional, facilitation, communication and advocacy skills, as well as a weak diversification of resource mobilisation strategies. CONCLUSION: The use of the analytical framework helped to identify the gaps and to propose paths for capacity development. Efforts need to be strengthened, intensified, coordinated, monitored, evaluated and funded.
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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 | 0.021 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".