Barriers and Facilitators to the Implementation of Large-Scale Nutrition Interventions in Africa: A Scoping Review
Bibliographic record
Abstract
Abstract The effective implementation of large-scale nutrition interventions in Africa is an ongoing challenge. This scoping review identifies and explores the barriers and facilitators to the implementation of large-scale nutrition interventions in the African region. We searched PubMed, EMBASE, Scopus, ERIC, and Web of Science using search terms focused specifically on barriers and facilitators to the implementation of nutrition interventions in Africa. To supplement the database search, reference lists in publications included for full-text review were also examined to identify eligible articles for inclusion. Eligible studies underwent quality assessment, and a directed content analysis approach to data extraction was conducted and aligned to the Consolidated Framework for Implementation Research (CFIR) to facilitate narrative synthesis. The search identified 1452 citations and following removal of duplicates and our inclusion/exclusion criteria, 34 papers were eligible for inclusion. More than half of included studies (n = 19) reflect research conducted in East Africa. Overarching thematic areas spanning the barriers and facilitators that were identified included policy and legislation; leadership management; resources mobilization; and cultural context and adaptability. Key activities that facilitate the development of successful implementation include (1) more supportive policy and legislation to improve government competency, (2) effective leadership, strategic partnership, and coordination across multiple sectors, (3) more effective resource mobilization, and (4) adequate adaptation of the intervention so that it is culturally relevant, tailored to local needs and aligned to research data. The barriers and facilitators identified under the CFIR domains can be used to build knowledge on how to adapt large-scale nutrition interventions to national and local settings. Registration Open Science Framework ( https://osf.io/6m8fy ).
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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 | 0.076 | 0.229 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.019 | 0.021 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".