Barriers and facilitators to the implementation of vitamin A supplementation programs in Africa: A systematic review
Bibliographic record
Abstract
Background: Vitamin A deficiency (VAD) impacts over 50% of children aged 6–59 months in sub-Saharan Africa, causing severe health issues. Despite the importance of vitamin A supplementation (VAS) programs, barriers limit their effectiveness, making it essential to understand these factors for better outcomes. Aim: This systematic review aimed to identify the barriers and facilitators to VAS programs in Africa, using the Consolidated Framework for Implementation Research (CFIR) to conceptualize the findings. Methods: A comprehensive search was conducted across OVID Embase, OVID Medline, Web of Science Core Collection, Scopus, CINAHL and CAB Direct. Studies were excluded if they did not report VAS administration via capsules or droplets in large-scale programs or omitted discussions on implementation barriers and facilitators. Results: The search yielded 4377 citations, with 10 studies meeting eligibility criteria, published from 2002 to 2021 across 12 countries. The most frequently represented were Ethiopia and Zimbabwe. A total of nine barriers and seven facilitators to VAS programs were identified. The most frequently cited barriers were capsule stock-outs, limited resources and lack of incentive for staff, while the most frequently cited facilitators were Child Health Days and involvement of community-based health workers. The key CFIR constructs associated with these findings were Tailoring Strategies, Incentive Systems and Available Resources. Conclusion: The barriers and facilitators identified in this review offer valuable insights for improving VAS coverage and implementation in Africa. Tailoring implementation strategies based on these findings can enhance the effectiveness and coverage of VAS programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".