Nutritional Challenges among African Refugee and Internally Displaced Children: A Comprehensive Scoping Review
Bibliographic record
Abstract
BACKGROUND: Children's nutrition in Africa, especially among those displaced by conflicts, is a critical global health concern. Adequate nutrition is pivotal for children's well-being and development, yet those affected by displacement confront distinctive challenges. This scoping review seeks to enhance our current knowledge, filling gaps in understanding nutritional and associated health risks within this vulnerable population. OBJECTIVE: We conducted a scoping review of the literature on the nutritional status and associated health outcomes of this vulnerable population with the goal of informing targeted interventions, policy development, and future research efforts to enhance the well-being of African refugee and internally displaced children. METHODS: This scoping review adopted Arksey and O'Malley (2005)'s methodology and considered studies published between 2000 and 2021. RESULTS: Twenty-three published articles met the inclusion criteria. These articles highlighted a wide variation in the levels of malnutrition among African refugee/internally displaced (IDP) children, with the prevalence of chronic malnutrition (stunting) and acute malnutrition (wasting) ranging from 18.8 to 52.1% and 0.04 to 29.3%, respectively. Chronic malnutrition was of 'high' or 'very high' severity (according to recent WHO classifications) in 80% of studies, while acute malnutrition was of 'high' or 'very high' severity in 50% of studies. In addition, anemia prevalence was higher than the 40% level considered to indicate a severe public health problem in 80% of the studies reviewed. CONCLUSION: In many settings, acute, chronic, and micronutrient malnutrition are at levels of great concern. Many countries hosting large, displaced populations are not represented in the literature, and research among older children is also lacking. Qualitative and intervention-focused research are urgently needed.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".