Factors associated with childhood chronic malnutrition in West and Central Africa: a scoping review
Bibliographic record
Abstract
Chronic malnutrition is a major public health concern that is the focus of a large body of scientific research. However, there is no synthesis of knowledge about the factors associated with this disease in West and Central Africa, where its prevalence is particularly high. We conducted a systematic search for scientific articles published between January 1, 2000, and October 15, 2020, that focus on chronic malnutrition in children in West and Central Africa. We queried CAIRN, PubMed, CINAHL, MEDLINE, Scopus, and Google Scholar databases for this purpose. The search process followed the recommendations of Arksey and O´Malley. Items reported in this review follow the PRISMA-ScR guidelines. Sixty articles involving children from a total of twenty (20) countries, mainly Ghana and Nigeria, were included in the final analysis. The data used were predominantly cross-sectional and were mainly drawn from demographic and health surveys. The analysis revealed that chronic malnutrition in children is associated with sociocultural, economic, and healthcare factors related to the characteristics of children, mothers, households, and communities. The association with children´s vulnerability to disease, maternal education, purchasing power, and autonomy need to be further investigated in West and Central Africa. Further analysis using longitudinal data is also needed to better understand the factors associated with chronic malnutrition in West and Central Africa.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".