Feeding Practices and Nutritional Status of Infants and Young Children Aged 6-23 Months in the South Kivu Region: A Cross-Sectional Study
Bibliographic record
Abstract
Background: The increasing prevalence of stunting in the Democratic Republic of the Congo (DRC) represents a major public health concern. Adequate complementary feeding is crucial for preventing and reducing chronic malnutrition in early childhood in the long term. Objective: This study aimed to assess the baseline complementary feeding practices and nutritional status of children aged 6–23 months in South Kivu Province, with a focus on commonly consumed complementary foods. Methods: A cross-sectional analytical study was conducted among 515 children in the Kadutu and Miti-Murhesa health zones of South Kivu. Participants were selected through a systematic random sampling method. Dietary intakes and infant feeding practices were assessed using dietary recall questionnaires and a 7-day food frequency questionnaire. The nutritional status was measured by anthropometry. Results: The mean age of children was 13.3 ± 5 months. Results showed that 59% of the children had a low dietary diversity score. Only 23% received an appropriate complementary feeding according to the minimum acceptable diet. Most of the children (88.5%) consumed porridge made exclusively of cereals, roots, or tubers and water. Animal-source foods, fruits, and vegetables were rarely consumed. Acute malnutrition and stunting affected 4.9% and 36.6% of children, respectively. Conclusion: Stunting remains prevalent in both rural and urban areas of South Kivu. Furthermore, infant diets are nutritionally inadequate, as evidenced by their lack of diversity. Enriching widely consumed staple foods (maize, sorghum, and soy) with locally available animal-source products could improve micronutrient intake and constitute a promising strategy for preventing child malnutrition.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".