Dietary Intakes and Nutritional Status of Mother-Child (6-23 Months Old) Pair Targeted through the "Organic Residual Products for Biofortified Foods for Africa Project" in Rural Area in Senegal
Bibliographic record
Abstract
Background: Despite micronutrient supplementation and food fortification strategies carried out for decades, micronutrient deficiencies remain prevalent among children under 5 years old in rural area in Senegal. The OR4FOOD project was implemented as a preventive and long-term approach to reduce malnutrition through biofortification. Objective: We aimed to assess the baseline dietary intakes and nutritional status of the mother-child (6-23 months old) pair in a rural community in Senegal. Methods: Dietary intakes were assessed using dietary recall questionnaires and weight food records. All foods and beverages consumed from waking to bedtime were quantified, and nutrient intakes were calculated. The nutritional status was measured by anthropometry. Results: Results showed that 77.2% of children had low dietary diversity score. Only 18% of them received an appropriate complementary feeding according to the minimum acceptable diet. Cereals and legumes were among the most consumed food groups, whereas orange-fleshed sweet potato (OFSP) and animal food products were rarely consumed. Median dietary intakes of iron, zinc, and vitamin A were lower than the recommended dietary allowances. Acute malnutrition and stunting affected 14.6% and 16.9% of children, respectively. Overall, 20.8% of mothers were underweighted, and overweight/obesity affected 23.1% of them. Conclusion: Malnutrition remains prevalent in rural areas of Senegal and affects both mothers and children. Furthermore, their nutrient requirements were not covered by the diet. Millet and cowpea being widely consumed, optimizing their iron and zinc content through biofortification and the introduction of OFSP might improve micronutrient intakes and would be promising strategies to prevent 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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".