The influence of fasting on energy and nutrient intake and their corresponding food sources among 6-23 months old children in rural communities with high burden of stunting from Northern Ethiopia
Bibliographic record
Abstract
BACKGROUND: Limited studies in Ethiopia showed that infants and young children are at high risk of inadequate intake of energy and nutrients. However, inclusive assessment of both nutrient intakes and their food sources are lacking. We aimed at assessing energy and nutrient intakes and their food sources during religious fasting and non-fasting periods among 6-23 months old children in Northern Ethiopia. METHODS: Data for this longitudinal study were collected following repeated multiple-pass 24-h dietary recall technique through face-to-face interviews with primary caregivers. Using a two-stage systematic random sampling method, a total of 570 and 551 children participated respectively in the lent fasting and non-fasting periods. Energy and nutrient intakes were estimated and compared with WHO daily requirements. All foods that a child consumed on the day preceding the date of data collection were recorded and processed with database software. Chi-square and t- tests were used to analyze the data. Non-normally distributed data were analyzed using Wilcoxon signed-rank test and statistical significance was set at p < 0.05. RESULTS: The overall prevalence of child stunting was 41.4%. Almost all of children (99.6%) consumed grains, roots, and tubers. The inadequacy prevalence of energy, protein and eight selected micronutrients (calcium, iron, zinc, vitamin A, thiamin, riboflavin, niacin, vitamin C) intake were 96.2, 44.9, and 95.5%, respectively. Calcium and zinc were the highest (100%) deficits observed across all age groups. Although consumption of animal source foods (ASFs) was very low (dairy 10.1%, meat 2.3% and eggs 23.6%), there was significantly higher consumption of meat and eggs during the non-fasting compared to fasting period (p < 0.001). CONCLUSIONS: Inadequate intake of energy and nutrients was common among 6-23 months old children. Cereals were found to be the main sources of many of the nutrients. The consumption of ASFs among 6-23-month-old children was low which was also affected by the religious fasting period. Hence, strengthening social and behavior change communication, supporting rural households to raise poultry and small ruminants is recommended.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".