Intervention with traditional food as a major source of energy, protein, iron, vitamin C and vitamin A for rural Dalit mothers and young children in Andhra Pradesh, South India.
Bibliographic record
Abstract
Intakes of energy, protein, iron and vitamin A in India are usually reported as inadequate. Recognizing that traditional food systems are sustainable, high in species variety, and have rich nutrient sources, we compared dietary intakes and nutrient sources of Dalit mothers and their children living in villages with and without an intervention based on improved access to the traditional Dalit food system. 24-hour recalls were conducted with Dalit mothers and their children aged 6-39 months during summer and rainy seasons in 2003. We found that mothers from intervention villages had significantly higher intakes of energy (mean +/- SD: 12,197 +/- 3,515 kJ vs. 11,172 +/- 3,352 kJ; p =0.02) and protein (77.5 +/- 25.1 g vs. 71.1 +/- 25.2 g; p =0.05) in summer, and higher intakes of energy (11,168 +/- 3,335 kJ vs. 10,168 +/- 3,730 kJ; p = 0.04), protein (68.9 +/- 22.6 g vs. 60.4 +/- 23.8 g; p <0.01) and iron (15.8 +/- 6.6 mg vs. 13.7 +/- 9.1 mg; p <0.01) during rainy season. There were no differences in children's intakes between intervention and control villages. In mothers, sorghum contributed 29% of energy, 33% of protein and 53% of iron, and green leafy vegetables contributed 21% of vitamin C and 38% of vitamin A. Our results indicate that traditional food such as sorghum, pulses and green leafy vegetables are major sources of energy, protein, iron, vitamin C and vitamin A, and that mothers from villages with the traditional food intervention had higher intakes of energy, protein and iron.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".