Mother and child nutrition among the Chakhesang tribe in the state of Nagaland, North‐East India
Bibliographic record
Abstract
Despite the importance of the nutritional status and food systems of Indigenous Peoples, the subject has received very little attention, especially in North-East India. Therefore, a community-based cross-sectional descriptive study was carried out among Chakhesang mothers with children under 5 years of age to evaluate their nutritional status and prevalence of chronic diseases in the context of their dietary habits. From 558 households (HHs), 661 children and 540 mothers were covered using standard anthropometric measurements as well as blood collection for haemoglobin and vitamin A. Data were collected from mothers on HH socio-demographic particulars and infant and young child feeding practices. The results showed that the prevalence of underweight, stunting, and wasting among children <5 years of age was 14%, 22%, and 7%, respectively. The prevalence of anaemia and vitamin A deficiency was 26% and 33% among children <5 years, whereas it was 33% and 26%, respectively, among mothers. Hypertension was observed in 16% of women, whereas diabetes was seen in 0.8%. Approximately 35% and 24% of HHs suffered mild or moderate food insecurity, respectively, which was associated with literacy of the parents, per capita income, and family size. Utilization of the rich agrobiodiversity and wild foods by the Chakhesangs appears to be a strong reason for their better nutritional and health status as compared to the rest of India. Therefore, this Indigenous knowledge and food system must be documented and kept vital, especially in policies and intervention programmes addressing food and nutrition security among the Chakhesangs.
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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 | 0.001 |
| 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".