Possible Causes of Malnutrition in Melghat, a Tribal Region of Maharashtra, India
Bibliographic record
Abstract
Melghat, situated in Amravati District of Maharashtra, India is a tribal region with amongst the highest numbers of malnutrition cases. This paper focuses on possible causes of malnutrition in the Dharni block of Melghat. Quantitative survey recorded the existing burden of malnutrition, kitchen garden (KG) practices, Public Distribution System, food provisioning, Anganwadi services and hygiene/sanitation in the community. Additionally a qualitative study was undertaken to understand the community's perspective on nutrition, cultural beliefs, spending habits and other factors contributing to malnutrition. Malnutrition was found to be highly prevalent amongst all age groups with 54% children aged 1-5 years and 43% adults aged ≥ 20 years being severe to moderately underweight. A major cause for malnutrition in children was faulty child care practices. Data on food provisioning revealed that while the caloric needs of the community were substantially met by consumption of cereals and pulses, minimal consumption of green leafy vegetables (GLVs) could lead to micronutrient deficiency in the community. KGs, which provide GLVs, were mainly cultivated in monsoon (98%) which declined to merely 4% in summer. The benefits of government schemes though targeted at malnourished children were often shared by the entire household and thus got diluted. Key finding was that nutrition interventions should be designed to address the entire household and emphasis should be given to appropriate nutrition education, without which distributing food or increasing income would have minimal effect.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".