POSITIVE DEVIANCE APPROACH AND SUPPLEMENTARY NUTRITION UNDER ICDS SCHEME ON IMPROVEMENT OF NUTRITIONAL STATUS OF 2-6 YEAR CHILDREN IN RURAL BANGALORE
Bibliographic record
Abstract
Introduction: Childhood malnutrition is a significant health problem in India and important cause for childhood morbidity and mortality. Lack of knowledge about child feeding contributes significantly to poor nutritional status among children. A system to deliver nutrition education effectively would be of value in India. Hence, the present study was designed to assess nutritional status of 2 to 6 year Anganwadi children and to evaluate effect of nutrition education and supplementary nutrition on nutritional status of malnourished Anganwadi children Methods: An Interventional study was carried out in rural anganwadi centres selected by cluster sampling technique. Intervention was done in the form of Nutrition education based on positive deviance approach, supplementary nutrition and supervision. Weight for age of study participants was measured every quarter for a period of one year. Results: The prevalence of underweight initially was 47.3%. After intervention prevalence reduced to 43%, 40%, 32% and 30 % during first, second, third and fourth quarter assessment. The improvement was significant between baseline and first quarter assessment (P<0.01), between second and third quarter assessment (P<0.001) and between third and fourth quarter assessment (P = 0.03) though not significant between first and second quarter assessment. Conclusion: Nutrition education based on positive deviance approach and supplementary nutrition helps to improve the nutritional status of the anganwadi children.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".