Assessment of the Feeding Practices in Infants and Young Children and its Association with Nutritional Status in Urban Areas
Bibliographic record
Abstract
Background: Poor nutrition at an early age leads to malnutrition, which in turn leads to an increase in risks of repeated infections, which is responsible for the poor health of children. The nutritional status of a child is directly proportional to their feeding practices, which are dependent on the knowledge and practices followed by the mother. This study assesses the level of knowledge and practices among mothers on feeding practices for their infants and young children and its association with nutritional status. Methods and Materials: A cross-sectional study was conducted in the households of urban slums in the field practice area of the Urban Health Training Centre of a private medical college. A questionnaire consisting of sociodemographic data, knowledge of breastfeeding, knowledge of complementary feeding, and actual practices of feeding the children from 0 –2 years was used for data collection using Google Forms, followed by anthropometric measurements of the children with the help of WHO standardized growth charts to assess their nutritional status. Results: Out of 112 participants, 37.5% of the mothers were less than 25 years old. The mean age of the babies was found to be 11 + 6.49 months. 53.57% of mothers had good knowledge, and 72.32% of mothers followed correct feeding practices. Conclusion: There is a significant association of good knowledge among mothers with babies who did not show wasting. There is no association between knowledge and feeding practices being followed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".