Living Environs and Nutritional Status of Children from an Urban Indian Slum: An Analysis of Associative Factors
Bibliographic record
Abstract
Growing urbanization gives rise to slums, which are densely populated peri-urban areas housing underprivileged populations. The nutritional status of children in slum areas can be compromised due to poor living environs despite availability of many urban health care facilities. The present cross- sectional study was undertaken to determine the nutritional status of children residing in slum and analyze the various associative factors. The study area was Ghousianagar, a slum in city of Mysore from South India. A sample of 676 children (2-11 years of age, males, 310 and females, 366) from two schools was chosen for detailed anthropometry. Data were also collected on living conditions, economic and literacy levels of parents and nutritional status of mothers (n=200) through standard techniques. The results revealed that the living conditions of children were highly unhygienic. Only in 36% of families both parents were literate. Children from all age groups exhibited different degrees of malnutrition which worsened with increasing age. Only 8% of children were normal and the rest suffered with different degrees of undernutrition. Stunting and wasting were significantly influenced by age and gender of children. Under associative factors studied, weight for age of children was significantly associated with economic status of family and maternal BMI. Weight for height was associated with economic status, family size and maternal BMI. Height for age exhibited marginal association with family size. It can be said that adverse living environment and limited resources influenced the nutritional status of children adversely.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".