Association of Socio-Economic Factors with the Nutritional Status of the Children Aged 2-8 Years from Slums of Kolkata, West Bengal, India
Bibliographic record
Abstract
Background: Undernutrition appears to be a plaguing factor for physical and cognitive development of a large proportion of Indian children living in impoverished conditions. The city of Kolkata, located in the eastern part of India has demonstrated a conspicuous rise in its slum growth profile in the past few decades. Hitherto, studies on physical growth and nutritional status of slum children are lacking. Objectives: To Investigate the nutritional status (stunting, wasting, and underweight) among 2- 8 year old children and to observe the association of socio-economic factors with undernutrition of the studied children. Materials and Methods: This cross sectional study was conducted on 185 children aged 2 to 8 years residing at slums of Tangra, Behala, and Dum Dum regions of Kolkata. Anthropometric measurements (height and weight) were take following standard protocol (Lohman et al., 1988), Socio-economic information were collected using a semi-structured questionnaire. Stunting, Underweight and Wasting were derived to evaluate the nutritional status of the studied population. Pearson correlation (r) coefficient test was undertaken to measure the association of some socio-economic variables on undernutrition. Results: The prevalence of stunting, underweight, and wasting were 38.91% (boys 32.14% and girls 45.45%), 50.27% (boys 51.16%, girls 49.49%), 31.35% (boys 30.23%, girls 32.32%) respectively. The present study showed the positive correlation (r) between educational levels of the parents and stunting, underweight and wasting independently. But household size has a negative correlation with all the three measures of nutritional status. Conclusion: We conclude that the slum children were facing a nutritional health risk and parental education and household size appeared to be the primary reasons.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".