Epidemiologic Risk Factors Associated with Malnutrition in Children (3-5 Years of Age) at District Peshawar
Bibliographic record
Abstract
OBJECTIVES: This study was designed to assess the prevalence of malnutrition in terms of wasting and stunting in children between 3- and 5 years of age and to analyze the risk factors associated with malnutrition among them in the district of Peshawar. METHODOLOGY: This cross-sectional study was conducted in the district of Peshawar. Multi-staging simple random probability sampling selected a sample of 225 children between 3 and 5 years. The data was collected through a structured questionnaire containing demographic, risk factors and anthropometry. RESULTS: In a sample of 225 children, the prevalence of malnutrition was quite alarming. Stunting and wasting were observed in the age group 3-5 years. Low weight- for height was observed at 20%, and leanness through mid-arm circumference among children between 3-5 years of age in Peshawar district was 28%, although the mean values for all the indicators fell at the 50th percentile. About 76.9% were exclusively breastfed, and 29.8% of children were partially vaccinated. The association of low MUAC with breastfeeding (chi-square value of 28.9 & P= <0.001), vaccination (chi-square value (χ2) 80.3 & P=<0.001), weaning (χ2= 31.1 & P= <0.001), mothers’ education (χ2= 5.28 & P= <0.028), family income χ2= 40.79 & P= <0.001), birth defects (χ2= 8.39 & P= <0.009), and frequent infections (χ2= 36.5 & P= <0.001) respectively showed that these confounding factors were the major factors behind these malnourished children. Almost the same association was found in the low w χ2= 31.1 & P= <0.001) and the tested socio-demographic and dietary factors. However, family size failed to show negative impacts in our study. CONCLUSION: Malnutrition tends to occur more in children who are not breastfed, started weaning later than 6 months, are not vaccinated, have birth defects and frequent infections, are born to uneducated mothers, and have low family incomes.
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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 | 0.001 | 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".