Prevalence of and Factors Associated with Undernutrition in Southern Bangladesh: A Cross-Sectional Study
Bibliographic record
Abstract
Background: Undernutrition is one of the main causes of child mortality and morbidity in Bangladesh. This study aimed to assess the prevalence and determinants of undernutrition among children aged six to fifty-nine months in the Jashore and Khulna districts of southern Bangladesh. Methods: The random sampling technique was used to select 400 children of 6–59 months of age. Data were collected through face-to-face interviews using a structured questionnaire and anthropometric measurements. The weight and height of children were taken using a digital weighing scale, measuring tape, and stadiometer following standard protocols. Children’s undernutrition was estimated by stunting, wasting, and being underweight. The logistic regression analyses were done to assess the factors associated with undernutrition. Results: The prevalence of stunting was 57% (95% CI: 52.1–61.8) which was positively associated with diarrhea, illiterate mothers, absence of sanitary latrine, and no antenatal care (ANC). The wasting prevalence was 13% (95% CI: 10.1–16.7) which remained directly associated with family size (≥6 members), low household income, and family planning methods. The rate of prevalence of underweight was 27.8% (95% CI: 23.6–32.3) which was significantly associated with illiterate mothers, low household income, family size (≥6 members), lack of pure drinking water, and complementary feeding started before six months. Stunting was more prevalent (74.7%) in the age group of 42–53 months compared to other groups, and boys (60.7%) were more stunted than their counterparts (52.9%). Conclusions: The magnitude of childhood undernutrition was high among the studied participants. Nutritional interventions should be implemented on the basis of significant factors in the local context to tackle this problem in Bangladesh effectively.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".