The Association of Household Garbage Management and Socioeconomic with Underweight in Children Under Five in Lebak District and Tangerang City, Banten, Indonesia
Bibliographic record
Abstract
Underweight in infants and under-five children may cause growth and developmental disorders and it remains one of the major factors causing child mortality, illness and disability. Underweight is still one of the nutritional problems in Indonesia and the government is undertaking all efforts and drawing up effective strategies to reduce the prevalence of underweight in Indonesia. The purpose of this study was to see what factors were associated with underweight in under-five-year-old children in Lebak Regency and Tangerang City, Banten Province. The study applied the logistic regression method using the 2013 Basic Health Research. Underweight in children was calculated by converting the anthropometric measurements into a standardized value (Z-score), which was then presented in the index of body weight for age. The results obtained among 492 infants revealed there were 28.5% (about 75 children under five) with underweight status. Children from families who did not apply good sanitation by disposing of garbage in an unsanitary manner had a risk of 15.2 folds (OR = 15.2, 95% CI = 4.69-49.65) to be underweight compared with those who had good sanitation behaviors. Children under five from families with a low socioeconomic status had a risk of 2.5 folds (OR = 2.5, 95% CI = 1.12-5.53) to suffer from underweight compared to those who had a high socioeconomic status. The conclusion is sanitation and socioeconomic status are related to the underweight status of children under five in Lebak Regency and Tangerang City.
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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 | 0.000 |
| 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".