The Effect of Feeding Patterns and History of Infectious Diseases on the Incidence of Stunting in Children Under Five in the Province of East Nusa Tenggara
Bibliographic record
Abstract
BACKGROUND: The prevalence of stunting of children under five years old (toddlers) in East Nusa Tenggara (NTT) reached 40.3 percent, the highest when compared to other provinces in Indonesia. This figure is above the National stunting prevalence of 29.6 percent. The prevalence of stunting in NTT consists of infants with a very short category of 18 percent and a short category of 22.3 percent. The purpose of the study was to analyze the influence of feeding patterns and a history of infectious diseases on stunting. METHOD: This type of research is quantitative with a case-control study design, located in Kupang Regency and South Central Timor Regency in 2020. The sample of this study was 150 children under five consisting of 75 children under five who were stunted and 75 children under five were not stunted as a control and a simple random sample development technique. The method of collecting data is through measuring the height and weight of children under five and conducting interviews with parents of toddlers using a questionnaire and analyzing it by Chi-Square. RESULTS AND CONCLUSION: The results showed that there was an influence of food feeding patterns (pattern of menu preparation, food processing, food presentation, and how to feed) and a history of infectious diseases (Ari and diarrhea) on the incidence of Stunting in children under five in East Nusa Tenggara province.
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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 | 0.002 |
| 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 | 0.000 |
| 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".