KAJIAN FAKTOR PEMBERIAN ASI EKSKLUSIF, RIWAYAT BERAT BADAN LAHIR DAN EKONOMI KELUARGA TERHADAP KEJADIAN BALITA STUNTING
Bibliographic record
Abstract
Abstract One form of physical growth failure in children is the "stunting" condition. Stunting is a form of growth disorder characterized by a child having a height that is less appropriate for his age, which is caused by chronic malnutrition since pregnancy. The incidence of Stunting toddlers is worth watching out for, because the danger of stunting can lead to generations who are not smart and sick. WHO set stunting tolerance limits (short stature) a maximum of 20 percent or one fifth of the total number of children under five. Meanwhile, in Indonesia, 7.8 million out of 23 million children under five were stunted or around 35.6 percent. As many as 18.5 percent are very short categories and 17.1 percent are short categories. In 2018, in East Java 2.1 percent of children under five were stunted from the total number of children under five. Experts explain that the main cause of stunting is due to the problem of chronic malnutrition since pregnancy. This research was conducted on community groups that have toddlers with an age range of 2 to 5 years in the working area of puskesmas in Kota Batu. The purpose of this study is to examine several factors that influence the occurrence of Stunting in Toddlers. The study design used probability sampling as a data collection technique, by taking a sample of 106 respondents. The results of the analysis of research data using a linear regression test obtained Economic factors (X3) with a significance value of 0.002 <0.005 and t arithmetic 3.182> t table 2.262, so it can be concluded that X3 influences Y. Thus it can be concluded that economic factors become the dominant factor among factors causing stunting Key word: causative factors, toddlers, stunting
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.006 |
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".