Bibliographic record
Abstract
The children period is known as the golden age of development because at this stage there is an acceleration of physical and mental growth. However, at the same time, children are also an age group that is very vulnerable to various health problems, one of which is prone to nutritional disorders, especially nutritional problems in children, such as stunting. This study aims to look at the determinants of stunting in Majalaya, Bandung using a case control study design. The selection of respondents used random sampling techniques. The case group consists of 15 children aged 24 to 59 months who have stunted status in Padaulun Village in March 2025. The control group is 45 children aged 24 to 59 months who are not stunted in Padaulun Village in March 2025. Data were analyzed using logistic regression tests. The results of the study were obtained from the variables of Mother's education (p=0.017; OR=11; 95%CI 1.048-115.510) and feeding pattern (p=0.002; OR=6.76; 95%CI 1,820-25,174) had an effect on the incidence of stunting in children, while the variables of maternal age and maternal occupation had no effect on the incidence of stunting in children. The results of the multivariate analysis showed that only feeding pattern was the most significant to increase the incidence of stunting. Regular education is needed about the right feeding pattern for mothers who have children so that the risk of stunting can be minimized.
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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 | 0.001 | 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".