EATING PATTERNS AND NUTRITIONAL STATUS OF CHILDREN'S AGE PRE-SCHOOL IN PUSKESMAS MELONGUANE TALAUD ISLANDS
Bibliographic record
Abstract
Background: Nutritional status in children aged 1-5 years is still a major health problem in the world including the country of Indonesia. Indonesia is a developing country that still faces considerable nutritional deficiencies. World Health Organization (WHO) estimates that 54% of causes of death in infants and toddlers are based on poor nutrition. Aims are to determine the relationship between eating patterns with the nutritional status of pre-school age children in the Public Health Center in the Melonguane sub-district. Methods: This research uses correlation analytic research with cross-sectional approach. This research was carried out in the Melonguane Community Health Center, Melonguane District, Talaud Regency. Sample as many as 33 pre-school age children (3-5 years). Based on statistical tests using the Chi-Square test with indigo ρ = 0.007 smaller than the value of α = 0.05, which means the null hypothesis is rejected. Conclusion: Most pre-school age children in the work area of the Melonguane Health Center in the District of Melonguane have a good diet. Most pre-school-aged children in the Melonguane Community Health Center area of Melonguane District have normal nutritional status. There is a relationship between eating patterns with the nutritional status of pre-school-aged children in the working area of the Melonguane Community Health Center, Melonguane District, normal nutritional status.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".