HUBUNGAN USIA PEMBERIAN MAKANAN PENDAMPING ASI DENGAN MASALAH MAKAN DAN STATUS GIZI BALITA DI DESA SATUI TIMUR KABUPATEN TANAH BUMBU PROVINSI KALIMANTAN SELATAN
Bibliographic record
Abstract
Background: Feeding problems occur in 25-50% of healthy toddlers. 3%-10% of \nchildren have severe feeding problems, that put them at risk of malnutrition, \ngrowth failure and developmental and behavioral disorders. One of indirect \ncauses that affect feeding problems and toddlers nutritional status is untimely \ncomplementary feeding (CF). In Satui Timur village many infants were given \nearly CF, as evidenced by the low coverage of exclusive breastfeeding in 2017 \n(only 31%). Method: This cross sectional study aims to determine the relationship \nbetween age of CF with feeding problems and nutritional status of toddlers. All \nmothers who had children 1-5 years old (142) were sampled. The Independent \nvariable is age of CF, while the dependent variable is feeding problems and \nnutritional status. Feeding problems were determined using a questionnaire \nadopted from The Montreal Children's Hospital Feeding Scale while nutritional \nstatus was determined by body weight/age index. Relationship between variables \ndetermined by contingency coefficient test (α=0.05). Results: 38% toddlers were \ngiven CF on time, 85.2% toddlers had no feeding problems, 76.1% toddlers had \nnormal nutritional status, and age of CF was not related to eating problems \n(p=0,631) and nutritional status of toddlers (p = 0,235). Conclusion: unrelated \nresult caused by homogeneous data, so it is necessary to do further research with \nbigger sample or about other factors that affect feeding problems and nutritional \nstatus of children. Although the results of this study are not related, there are \nmany negative impacts from untimely CF so community especially mother should \nintroduce CF timely. \nKeywords : Feeding Problems, Nutrition Status, age of Complementary Feeding \n(CF).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".