Bibliographic record
Abstract
Kasus stunting masih tinggi di Indonesia setara dengan 30,2% dari seluruh balita di Indonesia. Disebut stunting atau pendek apabila hasil pengukuran tinggi badan atau panjang badan anak dihubungkan dengan umur (TB/U, atau PB/U) dengan nilai Z- Score < -2, berdasarkan standar World Health Organization. Meskipun telah banyak usaha yang dilakukan untuk menekan angka stunting, tapi kenyataanya stunting masih tinggi. ASI eksklusif menjadi salah satu factor penyabab stunting. Masih banyak balita yang tidak diberikan ASI eksklusif selama 6 bulan pertama kehidupan, sehingga menyebabkan anak menjadi kurang gizi. Kekurangan gizi dalam waktu yang sangat lama diyakini menjadi penyebab stunting. Tujuan penelitian ini adalah untuk mengetahuan gambaran status pemberian ASI eksklusif pada balita stunting di Kabupaten Kulon Progo. Design penelitian dengan menggunakan deskriptif, data anak balita stunting diperoleh dari Puskesmas selanjutnya dilakukan home visit untuk dilakukan pengukuran antropometri, dan orangtua responden diminta untuk mengisis kuesioner tentang status pemberian ASI eksklusif. Responden pada penelitian ini sebanyak 100 anak balita usia 2-5 tahun yang mengalami stunting (data puskesmas). Hasil penelitian diperoleh bahwa terdapat 52% balita stunting yang memiliki status pemberian ASI eksklusif dalam kategori “terpenuhi” dan ada 42% balita stunting tidak diberikan ASI eksklusif (“tidak terpenuhi”).
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".