PERBEDAAN TINGKAT KECUKUPAN NUTRISI DAN PEMBERIAN ASI PADA BALITA STUNTING DAN TIDAK STUNTING DI DESA SUKAMUKTI WILAYAH KERJA UPTD PUSKESMAS JALAKSANA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Kurangnya asupan zat gizi dapat menyebabkan stunting. Prevalensi stunting di Indonesia 27,67% pada tahun 2019 (Riskesdas, 2019) sedangkan di Kabupaten Kuningan 42% salah satunya Kecamatan Jalaksana Desa Sukamukti terdapat 20 balita sangat pendek dan 115 balita pendek. Penelitian ini bertujuan untuk mengetahui perbedaan tingkat kecukupan nutrisi dan pemberian ASI pada balita stunting dan tidak stunting di Desa Sukamukti Wilayah Kerja UPTD Puskesmas Jalaksana Tahun 2020. Jenis penelitian comparative study dengan desain cross sectional. Populasi 241 balita, menggunakan teknik Proportionate Stratified Random Sampling jumlah sampel yaitu 150 responden. Analisis data menggunakan uji Mann Whitney. Sebagian besar balita memiliki kecukupan nutrisi dalam kategori normal sebanyak 118 responden (78,7%), diberikan ASI secara eksklusif sebanyak 93 responden (62%), tidak stunting sebanyak 79 responden (52,7%). Terdapat perbedaan kecukupan nutrisi (p value = 0,001) dan pemberian ASI (p-value=0,002) pada balita stunting dan tidak stunting. Kesimpulan terdapat perbedaan kecukupan nutrisi dan pemberian ASI pada balita stunting dan tidak stunting, diharapkan dapat meningkatkan pemberian ASI dan porsi makanan yang bergizi supaya tidak terjadi stunting.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it