DETEKSI DINI STUNTING PADA BAYI DAN BALITA DI WILAYAH KERJA PUSKESMAS PEGAMBIRAN KOTA PADANG
Bibliographic record
Abstract
Status gizi memiliki pengaruh yang signifikan terhadap tumbuh kembang anak. Gizi yang kurang baik selama 1000 hari pertama kehidupan (HPK) dapat menurunkan risiko terkena penyakit, salah satunya adalah stunting, begitu juga risiko kematian yaitu sekitar 13%. Tahun 2018 persentase balita sangat pendek dan pendek usia 0-59 bulan adalah 11,5% dan 19,3%. Besarnya risiko stunting terhadap bayi dan balita, maka perlu diadakannya deteksi dini stunting tersebut sebagai salah satu upaya untuk membantu meningkatkan pengetahuan yang berimplementasi terhadap kegiatan pemantauan pertumbuhan dan perkembangan bayi dan balita yag lebih optimal. Kegiatan telah dilaksanakan terhadap ibu yang memiliki bayi dan balita sebanyak 20 orang. Metode kegiatan berupa penyuluhan, pemeriksaan fisik dan deteksi dini tumbuh kembang dengan kuisioner KPSP. Hasil kegiatan diperoleh bahwa 6,25% bayi kurus dan 6,25% bayi obesitas, 44% bayi dan balita kategori pendek, 6% sangat pendek, 81% ASI Eksklusif, 6% bayi dengan penyimpangan (gerak halus, sosialisasi dan kemandirian) dan 6% hasil meragukan (gerak kasar). Diharapkan kepada suami, keluarga dan masyarakat melakukan pemantauan pertumbuhan dan perkembangan bayi dan balita untuk mencegah stunting dan gangguan pertumbuhan dan perkembangan lainnya. Petugas kesehatan agar selalu menggalakkan program nutrisi seimbang dan upaya pencegahan stunting lainnya sejak masa persiapan kehamilan (prakonsepsi).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 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".