BBLR DAN STATUS GIZI IBU SELAMA KEHAMILAN DENGAN KEJADIAN STUNTING PADA BALITA USIA 12-60 BULAN
Bibliographic record
Abstract
RINGKASAN - Menurut world health organization tahun 2017 Di wilayah afrika Jumlah anak Stunting telah meningkat di tahun 2021 berdasarkan Data SSGI, maka akan tetap perlu dilakukan evaluasi apabila angka stunted (pendek menurut usia) diikutkan dengan angka wasted (kurus menurut tinggi badan) sesuai standar dari WHO, Bali adalah satu-satunya provinsi dengan angka stunted yang rendah rendah (<= 20 persen) yakni 10.9 persen dan wasted rendah (<= 5 persen) yakni 3 persen. Provinsi yang di kategorikan kronik dengan angka stunted rendah dan wasted tinggi, terdapat 5 provinsi yaitu Bangka Belitung, Lampung, Kepulauan Riau, Yogyakarta, dan DKI Jakarta. Lalu , angka stunted tinggi dan wasted rendah ( kategori akut ) adalah Bengkulu. Tujuan Peneitian Ini Untuk mengetahui Hubungan BBLR Dan Faktor Gizi Ibu Selama Kehamilan Dengan Kejadian Stunting Pada Balita Usia 12-60 Bulan Di Puskesmas Kota Batam Tahun 2022 Teknik sampling yang digunakan simple random sampling, instrumen penelitian ini menggunakan kuesioner dan uji statistik Chi Square. Hasil Penelitian Ini , Ada hubungan BBLR dengan Kejadian stunting pada balita Puskesmas Kota Batam , dengan nilai ρ value 0,05 dan Ada hubungan status gizi Ibu saat hamil dengan kejadian stunting di wilayah Kerja Puskesmas Kota Batam dengan nilai ρ value 0,05, Berdasarkan hasil penelitian diharapkan bagi Puskesmas agar dapat memaksimalkan program edukasikan kepada ibu hamil agar dapat mencegah terjadinya BBLR dan stunting sehingga semua anggota keluarga memiliki status gizi yang baik.Kata Kunci : Berat Badan Lahir Rendah, Balita, Stunting, Gizi Ibu Hamil.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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".