Faktor Ibu Dan Anak Pada Kejadian Stunting Di Puskesmas Batakte
Bibliographic record
Abstract
Balita stunting memiliki risiko penurunan kemampuan intelektual, produktivitas, dan peningkatan penyakit degeneratif di masa mendatang. Hasil Riskesdas 2018 menunjukkan bahwa Nusa Tenggara Timur merupakan provinsi dengan proporsi balita gizi pendek dan sangat pendek tertinggi, yaitu 42,4%. Tujuan penelitian untuk menganalisis faktor risiko stunting pada anak balita di wilayah kerja Puskesmas Batakte Kabupaten Kupang. Rancangan penelitian ini adalah studi kasus kontrol. Subjek penelitian adalah balita usia (0-59 bulan) dengan kelompok kasus balita stunting sedangkan sampel kontrol adalah balita normal dengan perbandingan 1:1 sebanyak 48 balita dan ibu balita sebagai responden. Pemilihan sampel menggunakan simple random sampling. Analisis data menggunakan uji chi-square dan perhitungan OR untuk menilai faktor risiko. Penelitian dilaksanakan di wilayah kerja Puskesmas Batakte pada bulan Agustus sampai Oktober 2021. Variabel penelitian adalah riwayat penyakit menular, berat badan lahir rendah, pendidikan ibu, pola asuh, usia pekerjaan ibu saat hamil dan usia kehamilan. Hasil penelitian menunjukkan ada hubungan antara riwayat penyakit menular (OR=5.000; 95% CI 1.165-21.459), berat badan lahir rendah (BBLR) (OR=5.909; 95% CI 1.546-22.580), pendidikan ibu (OR=4,491; 95% CI 1,260-16,006) dan pola asuh (OR=6,000; 95% CI 1,711-21,038) dengan stunting pada anak balita, sedangkan pekerjaan ibu, usia saat hamil dan usia kehamilan tidak berhubungan dengan stunting pada anak balita di wilayah kerja Puskesmas Batakte.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".