PROFIL BALITA STUNTING DI WILAYAH PUSKESMAS MAPILLI DESA UGI BARU KEC. MAPILLI KAB. POLMAN
Bibliographic record
Abstract
Indonesia sebagai negara berkembang menghadapi beberapa permasalahan utamanya masalah gizi. Masalah gizi di Indonesia menjadi masalah kompleks yang perlu mendapatkan perhatian. Gizi kurang atau malnutrisi adalah kondisi kekurangan gizi akibat jumlah kandungan mikronutrien dan makronutrien tidak memadai (Sinaga, 2008). Kondisi ini dapat disebabkan oleh malabsorbsi yaitu ketidakmampuan mengonsumsi nutrisi. Masalah gizi kurang juga menyebabkan Stunting. Penelitian ini bertujuan untuk menggambarkan kejadian stunting di Wilayah Kerja Puskesmas Mapilli dengan jenis penelitian kuantitatif dengan pendekatan deskriptif yang bersifat retrospektif. Metode yang digunakan adalah metode analisis data sekunder. Penelitian ini dilakukan di Desa Ugi Baru, wilayah kerja Puskesmas Mapilli, Kecamatan Mapilli, Kabupaten Polman. Populasi pada penelitian ini adalah balita usia 13-59 bulan yang mengalami Stunting di Desa Ugi Baru pada tahun 2021 dengan total jumlah Bayi dan Balita Stunting sebanyak 23 Balita. Teknik pengambilan sampel pada penelitian ini adalah teknik total sampling. Hasil penelitian menunjukkan bahwa kejadian balita stunting di wilayah kerja Puskesmas Mapilli Desa Ugi Baru berdasarkan jenis kelamin yaitu balita lebih banyak mengalami stunting sebanyak 11 balita (60,9%) dan perempuan sebanyak 8 balita (39,1%) dan berdasarkan Usia, masa toddler 13-36 Bulan lebih sedikit sebanyak 11 balita (47,82%), dibandingkan masa praschool 37-59 Bulan sebanyak 12 balita (52,17%).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.048 | 0.008 |
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".