Pengelolaan Manajemen Kemiskinan Ekstrem Terhadap Penurunan Angka Stunting Di Kabupaten Musi Banyuasin
Bibliographic record
Abstract
Penurunan angka stunting menjadi salah satu fokus utama dalam pembangunan kesehatan di Indonesia, terutama di daerah-daerah dengan tingkat kemiskinan ekstrem yang tinggi. Kabupaten Musi Banyuasin merupakan salah satu wilayah yang menghadapi permasalahan kemiskinan ekstrem dan angka stunting yang signifikan. Penelitian ini bertujuan untuk menganalisis pengelolaan manajemen kemiskinan ekstrem terhadap penurunan angka stunting di Kabupaten Musi Banyuasin. Metode yang digunakan adalah pendekatan deskriptif kualitatif dengan pengumpulan data melalui Teknik menganalisis reduksi, penyajian data, verifikasi dan dianalisis secara kualitatif deskriptif. Hasil penelitian menunjukkan bahwa manajemen kemiskinan ekstrem yang melibatkan berbagai program pemerintah, seperti pemberian bantuan sosial, pemberdayaan ekonomi keluarga, peningkatan akses terhadap pelayanan kesehatan, serta program peningkatan gizi, memiliki kontribusi signifikan dalam penurunan angka stunting. Meskipun demikian, masih terdapat tantangan dalam implementasi, seperti keterbatasan infrastruktur, kurangnya kesadaran masyarakat tentang pentingnya gizi, dan kurangnya koordinasi antara berbagai pihak terkait. Penelitian ini menyimpulkan bahwa keberhasilan penurunan kemiskinan ekstrem dalam menurunkan angka stunting di Kabupaten Musi Banyuasin dengan mengadopsi prinsip-prinsip pengelolaan manajemen, memerlukan sinergi antara pemerintah, masyarakat, dan sektor swasta, serta fokus pada peningkatan kualitas pelayanan dasar yang meliputi kesehatan, pendidikan, dan pangan.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 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".