Optimalisasi Pemenuhan Asupan Gizi Terpadu Dalam Meningkatkan Kualitas Sumber Daya Manusia
Bibliographic record
Abstract
Investasi pemerintah dalam kesehatan masyarakat berperan penting dalam meningkatkan produktivitas jangka panjang dan kualitas sumber daya manusia. Tingginya prevalensi stunting di Indonesia menunjukkan adanya tantangan serius, terutama terkait kekurangan energi dan anemia pada anak serta ibu hamil sebagai faktor utama. Melalui pendekatan kuantitatif yang memanfaatkan berbagai sumber data dan literatur relevan, studi ini merumuskan strategi intervensi gizi yang lebih tepat sasaran untuk percepatan penurunan stunting. Temuan menunjukkan bahwa periode emas 1.000 Hari Pertama Kehidupan (HPK) merupakan fase paling efektif untuk memperbaiki kondisi gizi anak. Namun, beberapa program intervensi, termasuk program makan bergizi gratis, belum sepenuhnya memprioritaskan balita dan ibu hamil. Karena itu, diperlukan kebijakan pemenuhan gizi terpadu yang menyinergikan berbagai program yang ada dan berfokus pada kelompok rentan. Dokumen ini diharapkan menjadi kontribusi bagi perumusan kebijakan berbasis bukti dalam upaya percepatan penurunan stunting dan peningkatan kualitas sumber daya manusia di Indonesia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 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".