Pengaruh pemberian dimsum boster (brokoli, sapi, dan teri) terhadap status gizi kurang pada balita stunting
Bibliographic record
Abstract
Latar Belakang: Salah satu target SDGs (Sustainable Development Goals) adalah sistem kesehatan nasional pada tahun 2030, seluruh negara berupaya untuk menurunkan angka kematian balita sebesar 25/1.000 kelahiran hidup. Selain itu pada target gizi masyarakat tahun 2030, seluruh negara berupaya untuk mengakhiri segala bentuk malnutrisi, termasuk mencapai target Internasional 2025 yang bertujuan untuk menurunkan stunting dan wasting pada balita dan mengatasi kebutuhan gizi remaja perempuan, wanita hamil dan menyusui, serta lansia.Metode: Jenis penelitian yang digunakan adalah Quasi Eksperimen, menggunakan rancangan one group pretest and post-test. dan pengumpulan data menggunakan pengukuran dengan prosedur Antropometri. Jumlah sampel pada penelitian ini adalah balita stunting dengan status gizi kurang sebanyak 24 orang. Uji statistik yang digunakan adalah Uji Unpaired sampel t-test.Hasil: Analisis bivariat menunjukkan bahwa terdapat pengaruh status gizi kurang pada balita stunting sebelum dan sesudah pemberian dimsum boster (p-value 0,000 < 0,005).Kesimpulan: Adanya pengaruh pemberian dimsum boster (brokoli, daging sapi, dan ikan teri) terhadap status gizi kurang pada balita stunting di Wilayah Puskesmas Kawalu Kota Tasikmalaya.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.032 | 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".