Perbedaan Asupan Energi Protein, Frekuensi Jajan di Sekolah dan Status Gizi antara Anak Sekolah Dasar Penerima dan Bukan Penerima Program Makanan Tambahan Anak Sekolah
Bibliographic record
Abstract
Latar Belakang : Anak sekolah dasar merupakan kelompok rawan gizi yang rentan mengalami masalah gizi, yang dapat berdampak pada status gizi anak. Tujuan dilaksanakan Program Makanan Tambahan Anak Sekolah (PMT-AS) adalah untuk meningkatkan status gizi melalui pemberian makanan tambahan. Penelitian ini bertujuan untuk mengetahui perbedaan tingkat asupan energi protein, frekuensi jajan di sekolah, dan status gizi antara anak sekolah dasar penerima dan bukan penerima PMT-AS. Metode : Penelitian ini merupakan penelitian cross sectional. Subjek penelitian adalah murid kelas IV dan V SD Gabahan dan SD Kembangsari 01 yang diambil secara cluster sampling, besar sampel adalah 110 orang yang dibagi dalam 2 kelompok. Status gizi diukur menggunakan metode antropometri. Asupan makan dan frekuensi jajan diperoleh dengan metode wawancara dan food recall 3×24 jam. Analisis statistik yang digunakan adalah Independent sample t-test, Mann Whitney test, dan Wilcoxon. Hasil : : Pada kelompok penerima PMT–AS memiliki rerata yang lebih tinggi pada asupan energi dan status gizi. Frekuensi jajan kelompok bukan penerima PMT–AS memiliki rerata yang lebih tinggi. Simpulan : Asupan energi protein dan status gizi antara penerima dan bukan penerima PMT-AS tidak berbeda. Perbedaan antara penerima dan bukan penerima PMT-AS ditemukan pada frekuensi jajan di sekolah.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".