Optimalisasi Tumbuh Kembang Anak Balita Guna Mencegah Stunting
Bibliographic record
Abstract
Berdasarkan Riskesdas (2018), Kab. Pati menduduki peringkat kedua se-Jawa Tengah, dimana data dari Dinas Kesehatan Kabupaten Pati per April 2019 diperoleh bahwa wilayah Puskesmas Jakenan menduduki peringkat pertama. Penelitian Kasanah dan Muawanah (2020) menunjukkan bahwa ada perbedaan yang signifikan pada tinggi badan (TB) anak yang mendapat zinc (p value 0.001). Di samping itu, ibu-ibu kurang informasi dan keterampilan tentang bagaimana menyusun menu seimbang dengan benar sejak hamil, masa nifas/menyusui sampai bayi dan balita. Masyarakat perlu diberikan informasi dan edukasi kepada ibu nifas tentang asupan zat gizi yang seimbang bagi tumbuh kembang bayi/balita sejak dini dengan mengadakan pengabdian masyarakat guna meningkatkan keterampilan ibu hamil dan nifas sehingga dapat membersamai bayi/balitanya dalam proses tumbuh kembang dan akhirnya mampu menekan terjadinya stunting. Kegiatan dilaksanakan dalam 2 seri dengan mengangkat tema gizi seimbang sejak masa hamil, nifas/menyusui, bayi balita. Mengingat ada kebijakan PPKM pandemi covid-19 gelombang kedua di Jawa dan Bali mulai Juni 2021 maka kegiatan dilakukan daring menggunakan zoom (Juli dan Agustus 2021). Kegiatan zoom belum efektif meskipun evaluasi pre test rata-rata adalah 45 sedangkan rata-rata nilai post test adalah 85. Metode daring tidak dilaksanakannya praktik menyusun menu. Namun peserta telah mendapat contoh menu.
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 | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.009 |
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".