PENGARUH PEMBERIAN EKSTRAK KACANG HIJAU TERHADAP PENINGKATAN KADAR HEMOGLOBIN PADA IBU HAMIL TRIMESTER III TAHUN 2022-2023
Bibliographic record
Abstract
Pendahuluan: Angka kejadian anemia pada ibu hamil di dunia menurut data WHO (2015) menunjukkan prevalensi sebesar 38,2%, sedangkan angka kejadian anemia pada ibu hamil di Indonesia berdasarkan data Riskesdas tahun 2018 sebesar 48,9%. Jumlah ini meningkat dari tahun sebelumnya yaitu 37,1% pada tahun 2013. Tindakan pencegahan anemia tidak hanya secara farmakologi tetapi dapat menggunakan cara non farmakologi dengan pemberian kacang hijau. zat gizi pada kacang hijau seperti sumber protein, kaya serat, rendah karbohidrat, lemak sehat dan kaya vitamin. Tujuan: menganalisis pengaruh pemberian kacang hijau terhadap peningkatan kadar hemoglobin pada ibu hamil trimester III. Metode: Rancangan penelitian yang digunakan adalah Pre-Experimental Design dengan bentuk One Group Pretest-Posttest. Pada desain ini, sebelum diberikan perlakuan, sampel terlebih dahulu diberikan pretest (tes awal) dan setelah eksperimen, sampel diberikan posttest (tes akhir). Hasil: Menggunakan independent t-test menunjukkan perbedaan peningkatan kadar hemoglobin pada ibu hamil. Nilai p = 0,045 artinya ada perbedaan kadar hemoglobin pada ibu hamil yang rutin minum ekstrak kacang hijau dengan ibu hamil yang tidak rutin minum di wilayah kerja Puskesmas Kabupaten Kemayoran. Kesimpulan: Bahwa ada pengaruh pemberian ekstrak kacang hijau dengan tablet Fe terhadap peningkatan kadar Hb pada ibu hamil di Puskesmas Kemayoran.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".