HUBUNGAN ASUPAN PROTEIN, ZAT BESI DAN POLA MAKAN TERHADAP KEJADIAN ANEMIA PADA IBU HAMIL TRIMESTER III DI WILAYAH KERJA PUSKESMAS CILACAP UTARA 2
Bibliographic record
Abstract
Anemia pada kehamilan dapat menyebabkan gangguan kelangsungan kehamilan, gangguan proses persalinan, gangguan pada masa nifas, dan gangguan pada janin. Beberapa penyebab anemia pada ibu hamil adalah asupan protein, zat besi dan pola makan yang kurang baik. Tujuan penelitian ini adalah untuk mengetahui hubungan antara asupan protein,zat besi dan pola makan dengan kejadian anemia pada ibu hamil trimester III di wilayah kerja Puskesmas Cilacap Utara 2 tahun 2019. Penelitian ini menggunakan desain survey analytic, rancangan pengambilan data case control dan pendekatan waktu retrospektif. Tehnik sampling menggunakan teknik purposive sampling, dengan besar sampel 66 orang ibu hamil trimester III. Hasil penelitian menunjukkan bahwa tidak ada hubungan antara asupan protein dan asupan zat besi dengan kejadian anemia pada ibu hamil trimester III di wilayah kerja Puskesmas Cilacap Utara 2 (pv= 0,819; pv= 0,120). Ada hubungan signifikan antara pola makan dengan kejadian anemia pada ibu hamil trimester III di wilayah kerja Puskesmas Cilacap Utara 2 (pv= 0,003; Odds Ratio= 5,290). Ibu hamil dengan pola makan kurang baik beresiko 5,29 kali mengalami anemia dibandingkan ibu hamil dengan pola makan yang baik.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".