PENGARUH PEMBERIAN JUS APEL TERHADAP EMESIS GRAVIDARUM PADA IBU HAMIL TRIMESTER I DI KLINIK ELIZA BESTARI
Bibliographic record
Abstract
Menurut WHO (2019), bahwa angka kejadian emesis gravidarum yang terjadi di dunia sangat beragam yaitu 10.8% di China, 2.2% di Pakistan, 1-3% di Indonesia, 1.9% di Turki, 0.9% di Norwegia, 0.8% di Canada, 0.5% di California, 0,5%-2% di Amerika emesis gravidarum berkisar antara 1 sampai 3 persen dari seluruh kehamilan. Rasio kejadian keseluruhan adalah 4: 1000. kejadian mual muntah pada ibu hamil di Indonesia berkisar antara 50% sampai 75% selama trimester pertama atau awal kehamilan. Tujuan Penelitian : Untuk menganalisis bahwa jus apel adakah pengaruh untuk mengatasi emesis gravidarum pada ibu hamil trimester I di Klinik Eliza Bestari. Metode Quasy Eksperimental dengan design one group pre-test post-test populasi seluruh ibu hamil yang melakukan kunjungan Antenatal Care (ANC) di Klinik Eliza Bestari, pengambilan sampel dilakukan dengan purposive sampling dengan jumlah sampel 30 responden. Pengambilan data menggunakan pengamatan atau observasi, dapat menggunakan instrument penelitian berupa pengamatan Paduan observasi (Observation sheet atau Observation schedule) analisi nilai p (0,000) ˂ α (0,005). Hal ini menunjukkan bahwa jus apel lebih efektif dalam mengurangi emesis gravidarum pada ibu hamil trimester I. Kesimpulan : pemberian jus apel terbukti efektif dalam penurunan emesis gravidarum pada ibu hamil trimester I di klinik eliza bestari.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".