HUBUNGAN IBU BERSALIN TERKONFIRMASI COVID -19 DENGAN JENIS PERSALINAN DI WILAYAH KERJA PUSKESMAS SLAWI
Bibliographic record
Abstract
Derajat kesehatan masyarakat Indonesia terlihat dari Angka Kematian Ibu dan Angka Kematian Bayi. AKI dan AKB tersebut digunakan sebagai indicator pelayanan kesehatan Ibu dan Bayi. Prevalensi AKI di Kabupaten Tegal Tahun 2021 sebesar 30 orang. Penyebab AKI tersebut dikarenakan Covid-19 (40%), Pre Eklampsia Berat (30%), Perdarahan (20%), lain-lain (10%) (Dinkes Kabupaten Tegal, 2021). Berdasarkan data di Puskesmas Slawi Tahun 2021 terdapat 350 ibu yang melahirkan di Puskesmas Slawi. Dari 350 ibu bersalin terdapat 2.6% ibu bersalin yang terkonfirmasi COVID-19. Dilihat dari jenis persalinan, 40% jenis persalinan dilakukan dengan tindakan (rujuk) dan 60% dengan persalinan spontan.Metode penelitian adalah korelasi dengan pendekatan cross sectional. Dilaksanakan di Puskesmas Slawi pada bulan Januari – Mei 2022, sampel yang digunakan adalah 130 ibu bersalin yang memenuhi kriteria inklusi dan kriteria eksklusi, menggunakan data sekunder dengan uji statistik Chi-square. Hasil: berdasarkan uji statistik didapatkan nilai p sebesar 0.266 sehingga dapat disimpulkan Tidak Terdapat Hubungan Ibu Bersalin Terkonfirmasi COVID-19 dengan Jenis Persalinan di Wilayah Kerja Puskesmas Slawi Kabupaten Tegal.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".