HUBUNGAN PENDIDIKAN KESEHATAN TENTANG TANDA BAHAYA KEHAMILAN DENGAN KEPATUHAN ANTENATAL CARE
Bibliographic record
Abstract
Perawatan kesehatan ibu diwujudkan melalui penyediaan layanan Antenatal Care setidaknya empat kali selama kehamilan, dengan distribusi setidaknya satu pada trimester pertama, satu di trimester kedua, dan dua kali pada trimester ketiga. Perawatan antenatal secara teratur dapat diharapkan setiap ibu hamil yang sehat selama kehamilan, persalinan, dan pascapersalinan. Indikator kinerja cakupan perawatan kesehatan ibu pusat K4 di provinsi Riau pada tahun 2015 belum tercapai sebesar 86,7% dari target Rencana Strategis (Perencanaan) Kementerian Kesehatan sebesar 95%. Tujuan dari penelitian ini adalah untuk mengetahui apakah hubungan antara pendidikan kesehatan tentang bahaya kehamilan dengan kepatuhan Antenatal Care. Penelitian dilakukan pada tanggal 30 November sampai 7 Desember 2016. Penelitian ini merupakan penelitian kuantitatif analitik observasional dengan rancangan cross-sectional study. Teknik sampling yang digunakan adalah purposive sampling sampling nonprobability dimana seluruh ibu hamil trimester ketiga masuk dalam rekam medik RB Siti Julaeha sebanyak 41 orang. Metode pengumpulan data menggunakan wawancara terstruktur dan daftar periksa. Data dianalisis dengan uji chi square. Hasil penelitian menunjukkan tidak ada hubungan antara pendidikan kesehatan tentang tanda bahaya kehamilan dengan kepatuhan Antenatal Care, dengan melihat nilai probabilitas (probabilitas) dengan menggunakan uji chi square dimana hasil pvalue = 0,410 alpha lebih besar dari 0,05.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".