Analisis Pengetahuan Ibu Hamil Dengan Perencanaan Persalinan dan Pencegahan Komplikasi
Bibliographic record
Abstract
Pendahuluan Kematian ibu di Indonesia yaitu sebesar 359/100.000 kelahiran hidup. Hal ini dapat dicegah melalui beberapa cara yaitu melalui perencanaan persalinan di tempat kesehatan, ditolong oleh tenaga kesehatan, persiapan biaya maupun dukungan keluarga. Tujuan penelitian ini yaitu menganalisis pengetahuan ibu hamil tentang faktor resiko tinggi dan komplikasi persalinan terhadap perencanaan persalinan. Desain penelitian yang digunakan dalam penelitian ini adalah survey analitik dengan pendekatan cross sectional. sampel dalam penelitian ini sebanyak 63 responden. Pengumpulan data dilakukan bulan Desember 2017. Instrumen yang digunakan untuk mengumpulkan data adalah kuesioner. Hasil uji statistik chi square diperoleh p value = 0,048 (p 0,05) yang berarti ada hubungan antara pengetahuan ibu hamil dengan perencanaan persalinan dan pencegahan komplikasi. Oleh karena itu diharapkan untuk peneliti selanjutnya menambah variabel penelitian terkait perencanaan persalinan dan pencegahan komplikasi.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".