GAMBARAN PENERAPAN MANAJEMEN KEBIDANAN TERHADAP CAKUPAN PERSALINAN DI WILAYAH KERJA PUSKESMAS BINUANG TAHUN 2022
Bibliographic record
Abstract
Menurut hasil evaluasi program di Kabupaten Polewali Mandar, di daerah itu banyak bidan. Di Polewali Mandar tercatat hampir 35 kasus kematian bayi pada tahun 2018 dan 2019. Bayi terbesar di Provinsi Sulawesi Barat berada di Kabupaten Polewali Mandar. Tujuan dari penelitian ini adalah untuk memahami implikasi dari Gambaran Penerapan Manajemen Kebidanan terhadap Cakupan Persalinan di Wilayah Kerja Puskesmas Binuang Tahun 2022. Jenis penelitian yang dilakukan adalah penelitian kuantitatif dengan menggunakan protokol desk-top scribing untuk mendapatkan lebih banyak informasi. informasi mendalam tentang fungsi bidan desa di Wilayah Kerja Puskesmas Binuang. Ditunjukkan dengan adanya posyandu setiap bulannya, selesainya kegiatan kegiatan dan proyek, dan penggunaan data penginputan baik offline maupun online, hasil kajian mengenai analisis situasi menunjukkan bahwa kegiatan dan proyek yang dilakukan oleh petugas kesehatan berjalan sesuai rencana dan sesuai dengan aturan yang ditetapkan puskesmas.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.085 | 0.024 |
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".