Bibliographic record
Abstract
Penelitian ini dilakukan untuk mendiskripsikan proses implementasi sistem rujukan ibu hamil dan ibu bersalin oleh bidan Polindes di wilayah Kecamatan Dampit dan faktor – faktor yang mendukung dan menghambat pada proses tersebut. Penelitian ini merupakan jenis penelitian kualitatif dengan pendekatan studi kasus. Tehnik pengumpulan data menggunakan wawancara, dokumentasi dan focus group discussion. Informan terdiri atas Kepala Puskesmas, Bidan dan pasien. Pengambilan sampel dengan tehnik purposive sampling. Analisa data dengan analisa isi. Hasil penelitian menggambarkan bahwa jumlah rujukan cukup banyak, SOP sudah tersedia. Tujuan rujukan adalah Puskesmas/Rumah Sakit dan dokter spesialis. Kasus yang dirujuk mengacu pada standar penapisan 18 indikasi rujukan ibu bersalin. Perlengkapan yang dibawa bidan adalah set alat dan obat. Jalur rujukan dari polindes ke Puskesmas, ke Rumah sakit, ke dokter spsesialis, ke Puskesmas lalu ke rumah sakit. Pendampingnya bidan, keluarga dan sopir. Persiapan sebelum dirujuk adalah perlengkapan ibu, perlengkapan bayi, uang dan syarat-syarat administrasi. Alat transportasi menggunakan kendaraan milik pribadi, milik bidan, ambulan desa, ambulan Puskesmas, ambulan Rumah Sakit. Biaya menggunakan asuransi atau membayar tunai. Faktor-faktor yang mempengaruhi proses rujukan meliputi: biaya, pasien, pengambilan keputusan, rumah sakit yang dituju, transportasi, kompetensi bidan, status domisili pasien dan kepercayaan masyarakat.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.499 | 0.309 |
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".