MOTIVASI WANITA PASANGAN USIA SUBUR DALAM PEMERIKSAAN IVA DI DESA CANGKORAH BATUJAJAR
Bibliographic record
Abstract
Latar Belakang: Penyakit kanker merupakan penyebab kematian terbanyak di dunia. Setiap tahun 12 juta orang di dunia menderita kanker dan 7,6 juta diantaranya meninggal dunia. Dalam upaya penanggulangan kanker pemerintah melaksanakan program khusus deteksi dini pada perempuan Indonesia untuk kanker leher rahim yaitu pemeriksaan IVA. Cakupan pemeriksaan IVA di Indonesia tahun 2016 yaitu 4,34% hal ini masih jauh dari target nasional yaitu 10%.Tujuan Penelitian: Untuk mengetahui hubungan motivasi dengan keikutsertaan melakukan deteksi dini kanker serviks metode IVA Tes di Wilayah Kerja Puskesmas Batujajar Desa Cangkorah Tahun 2018.Metode Penelitian: Penelitian ini menggunakan metode penelitian analitik dengan desain Cross sectional. Sampel pada penelitian ini adalah wanita pasangan usia subur yang berada di desa Cangkorah sebanyak 285 orang dengan teknik stratified random sampling, di analisis secara bivariat menggunakan Chi Square.Hasil: Lebih dari setengah (59,6%) atau 170 orang memiliki motivasi yang rendah dalam pemeriksaan IVA Tes dan hanya sebagian kecil (9,1%) atau 26 orang yang pernah melakukan pemeriksaan IVA Tes.Simpulan: Terdapat hubungan antara motivasi wanita pasangan usia subur dan keikutsertaan melakukan deteksi dini kanker serviks metode IVA tes di wilayah kerja Puskesmas Batujajar Desa Cangkorah Tahun 2018.
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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 | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.005 |
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".