PERBEDAAN PENGETAHUAN IBU HAMIL TENTANG TRIPEL ELIMINASI SEBELUM DAN SESUDAH PEMBERIAN EDUKASI DENGAN BOOKLET
Bibliographic record
Abstract
ABSTRAKLatar belakang : Eliminasi penularan HIV, sifilis dan hepatitis B dari ibu ke anak dalam program tripel eliminasi perlu dilakukan penanggulangan yang terintegrasi, komprehensif berkesinambungan, efektif dan efisien dengan indikator berupa infeksi baru HIV, sifilis dan atau hepatitis B pada anak kurang dari atau sama dengan 50/100.000kelahiran hidup.Pelaksanaan tripel eliminasi dilakukan melalui promosi kesehatan, surveilans kesehatan, deteksi dini dan atau penanganan kasus.Pengetahuan ibu hamil dapat ditingkatkan dengan melakukan edukasi menggunakan media booklet yang mengutamakan pesan-pesan visual dalam bentuk buku baik berupa tulisan atau gambar.Tujuan : Menganalisis perbedaan pengetahuan ibu hamil tentang tripel eliminasi sebelum dan sesudah pemberian edukasi dengan booklet.Metode : Jenis penelitian adalah pre eksperimen, dengan desain one group pre test and post test design menggunakan pendekatan cross sectional.Pengumpulan data secara observasi menggunakan kuesioner pada 33 responden.Analisa data yang digunakan secara univariat dan bivariat menggunakan uji pair "t" test Wilcoxon.Hasil : Pengetahuan ibu hamil sebelum pemberian edukasi didapatkan nilai mean 52,67, dengan standar deviasi 15,753, sedangkan pengetahuan ibu hamil sesudah pemberian edukasi didapatkan nilai mean 91,30dengan standar deviasi 11,698.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.071 | 0.013 |
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".