Peningkatan Edukasi Stroke Masyarakat dengan Video Edukasi Stroke – “CHERIE” (Cerebral Hemorrhage and Ischemic Educational Video)
Bibliographic record
Abstract
Berdasarkan data Kementerian Kesehatan, stroke merupakan penyebab kematian pertama untuk semua umur dengan persentase 15,4 persen, dan stroke juga menempati urutan pertama penyakit tidak menular yang mematikan. Time window yang ditujukan untuk pengobatan stroke adalah tiga jam, sehingga pengenalan gejala stroke sedini mungkin kepada masyarakat sangat penting. Tujuan Pengabdian masyarakat ini adalah untuk mengedukasi masyarakat dengan video edukasi stroke CHERIE (Cerebral Hemorrhage and Ischemic Educational Video) bagi masyarakat untuk lebih memahami penyakit stroke, mulai dari gejala hingga petunjuk penggunaan obat. Sasaran kegiatan ini adalah masyarakat umum di wilayah kabupaten Jember. Metode yang digunakan adalah memberikan edukasi dan mengukur tingkat pengetahuan melalui pretest dan post-test untuk mengetahui perubahan tingkat pengetahuan. Hasil dari kegiatan ini, sasaran mengalami peningkatan pengetahuan dengan rata-rata 42,8%, dengan nilai rata-rata pretest sebesar 68, dan rata-rata nilai post-test sebesar 95,3. Sehingga dapat disimpulkan bahwa kegiatan Pengabdian masyarakat dalam Upaya peningkatan edukasi masyarakat mengenai stroke melalui video CHERIE cukup efektif dalam meningkatkan pengetahuan masyarakat.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 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".