PERILAKU PENCEGAHAN TERHADAP KEJADIAN DEMAM BERDARAH DENGUE PADA MASYARAKAT
Bibliographic record
Abstract
Penyakit Demam Berdarah Dengue (DBD) merupakan salah satu masalah kesehatan masyarakat di Indonesia karena dapat menyerang seluruh kelompok umur dan sering menimbulkan kematian. Angka kesakitan di wilayah Kabupaten Cianjur pada tahun 2018 yaitu terdapat 361 kasus DBD yang tersebar di wilayah kerja dinas kesehatan Cianjur dengan angka kesakitan sebanyak 1543 per 100.000 penduduk dan angka kematian 0,28% per 100.000 penduduk. Salah satu faktor penyebab adalah perilaku pencegahan masyarakat yang kurang baik. Tujuan penelitian yaitu untuk mengetahui hubungan perilaku pencegahan terhadap kejadian DBD pada masyarakat. Metode penelitian menggunakan survei Analitik Cross sectional. Teknik pengambilan sampel yaitu Stratified Random Sampling dengan perolehan sampel 100 responden. Hasil penelitian menunjukkan sebanyak 100 responden ditemukan berperilaku baik melakukan pencegahan DBD sebanyak 82 (82%) responden, sedangkan 18 (18%) responden yang berperilaku buruk tidak melakukan pencegahan DBD. Responden yang ditemukan terjangkit DBD sebanyak 28 (28%) responden, sedangkan responden yang tidak terjangkit DBD sebanyak 72 (72%) responden. Saran: Tim Kesehatan perlu meningkatkan pengetahuan promotif dan preventif yang berkaitan dengan perilaku 3M Plus pada masyarakat agar terbebas dari DBD. Kata Kunci : Demam Berdarah Dengue, Perilaku Pencegahan
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".