Bibliographic record
Abstract
Malaria merupakan masalah kesehatan dunia yang menjadi penyebab utama morbiditas dan mortalitas di Benua Afrika dan Asia. Diperkiraan 30 ribu orang meninggal dunia dengan lebih dari 15 juta penderita klinismalaria di Indonesia. Tujuan penelitian ini menentukan faktor risiko yang terkait dengan malaria di wilayah kerja Puskesmas Kecamatan Cikeusik Kabupaten Pandeglang. Desain penelitian ini menggunakan kasus kontrol,dengan menganalisis data kasus kontrol dan data kesehatan masyarakat. Jumlah sampel sebanyak 378 responden. Hasil penelitian ditemukan bahwa terdapat tujuh variabel yang merupakan faktor risiko malaria (OR>1). Namun, faktor risiko yang bermakna secara statistik yaitu umur (OR=2,032;95%CI=1,309-3,154), pekerjaan (OR=3,868; 95% CI=2,00-7,48), lama tinggal di daerah endemis (OR=1,848; 95% CI=1,043-3,273), kebersihan lingkungan (OR=1,810;95%CI=1,154-2,839), dan penggunaan obat anti nyamuk (OR=8,183;95%CI=4,988–13,422). Darianalisis multivariat dengan uji korelasi spearman, ditemukan faktor risiko yang paling dominan menyebabkan malaria di daerah Pandeglang yaitu penggunaan obat anti nyamuk (B=2,227;OR=9,271). Disimpulkan bahwaumur, pekerjaan, lama tinggal di daerah endemis, kebersihan lingkungan, dan penggunaan obat anti nyamuk merupakan faktor risiko kejadian malaria di wilayah tersebut.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".