FAKTOR-FAKTOR YANG BERHUBUNGAN DENGAN PEMANFAATAN PELAYANAN KESEHATAN OLEH PASIEN LUAR WILAYAH DI PUSKESMAS TANAH SAREAL KOTA BOGOR TAHUN 2018
Bibliographic record
Abstract
Jumlah kunjungan pasien yang memanfaatkan pelayanan kesehatan di Puskesmas Tanah Sareal Kota Bogor pada tahun 2017 sebanyak 58.340 dengan rata-rata kunjungan 200 pasien/hari. Dari jumlah kunjungan tersebut, kunjungan pasien dalam wilayah hanya 13.727 (24%) dan 44.613 (76%) sisanyamerupakan pasien luar wilayah kerja Puskesmas Tanah Sareal. Penelitian ini bertujuan untuk mengetahui gambaran dan hubungan antara karakteristik predisposisi (umur, pendidikan, pekerjaan),karakteristik pendukung (ketersediaan tenaga kesehatan, aksesibilitas, kepemilikan asuransi kesehatan), dan karakteristik kebutuhan (persepsi sakit) pasien luar wilayah terhadap pemanfaatan pelayanan kesehatan di Puskesmas Tanah Sareal Kota Bogor tahun 2018. Jenis penelitian ini adalahpenelitian deskriptif kuantitatif dengan desain cross sectional, dengan jumlah sampel 110 responden. Teknik pengambilan sampel menggunakan teknik Insidental Sampling. Pengumpulan data menggunakan instrumen berupa kuisioner dan dianalisis menggunakan uji Chi Square. Hasil analisaChi Square menunjukan bahwa ada hubungan antara variabel ketersediaan tenaga kesehatan (p=0,012) dan persepsi sakit (p=0,002) dengan pemanfaatan pelayanan kesehatan.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.006 |
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".