ANALISIS KINERJA KEUANGAN DAN NON KEUANGAN RUMAH SAKIT SEBELUM DAN SESUDAH BADAN LAYANAN UMUM DAERAH
Bibliographic record
Abstract
Diterbitkannya Peraturan Menteri Dalam Negeri Nomor 61 Tahun 2007 tentang Pedoman teknis Pola Pengelolaan Keuangan Badan Layanan Umum Daerah mengharuskan Pemerintah Daerah menganut PPK - BLUD dalam manajemen Rumah Sakit dalam rangka meningkatkan pelayanan kesehatan bagi masyarakat. Penelitian ini bertujuan untuk melihat perbedaan kinerja keuangan dan non keuangan RSUD Dr Moewardi sebelum dan sesudah berstatus BLUD. Kinerja keuangan diukur dengan rasio likuiditas, rasio aktivitas, rasio profitabilitas, dan rasio struktur modal. Sedangkan kinerja non keuangan diukur dengan rasio efisiensi pelayanan yaitu Bed Occupancy Rate, Bed Turn Over, Turn Over Interval, Average Length Of Stay, Gross Death Rate dan Net Death Rate. Teknik analisis yang digunakan adalah Paired Sample T Test. Hasil uji statistik menunjukkan tiga dari empat kelompok rasio keuangan yang diuji memiliki nilai Asymp. Sig. (2-tailed) kurang dari 0,05 sehingga dapat disimpulkan terdapat perbedaan signifikan pada kinerja keuangan RSUD Dr Moewardi sebelum dan sesudah BLUD, sedangkan pada rasio efiseiensi pelayanan hanya dua dari enam rasio yang memiliki nilai Asymp. Sig. (2-tailed) kurang dari 0,05 sehingga dapat disimpulkan tidak terdapat perbedaan signifikan pada kinerja efisiensi pelayananRSUD Dr Moewardi sebelum dan sesudah BLUD.Kata kunci :BLUD, kinerja keuangan, kinerja efisiensi pelayanan, rasio keuangan
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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 | 0.003 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".