HUBUNGAN KINERJA PELAYANAN DENGAN PROFITABILITAS DI RSUD TONGAS KABUPATEN PROBOLINGGO
Bibliographic record
Abstract
This study aims to find out whether there is a significant relationship between Service Performance and Profitability in the Tongas District Probolinggo Hospital in 2013 - 2017. The population used in this study is the Inpatient Census Report taken from indicators of Hospital Service Performance and Financial Statements of the Tongas District Hospital in Probolinggo in the form of Semester reports from 2013-2017.The results showed that there was no significant effect between the variables X, namely Service Performance where in this study using the BOR (Bed Occupancy Ratio) indicator, ALOS (Average Length of Stay), TOI (Turn Over Interval), BTO (Bed Turn Over) , GDR (Gross Death Rate) and NDR (Net Death Rate) for Y variables, namely ROA, NPM and ROE (Profitability). This is because the RSUD Tongas Probolinggo Regency is a regional public hospital not a private hospital where the number of patients treated and the number of patients treated the majority use health insurance cards, namely BPJS Kesehatan. The director must immediately take a policy to raise the type of hospital from type D to type C so that the claim rates for BPJS patients will rise and make changes to the applicable rates at the Hospital. By increasing the type of automatic hospital also to have an impact on the increase in income which will affect profitability.Keywords: BOR, ALOS, TOI, BTO, GDR and NDR and ROA, NPM and ROE.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".