Technical Efficiency of Public Service Hospitals in Indonesia: A Data Envelopment Analysis (DEA)
Bibliographic record
Abstract
In order to promote efficiency and the development of hospitals’ services, the Government of Indonesia has issued specific policy which require all of the government-owned hospitals to be managed based on the principals applied in public services agency (Badan Layanan Umum/Badan Layanan Umum Daerah (BLU/BLUD)). The policy of BLU/BLUD is to grant each hospital authorization in managing their funds and resources under the principles of public accounting. Unfortunately, not all government-owned hospitals were granted BLU/BLUD authorization, especially hospitals outside of Jakarta, because local government did not wish to lose one of their main income. The main focus of this research is to calculate the efficiency of the hospitals of which have been granted BLU/BLUD, since one of the main purposes of BLU/BLUD is to provide high quality and efficient health care to the public. The measurement of hospitals’ efficiency is not an easy thing to do, since there are so many inputs and outputs that were related to each other. Which is why, this research is measuring the efficiency level using the DEA (Data Envelopment Analysis) which is able to provide efficiency calculation with multiple inputs and outputs. With the total samples of 82 BLU/BLUD hospitals, this research concluded that the average of hospital’s efficiency score is still on the level of 78.9 % out of 100%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.060 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.009 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".