Measuring the Efficiency of Health Services Areas in Kingdom of Saudi Arabia Using Data Envelopment Analysis (DEA): A Comparative Study between the Years 2014 and 2006
Bibliographic record
Abstract
The study aimed to assess the efficiency of health services provided by the government hospitals in various districts of the Kingdom of Saudi Arabia. The number of beds at hospitals, doctors, nursing staff and paramedical categories were used as inputs for the model. The average productivity efficiency of government hospitals in the districts of the Kingdom of Saudi Arabia in 2014 was 92.3%; whereas, the average internal production efficiency of these districts in the provision of health services through their respective hospitals was 94.7%; and the average external productivity efficiency in the different cities of the districts in Kingdom of the Saudi Arabia was 97.5%. It has been found that the average overall productivity efficiency was 90.2%, concerning the relative efficiency indicators of government hospitals, which were based on the hospitals’ distribution of Saudi Arabian districts in 2006. An analysis of the indicator showed that the average production efficiency of the services provided (internally) by the districts of the Kingdom of Saudi Arabia was 94.7%, and that the average of the external production efficiency for such services was 95.4%. The Data Envelopment Analysis is a successful technique in measuring the performance efficiency of hospitals and it also assists to identify possible improvement and reduction in cost.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 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".