Comparing Performance of Selected Teaching Hospitals in Kerman and Shiraz Universities of Medical Sciences, Iran, Using Pabon-Lasso Chart
Bibliographic record
Abstract
Background: Statistical control is one of the tools in performance assessment and comparison of hospitals. In this regard the Pabon Lasso graph is used as a technique in hospitals. The current study aimed to compare the performance of teaching hospitals of Kerman and Shiraz Universities of Medical Sciences, Iran, in 2007 using the Pabon Lasso tools. Methods: This was a descriptive cross-sectional study. Eight teaching hospitals in Kerman and Shiraz were selected through purposive sampling. The data gathering instrument was the standard data form for hospital activities that had been verified by the Ministry of Health and Medical Education. Three indicators related to Pabon Lasso chart including Bed Occupancy Rate, Average Length of Stay and Bed Turnover Rate were calculated using the Excel software. Finally, the Pabon Lasso graph was used to rank the performance of the selected hospitals in terms of the indicators. Results: Two out of the 8 hospitals (25%) including Shafa and Khalili fell in the second quarter and 4 out of the 8 hospitals (50%) including Shahid Bahonar, Shahid Faghihi, Namazi and Afzalipour were placed in the third quarter of the chart. Also 2 out of the 8 hospitals were positioned in the fourth quarter. Overall, a separate comparison of the three indicators showed that Shiraz teaching hospitals have better efficiency and performance than Kerman teaching hospitals. Conclusion: Pabon Lasso graph can be used as a suitable tool in the performance assessment of hospitals. Also, it is recommended that these functional indicators be prioritized in the annual evaluation of hospitals. Keywords: Performance, Pabon-Lasso graph, Evaluation, Teaching hospital
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".