The impact of Health Sector Evolution Plan on the performance of hospitals in Iran
Bibliographic record
Abstract
Purpose The Health Sector Evolution Plan (HSEP) is the most recent reform in Iran’s health care system that was launched in May 2014 in all university-affiliated hospitals to reduce health care expenditure for patients, while improving the efficiency and quality of hospital services. The purpose of this paper is to evaluate the impact of the HSEP on the performance of 15 hospitals affiliated with Kermanshah University of Medical Sciences (KUMS), located in the western region of Iran. Design/methodology/approach The Pabon Lasso model was used to measure the performance of hospitals before and after the implementation of the HSEP in 2013-2014 and 2015-2016, respectively. Three indicators of average length of stay (ALoS), bed occupancy rate (BOR) and bed turnover rate (BTR) were analyzed by the Pabon Lasso model. Findings The results showed that the average ALoS, BTR and BOR before the introduction of the HSEP were 2.59 days, 92 times and 57 percent, respectively, and the corresponding figures for these indicators after the implementation of the HSEP were 2.61 days, 98.9 times and 59.9 percent. The results indicated that before the introduction of the HESP, 40 percent of hospitals were in zone 1 (poor performance: low BTR and BOR and high ALoS), 27 percent in zone 2, 20 percent in zone 3 (good performance: high BTR and BOR and low ALoS) and 13 percent in zone 4. After the HSEP, the proportion of hospitals in zones 1-4 was 33, 27, 20 and 20 percent, respectively. Originality/value This study is the first to use the Pabon Lasso model technique to evaluate the impact of the HSEP on hospitals affiliated with KUMS.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".