The effect of Iran’s health sector evolution plan on hospitals performance indicators: an interrupted time series analysis
Bibliographic record
Abstract
Background: The Health Sector Evolution Plan (HSEP) was set up in Iran's health system to respond to some of the main problems in hospitals and other health sectors. We aimed to compare the effect of the HSEP on teaching hospital performance before and after the implementation of the HSEP through the interrupted time series (ITS) analysis. Methods: With a cross-sectional design, data collection was performed in 17 teaching hospitals affiliated with the Kermanshah University of Medical Sciences (KUMS). We used the existing data on three indicators of hospitalization rate (per 10,000 population), Emergency Department Visits (EDVs) (per 10,000 population), and in-hospital mortality (per 10,000 population). The monthly data from 2009 to 2019 was analyzed by the ITS method 60 months before and 61 months after the HSEP. Results: We found a non-statistically significant decrease in the monthly trend of hospitalization rate relative to the period before the HSEP implementation (-0.084 per 10,000 population [95%CI: -0.269, 0.101](. There was a statistically significant increase in the monthly trend of EDVs rate compared to before the HSEP implementation (1.07 per 10,000 population [95%CI: 0.14, 2.01]). Also, a significant decrease in the monthly trend of in-hospital mortality compared to before the HSEP implementation [-0.003 per 10,000 population (95%CI: -0.006, -0.001)] was observed. Conclusion: Our study demonstrated a significant increasing and decreasing trend for EDVs and in-hospital mortality following the HSEP implementation, respectively. Regarding the increase in hospitalization rate and EDVs after the implementation of HESP, it seems that there is a need to increase investment in healthcare and improve healthcare infrastructure, human resources-related indicators, and the quality of healthcare.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".