Comparing the Efficiency of Hospitals in Northern Iran Before and After the Covid-19 Pandemic Using the Pabon Lasso Model
Bibliographic record
Abstract
The Pabon Lasso Model is often used to assess the efficiency of hospitals as the most important component of the health care system. The present study sought to evaluate and compare the efficiency of hospitals in northern Iran before and after the COVID-19 pandemic using the Pabon Lasso Model. This descriptive study was conducted in 36 public, private, and social security hospitals in northern Iran from 2019 to 2020. The hospitals were selected using the census method. The data were collected using the forms approved by the Ministry of Health and Education. The three indices of bed occupancy rate, bed turnover frequency, and the average length of stay were calculated and plotted using the Pabon Lasso graphs. The collected data were analysed using the paired samples t-test. The average bed occupancy rates in 2019 and 2020 were equal to 67.72% and 52.28%, lower than the national standard rate. Moreover, the average lengths of stay were 2.58 and 2.83 days and the bed turnover rates were 96.7 and 77.94, higher than the national standard rate. Of the total 36 hospitals in 2019, 33.3% of hospitals were in the first quarter (low efficiency), and 16.6% in the third quarter (high efficiency). Furthermore, the data for 2020 indicated that 38.8% of hospitals were in the first quarter (low efficiency), showing an increase compared to 2019 and 19.4% of hospitals were in the third quarter, indicating a decrease compared to 2019. The paired samples t-test indicated that the bed occupancy rate and bed turnover showed significant differences in 2019 and 2020 (P-value<0.05). The data confirmed that the average length of stay and bed turnover in the studied hospitals were favourable. In addition, unlike private hospitals, the number of efficient units in public hospitals decreased during the COVID-19 pandemic. Thus, hospital managers need to pay more attention to improving performance indicators and increasing productivity in these hospitals.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".