The impact of health sector evolution plan on hospitalization and cesarean section rates in Iran: an interrupted time series analysis
Bibliographic record
Abstract
OBJECTIVE: To investigate the effect of the health sector evolution plan (HSEP) on hospitalization and cesarean section (C-section) rates in Kermanshah province in the western region of Iran. DESIGN: Interrupted time series analysis. SETTING: Hospital care system in Kermanshah province. STUDY PARTICIPANTS: Fifteen hospitals affiliated to Ministry of Health and Medical Education (MoHME) in Kermanshah province. INTERVENTION(S): Health sector evolution plan. MAIN OUTCOME MEASURES: Hospitalization rate and C-section rate. RESULTS: We observed a statistically significant increase in the hospitalization rate (12.9 hospitalizations per 10 000 population, P < 0.001) in the first month after the implementation of the HSEP. Compared with the monthly trend in hospitalization rate before the intervention, we found a significant increase of 0.70 hospitalizations per 10 000 population (P < 0.001) in monthly trend in hospitalization rate after the HSEP. Although the proportion of C-section from total deliveries decreased by 11% (P = 0.044) in the first month after the implementation of the HSEP, the proportion of C-section from total deliveries increased at the rate of 0.0017% (P = 0.001) per month during post-intervention period. CONCLUSION: We found an increase in the hospitalization rate after the intervention of HSEP. Although the C-section rate in the first month after the HSEP decreased, we observed an increasing trend in C-section rate over the study period; this implies that the HSEP did not promote vaginal delivery in Iran, which is outlined as one of the objectives of the intervention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".