Prerequisites of Preparedness against Earthquake in Hospital System: A Survey from Iran
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Considering the history of frequent, and severe, earthquakes in Iran and the importance of health care service delivery by hospitals in these cases, having a plan to deal with disasters should be considered a priority. The aim of this study was the observance of preparedness prerequisites against earthquake in hospitals affiliated with Shahid Beheshti University of Medical Sciences (SBUMS) and its relationship with demographic and organizational characteristics. METHODS: This was a cross- sectional study that was conducted in 15 hospitals affiliated with SBUMS, Iran in 2012. Data were collected using observation of documents and questionnaire consists of 138 questions in 8 dimensions. The content validity and reliability were confirmed. Data analysis was performed with descriptive statistic, t-test and ANOVA. RESULTS: Results showed that 86.7% of hospitals were in good preparedness level, with the average 85.9 ± 15.5. The maximum and minimum level of preparedness was related to mitigation of construction hazards (56.6 ± 35.6) and support of vital services (97.2 ± 6.0) dimensions, respectively. According to the results, there was a significant statistical difference between mean preparedness and safety of equipment and hazardous materials, hospital evacuation and field treatment, hospital environmental health proceedings, hospital curriculum programs and support of services dimensions with management experience (P<0.05). CONCLUSION: Although results corroborate that preparedness prerequisites against earthquake are in good level but attention to the weaknesses mitigation of construction hazards dimension and strengthening these prerequisites, which have obvious impacts on the structural vulnerability of hospitals and adjacent buildings in earthquakes, have been proposed.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".