Hospital Workers Disaster Management and Hospital Nonstructural: A Study in Bandar Abbas, Iran
Bibliographic record
Abstract
INTRODUCTION: A devastating earthquake is inevitable in the long term and likely in the near future in Iran. The objective of the study was to assess the knowledge of hospital staff to disaster management system in hospital and to determine nonstructural safety assessment in Shahid Mohammadi hospital in Bandar Abbas city of Iran. This hospital is the main referral hospital in Hormozgan province with a capacity of about 450 beds and the highest patient admissions. METHODS: The cross-sectional study was conducted in 2013 on 200 healthcare workers at Shahid Mohammadi hospital, in the city of Bandar Abbas, Iran. This hospital is the main referral hospital in Hormozgan province and has a capacity of about 450 beds with highest numbers of patient admissions. Questionnaire and checklist used for assessing health workers knowledge and awareness towards disaster management and nonstructural safety this hospital. RESULTS: This study found that knowledge, awareness, and disaster preparedness of hospital staff need continual reinforcement to improve self efficacy for disaster management. Equipping health care facilities at the time of natural disasters, especially earthquakes are of great importance all over the world, especially in Iran. This requires the national strategies and planning for all health facilities. CONCLUSION: It seems due to limitations of hospital beds, insufficient of personnel, and medical equipment, health care providers paid greater attention to this issue. Since this hospital is the only educational public hospital in the province, it is essential to pay much attention to the risk management not only to this hospital but at the national level to health facilities.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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".