Safety and security of hospitals during natural disasters: Challenges of disaster managers
Bibliographic record
Abstract
The purpose of this research is to explore some challenges that hospital disaster managers face in dealing with natural disaster events.In such events, it is crucial that hospitals remain safe and functional during and after the disaster; thus, hospitals at all levels need to plan for natural disasters and be aware of the requirement to remain in an operational condition both during and after any event.Evidence from around the world suggests that the malfunctioning of hospitals during a disaster have extensive impacts on both inbound and outbound patients; as such, disaster preparedness is a significant concern for hospital disaster managers.Hospital disaster management is important because of the critical services that healthcare facilities provide for injured people and existing patients.Therefore, developing a good management system for natural disaster events can help to ensure better efficiency and economy in the use of facilities and human resources within hospitals.Although appropriate disaster management can mitigate the impact of natural disasters in hospitals, there are some barriers that can prevent the effective management of these facilities in such events.For this study, secondary information was retrieved from the Internet and via academic database on sudden-onset natural disasters, and it was found that the: awareness, knowledge, disaster preparedness of hospital staff; allocation of building codes, and the relocation of buildings to higher levels, need be improved.Also, equipping health care facilities at the time of natural disaster events is important.To manage the challenges facing hospital disaster managers, a national strategy for the disaster management planning for hospitals is required.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".