Comprehensive Disaster Risk Management Standards for Hospitals
Bibliographic record
Abstract
Background: Hospitals play an important role in protecting the health and survival of people during disasters. Despite the development of risk management programs worldwide in recent years, hospital preparedness in disasters is low and one reason for that is the lack of hospital standards for disaster preparedness. This study aims to develop hospital accreditation standards for hospital disaster risk management based on national and international experiences. Materials and Methods: We used a mixed-method explanatory sequential approach. At first, a comparative study was conducted and the disaster risk management (DRM) hospital standards were extracted from 10 selected countries, namely the United States, Canada, Australia, Malaysia, India, Thailand, Egypt, Turkey, Saudi Arabia, and Denmark. Standards were analyzed according to the DRM life cycle and the most comprehensive framework was chosen. For national experiences, purposeful semi-structured interviews were conducted with 22 experts in disastrous events in the country and continued until the saturation stage. In addition, Graneheim and Landman’s contractual content analysis method was used for data analysis. After combining international standards and national experiences, the proposed standards were introduced and the content validity index and content validity ratio were done by 25 experts. Results: Differences were observed in the quality and quantity of the selected countries’ DRM standards. The national accreditation standards of the United States, Australia, and Canada had comprehensive standards and covered all aspects of the disaster risk management cycle. A total of 27 standards from the International Standards Review and 31 standards from interviews were added (a total of 58 standards). The content validity results of the standards were within acceptable limits. After editing and determining the measurement criteria, the final standards were introduced. Conclusion: This study introduces comprehensive DRM standards based on international and national documents and experiences that can be useful for policymakers and accreditation organizations in both developed and developing countries for hospital evaluation. This is also useful for hospitals as a roadmap for promoting preparedness in disasters.
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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 | 0.055 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".