Development of hospital disaster risk management accreditation standards
Bibliographic record
Abstract
Background: The preparedness and safety of hospitals in disasters are essential to maintain the health and survival of the community. Numerous studies have shown that the level of preparedness of Iranian hospitals is moderate and low. Lack of comprehensive hospital standards for disaster preparedness is one of the reasons. This study aimed to develop hospital accreditation standards for hospital disaster risk management. Methods: This comparative study was conducted between April and September 2016. Hospital disaster risk management accreditation standards were extracted from the hospital accreditation standards of 11 countries including the United States, Canada, Australia, Malaysia, India, Thailand, Egypt, Turkey, Saudi Arabia, Denmark and Iran. Overall, 27 hospital disaster risk management accreditation standards were introduced. The opinions of 22 disaster risk management experts were used to assess the content validity of the proposed disaster risk management accreditation standards. Results: Differences were observed in the quality and quantity of those countries’ disaster risk management 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. Finally, 27 standards were proposed for developing Iranian hospitals’ disaster risk management accreditation standards. The CVI & CVR validity of the proposed standards were acceptable. There were significant differences in the quantity and quality of hospital disaster risk management accreditation standards in selected countries. The most comprehensive standards belonged to the US National Standards (12 standards and 113 sub-standards), followed by the Australian and Canadian accreditation standards. The accreditation standards of the developing countries and Iran were not comprehensive and did not meet the international goals of disaster risk management. The proposed hospital disaster risk management accreditation standards had high content validity. Conclusion: Disaster risk management accreditation standards in Iran and developing countries need to be revised and upgraded. Comprehensive standards based on international experiences and expert opinions were introduced in this study that can be used to develop hospital accreditation standards in Iran and other countries.
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 | 0.065 | 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 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".