Systematic review on the current state of disaster preparation Simulation Exercises (SimEx)
Bibliographic record
Abstract
INTRODUCTION: The simulation exercise (SimEx) simulates an emergency in which an elaboration or description of the response is applied. The purpose of these exercises is to validate and improve plans, procedures, and systems for responding to all hazards. The purpose of this study was to review disaster preparation exercises conducted by various national, non-government, and academic institutions. METHODOLOGY: Several databases, including PubMed (Medline), Cumulative Index to Nursing and Allied Health Literature (CINAHL), BioMed Central, and Google Scholar, were used to review the literature. Information was retrieved using Medical Subject Headings (MeSH) and documents were selected according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). To assess the quality of the selected articles, the Newcastle-Ottawa Scale (NOS) technique was utilized. RESULTS: A total of 29 papers were selected for final review based on PRISMA guidelines and the NOS quality assessment. Studies have shown that many forms of SimEx commonly used in disaster management including tabletop exercises, functional exercises, and full-scale exercises have their benefits and limitations. There is no doubt that SimEx is an excellent tool for improving disaster planning and response. It is still necessary to give SimEx programs a more rigorous evaluation and to standardize the processes more thoroughly. CONCLUSIONS: Drills and training can be improved for disaster management, which will enable medical professionals to face the challenges of disaster management in the 21st century.
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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 | 0.020 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".