Effectiveness of Chemical, Biological, Radiological, Nuclear, Explosive (CBRNE) Event-Response Training in a Hospital Setting: A Scoping Review
Bibliographic record
Abstract
OBJECTIVE: The risk of Chemical, Biological, Radiological, Nuclear, and Explosive (CBRNE) incidents is increasing due to terrorism, technological advancements, conflicts, and emerging diseases. Hospitals, as critical response centers, face unique challenges during such events. Comprehensive training is crucial to ensure effective response and protect both patients and staff. This scoping review assesses the effectiveness of CBRNE training in enhancing knowledge, competencies, and preparedness among hospital-based health care providers. METHODS: Comprehensive searches were conducted in Ovid MEDLINE, Ovid Embase, Scopus, Web of Science Core Collection, and CINAHL using targeted keywords. Papers were screened using Covidence. Data were analyzed to evaluate the effectiveness of various training methods used in hospital settings. RESULTS: A total of 23 papers were included in this review. Training effectiveness was reported in 91% of the reviewed articles. Nurses were the predominant group participating in hospital-based training programs. Tabletop exercises were the most commonly used training method, and biological hazards were the most frequent scenario type. No study identified a single superior method for improving training effectiveness. CONCLUSIONS: CBRNE training incorporating diverse modalities improves health care providers' knowledge and competencies. Enhanced preparedness supports better responses to disasters, potentially leading to improved patient outcomes and public safety.
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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 | 0.013 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".