Survey of major trauma centre preparedness for mass casualty incidents in Australia, Canada, England and New Zealand
Bibliographic record
Abstract
BACKGROUND: Mass casualty incidents (MCIs) are increasing. Trauma centres play a key role in MCIs due to their readiness and expansive multidisciplinary expertise for injury management. Previous studies have shown deficiencies in trauma centre disaster preparedness. The aim of this study was to describe the current disaster preparedness of Major Trauma Centres (MTCs) in Australia, Canada, England and New Zealand. METHODS: = 82) MTCs was undertaken. The anonymous survey collected data about disaster preparedness in nine key areas. Respondents were encouraged to consult appropriately at their centre to provide an accurate representation of their centre's preparedness. FINDINGS: Responses were received from 69 (84%) centres; 61 completed all questions. 91% had a disaster preparedness committee and 80% had an all-hazards emergency plan. 79% had held an MCI drill in the past 2 years. 54% reported a system in place to calculate maximum capacity, but testing of surge capacity was uncommon. 55% reported the presence of stored resources for an MCI and 58% had a database of staff trained in Emergency Management. 74% had a training and education plan available for staff involved in an MCI and a plan for professional debriefing of staff post-MCI, while 62% had a post-disaster employee assistance programme. Most centres had appropriate back-up communication, safety and security plans. INTERPRETATION: The disaster preparedness of MTCs was high for communication, safety and security but there was clear need for improvement in other areas including surge capacity, human resources and post-disaster recovery.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".