The current status of disaster preparedness in Canadian trauma centers
Bibliographic record
Abstract
Mass casualty incidents (MCIs) put substantial stress on loco-regional resources, and trauma centers are critical to responding to these events. Our previous evaluation of Canadian centers helped to identify several weaknesses in disaster responsiveness. In this analysis, we determined the current state of MCI readiness across Canada and how this has changed over time. A multinational cross-sectional survey-based study on MCI preparedness was performed, including 24 Canadian trauma centers. Surveys were completed anonymously online by representatives of each facility. Responses from Canadian centers were examined and compared to previous findings to assess temporal changes in institutional capacity. Fifteen (63%) trauma centers responded, 100% of which had a disaster committee. Sixty percent had a single all-hazards emergency plan, and 71% performed a practice drill in the last two years. Sixty-two percent had communications systems designed to function during an MCI. Ninety-two percent had a triage system in place, and 54% of centers could monitor surge capacity. Half (54%) reported back-up systems for survival essentials, but the capability for prolonged operation during a disaster was limited. A minority (15%) had a database denoting staff with emergency training, although half (54%) had disaster training programs. Comparison to past data showed an increased prevalence of committees dedicated to disaster preparedness and disaster drills but worsened external stakeholder representation and poor ability to provide a prolonged response to crises. Our results demonstrate that MCI preparedness is a growing focus of Canadian trauma centers, but that there are deficiencies that remain unaddressed. Future efforts should focus on these vulnerabilities to ensure the provision of a robust disaster response. LEVEL OF EVIDENCE: Level 3b (prevalence study, limited population).
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 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".