Evaluating Nurses’ Preparedness in Managing Critical Incidences and Disaster Relief : A Survey in Quebec
Bibliographic record
Abstract
BackgroundAlthough nurses play a major role in alleviating the burdens associated with critical incidents, little is known about nursing preparedness in these emergent situations. This study aims to evaluate the degree of preparedness and training of Canadian nurses in disasters and critical incidents. MethodsAn observational cross-sectional survey through self-administered web-based questionnaire was shared with nurses working in Emergency Departments, Adult/pediatric Intensive Care Units, and Cardiac Care Units at five hospitals affiliated with McGill University in Montreal (Quebec, Canada). General demographics, level of experience, critical-care training, and level of confidence in performing trauma-related activities were collected. The statistical significance level was set at p=0.05. ResultsIn total, 145 nurses completed the survey. Most nurses have not participated in a disaster management simulation (64.8%, n= 94). Moreover, almost only half of them knew what was their specific role in such a simulation (49.6% , n=72) and where to find their department’s code orange (external disaster) plan ( 44.8% , n=65). The vast majority of participants (78.6%, n=114) never participated in a real code orange scenario. On multiple logistic regression, having over 10 years of experience in critical care setting (OR 5.37, p <0.05) and having completed two (OR 3.75, p= 0.03) or three or more (OR 4.60, p = 0.03) courses in trauma/ critical care were significantly associated with a higher level of preparedness.ConclusionNurses are essential in optimal trauma care provision. This study shows a lack of nurses’ preparedness to deal with critical situations based on their self-assessment. The completion of a trauma course was noted to be essential for high level of preparedness
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 | 0.002 | 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".