Evaluating nurses’ preparedness in critical incidents
Bibliographic record
Abstract
Objective: This study aims to evaluate the preparedness and training of Canadian nurses in critical incidents.Methods: Design: An observational cross-sectional survey through a self-administered web-based questionnaire. Setting: The questionnaire was shared with nurses working in emergency departments, intensive care units, and coronary care units at five hospitals affiliated with McGill University in Montreal (Quebec, Canada). Participants: In total, 145 nurses completed the questionnaire. It was sent through email to nurse managers and assistant nurse managers working in the emergency department, adult intensive care unit, and cardiac care unit at four academic hospitals. Main Outcome Measured: level of preparedness and skills of nurses to deal with critical incidents.Results: Most nurses have not participated in a disaster management (code orange) simulation (64.8%, n = 94). Moreover, around half of them knew their specific role in such a simulation (49.6%, n = 72). The vast majority of participants (78.6%, n = 114) never took part in a real code orange scenario. On multiple logistic regression, having > 10 years of experience in nursing, having > 10 years of experience in critical care, participating in a code orange simulation, knowledge of roles and responsibilities during a code orange situation, and having knowledge of the department's code orange plan, were significantly associated with a higher level of preparedness.Conclusions: This study shows a lack of nurses’ preparedness in dealing with critical incidents based on their self-assessment. Confidence and knowledge of skills associated with BLS and ACLS were noted to be essential for a high level of preparedness.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".