Disaster and Emergency Management: Canadian Nurses' Perceptions of Preparedness on Hospital Front Lines
Bibliographic record
Abstract
INTRODUCTION: Three years following the global outbreak of severe acute respiratory syndrome (SARS), a national, Web-based survey of Canadian nurses was conducted to assess perceptions of preparedness for disasters and access to support mechanisms, particularly for nurses in emergency and critical care units. HYPOTHESES: The following hypotheses were tested: (1) nurses' sense of preparedness for infectious disease outbreaks and naturally occurring disasters will be higher than for chemical, biological, radiological, and nuclear (CBRN)-type disasters associated with terrorist attacks; (2) perceptions of preparedness will vary according to previous outbreak experience; and (3) perceptions of personal preparedness will be related to perceived institutional preparedness. METHODS: Nurses from emergency departments and intensive care units across Canada were recruited via flyer mailouts and e-mail notices to complete a 30-minute online survey. RESULTS: A total of 1,543 nurses completed the survey (90% female; 10% male). The results indicate that nurses feel unprepared to respond to large-scale disasters/attacks. The sense of preparedness varied according to the outbreak/disaster scenario with nurses feeling least prepared to respond to a CBRN event. A variety of socio-demographic factors, notably gender, previous outbreak experience (particularly with SARS), full-time vs. part-time job status, and region of employment also were related to perceptions of risk. Approximately 40% of respondents were unaware if their hospital had an emergency plan for a large-scale outbreak. Nurses reported inadequate access to resources to support disaster response capacity and expressed a low degree of confidence in the preparedness of Canadian healthcare institutions for future outbreaks. CONCLUSIONS: Canadian nurses have indicated that considerably more training and information are needed to enhance preparedness for frontline healthcare workers as important members of the response community.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".