Nurses’ Perception of Readiness for Mass Casualty Events Involving Children
Bibliographic record
Abstract
Background: During mass casualty events, hospitals must be ready to receive and provide patient care for both children and adults. However, many studies have shown that due to a lack of funding, resources, training, and time, nurses consistently report feeling unprepared to care for children during mass casualty events. Methods: To improve understanding of how prepared pediatric-trained nurses are to respond to mass casualty events involving children, Registered Nurses (RN) completed a survey with questions that included four domains: professional demographics and employment history, experience working as an RN in a mass casualty event, knowledge questions related to current organizational mass casualty procedures, and perceptions on professional preparedness.Results: Seventy-four percent of participants agree that a mass casualty event primarily involving children, requiring what is known as a Code Orange activation, will occur at some point during their career. Nurse participants do not currently receive regular training related to a Code Orange activation, and are overall dissatisfied with the little training provided. Nurses believe emergency preparedness is important to their professional development.Discussion: Increasing nurses’ preparedness to respond to a mass casualty event involving children is important and may require additional training across nurses’ career trajectory.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".