Results of a mass casualty incident simulation in an undergraduate nursing program
Bibliographic record
Abstract
Background : Nurses in collaboration with fire rescuers, emergency medical technicians (EMTs), and doctors are often called to be first responders to world-wide disasters ranging from terrorist attacks to catastrophic weather events. The American Association of Colleges of Nursing has established the need for disaster-preparedness education in baccalaureate nursing programs. Limited research has been conducted about the impact of utilizing simulation as an educational tool to prepare nursing students for disaster response. This paper presents the results of a simulation of a mass casualty incident utilizing low-fidelity and static manikins, as well as actors to play the role of victims, family members and news personnel. Methods : One hundred and seven students from traditional and accelerated second-degree programs participated in a simulation in the roles of victims as well as providers. A quasi-experimental pre- and post-test design was used to assess students ’ self-perceptions. Results : Statistically significant improvement in self-perceived knowledge, attitudes and skills was seen. Students who participated as victims or providers reported similar improvements. Conclusions : Well-designed and concise mass casualty incident simulation is a valuable educational tool that can be easily incorporated into nursing curricula, with students undertaking the role of either a victim or a provider.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".