Large-Scale Disaster Simulations: Advancing Pediatric Disaster Preparedness and Safety through Whole-Hospital, Inter-Professional Learning
Bibliographic record
Abstract
Study/Objective: Test a model that was developed to compare PC screen-based vs high-fidelity simulation supported training for basic trauma skills in terms of learning and cost outcomes.Background: As disasters increase in numbers and intensity, more attention is being paid to trauma skills training for health workers.There is a wide spectrum of simulation types, and while high-fidelity simulation is known to be effective, it is also very costly.Methods: The Nursing Education Simulation Framework guided the development of a model to compare the two simulation methods in terms of confidence, knowledge, skills, and cost outcomes.Participants (N = 70) were nurses and EMT's from the civilian and military sectors.All underwent pre-testing, random assignment to PC screen-based or high-fidelity simulation training groups, trauma skills training, immediate post and then post-post (6-12-weeks) evaluation.The evaluator was blinded to the simulation training type for each participant.Results: There were no differences in the learning outcomes between the PC screen-based vs high-fidelity groups.Both groups increased their confidence, knowledge, and skills.However, the cost of high-fidelity simulation was ten times that of PC screen-based instruction per unit.Conclusion: For basic trauma nursing skills, a less costly method of instruction can achieve the same learning outcome results.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".