It Takes a Village to Move a Hospital: Simulation Improves Intensive Care Team Preparedness for a Move to a New Site
Bibliographic record
Abstract
OBJECTIVES: To evaluate in-situ simulation to prepare a PICU to move to a new, redesigned unit. METHODS: The study setting is an academic PICU. This is a cross-sectional study using in-situ simulations of common PICU admissions. Postsimulation, participants completed a survey comparing the perception of preparedness pre- and postsimulation (via a 10-point Likert scale). Participants were resurveyed 6 months postmove to assess whether effects persisted. Qualitative data were obtained via thematic review of the survey comment section and from postsimulation debriefing. RESULTS: Response rates were initially 100% and 67% at the 6-month follow-up. In the initial phase, all questions had statistically significant improvements in post- versus presimulation scores. Participants felt better prepared (presimulation: 6.20, postsimulation: 7.90, P < .001) and more confident about caring for real patients (presimulation: 5.49, postsimulation: 7.41, P < .001). They felt more comfortable working in the new unit (presimulation: 5.65, postsimulation: 7.50, P < .001) and better able to deliver safe care (presimulation: 5.85, postsimulation: 7.60, P < .001). Six months postmove, participants still believed that simulation was helpful (7.43, SD: 2.20) and still reported improved team confidence (7.36, SD: 2.11). Only 1 of 28 participants preferred less simulation. Exercises were described as helpful in identifying process and latent patient safety issues. CONCLUSIONS: Our pediatric intensive care team found simulations to be beneficial in preparation for providing care to critically ill children in a complex new setting. Simulations uncovered latent process, personnel, and patient-safety issues that were addressed before actual patient care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".