Implications of long-term low-fidelity in situ simulation in acute care and association with a reduction in unexpected cardiac arrests: A retrospective research study
Bibliographic record
Abstract
In situ simulation is a new tool for building teamwork during crisis. However, only a few studies have discussed the long-term effects of regular in situ simulations. To better understand these effects, this study retrospectively analyzed the effect of regular (twice a month over a four-year period) in situ simulations in the National Cheng Kung University Hospital acute care ward, which provides care for patients with acute illnesses and requires admission during an emergency room visit. The simulations were held in a real clinical environment using a low-fidelity mannequin and the trainees involved in the simulations were the medical staff of the acute care ward. In this study, we review the effects of such long-term simulations with respect to team performance based on the Ottawa global rating scale (GRS) and incidences of urgent intubation and unexpected cardiac arrest. Our results revealed that among the 84 simulations that were conducted during the study period, 42 could be categorized as "high performance" and the remaining 42 as "low performance" based on the team's Ottawa GRS. Further, the seniority of nurse leaders and exposure of nurses to repeated simulations did not have any effect on performance. However, although regular simulations did not have any effect on the number of urgent intubations, they caused a marked decrease in the number of unexpected cardiac arrests. The current study did not show that repeated, low-fidelity, regular in situ simulations improve team performance in simulations based on Ottawa GRS, but it was associated with a reduction in the unexpected cardiac arrest rate in the acute care ward. Our results support the use of in situ simulations in acute care wards as an educational tool for first-line caregivers.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".