Environmental Features of in Situ and Laboratory Simulations and Their Potential Impact on the Development of Teamwork Skills for Novice Trauma Professionals
Bibliographic record
Abstract
Background The effect of environmental fidelity on the development of teamwork skills in health care simulations is unclear. This preliminary descriptive study explored how in situ and laboratory environments impact the development of teamwork skills among novice trauma professionals. Method Four teams of six novice trauma professionals participated in two in situ or two laboratory simulations. Environmental features such as noise, interruptions, and the presence of nonparticipating individuals were assessed. Teamwork skills were evaluated from video recordings with the Team Emergency Assessment Measure (TEAM). Results In situ simulations involved higher levels of noise, more interruptions, and the presence of nonparticipating individuals compared with laboratory simulations. Teamwork skills in the in situ setting were rated as poor, with no improvement between simulations. In contrast, in the laboratory setting, teamwork skills were rated as good, with a 10-point improvement. Conclusion Preliminary results suggest that the in situ environment may hinder the development of teamwork skills. Further research with larger samples is needed to explore these effects and guide educators in choosing optimal training environments for novice trauma health care professionals. [ J Contin Educ Nurs. 2025;56(1):34–40.]
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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 | 0.002 | 0.019 |
| 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 | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".