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Record W4405811704 · doi:10.3928/00220124-20241213-07

Environmental Features of in Situ and Laboratory Simulations and Their Potential Impact on the Development of Teamwork Skills for Novice Trauma Professionals

2024· article· en· W4405811704 on OpenAlexaff
Alexandra Lapierre, Patrick Lavoie, Caroline Arbour

Bibliographic record

VenueThe Journal of Continuing Education in Nursing · 2024
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsUniversité du Québec
Fundersnot available
KeywordsTeamworkPsychologyMedical educationTrauma careHealth careApplied psychologyMedicineMedical emergency

Abstract

fetched live from OpenAlex

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.]

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.811
Threshold uncertainty score0.194

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.363
Teacher spread0.355 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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