Impact of crisis resource management simulation-based training for interprofessional and interdisciplinary teams: A systematic review
Bibliographic record
Abstract
Crisis resource management (CRM) abilities are important for different healthcare providers to effectively manage critical clinical events. This study aims to review the effectiveness of simulation-based CRM training for interprofessional and interdisciplinary teams compared to other instructional methods (e.g., didactics). Interprofessional teams are composed of several professions (e.g., nurse, physician, midwife) while interdisciplinary teams are composed of several disciplines from the same profession (e.g., cardiologist, anaesthesiologist, orthopaedist). Medline, EMBASE, CINAHL, Cochrane Central Register of Controlled Trials, and ERIC were searched using terms related to CRM, crisis management, crew resource management, teamwork, and simulation. Trials comparing simulation-based CRM team training versus any other methods of education were included. The educational interventions involved interprofessional or interdisciplinary healthcare teams. The initial search identified 7456 publications; 12 studies were included. Simulation-based CRM team training was associated with significant improvements in CRM skill acquisition in all but two studies when compared to didactic case-based CRM training or simulation without CRM training. Of the 12 included studies, one showed significant improvements in team behaviours in the workplace, while two studies demonstrated sustained reductions in adverse patient outcomes after a single simulation-based CRM team intervention. In conclusion, CRM simulation-based training for interprofessional and interdisciplinary teams show promise in teaching CRM in the simulator when compared to didactic case-based CRM education or simulation without CRM teaching. More research, however, is required to demonstrate transfer of learning to workplaces and potential impact on patient outcomes.
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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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".