Uncovering success stories: how to resuscitate in situ simulation initiatives in Canadian emergency departments
Bibliographic record
Abstract
In situ simulation (ISS) has long been recognized as a powerful tool for identifying latent safety threats, enhancing teamwork, and ultimately improving patient safety in Emergency Departments (EDs). However, the challenges of operationalizing ISS training in the current clinical environment in Canadian EDs have become increasingly evident. While many EDs face hurdles in implementing ISS, some teams have proven resilient and successful in their ISS endeavors. This study aims to determine which factors are associated with the successful maintenance of ISS programs within Canadian EDs. Using a positive deviance approach, we conducted a qualitative study of ED teams engaged in ISS projects, using interviews as a data collection tool. We recruited 14 healthcare providers who had participated in successful ISS initiatives in Canadian EDs. Participants highlighted the importance of engaging interprofessional stakeholders, flexibility from the simulation team, and buy-in from participants and colleagues as key factors contributing to the success of ISS programs. Challenges identified included lack of buy-in, space constraints, high patient volume and acuity, and staff shortages. Strategies for managing these challenges included scheduling simulations during less busy times and having alternative spaces for simulations. ISS was found to have a significant impact on patient safety, improving teamwork, crisis resource management, and overall patient care. These findings provide valuable insights for EDs looking to start or improve their ISS programs, emphasizing the importance of collaboration and adaptability in overcoming challenges to ensure the success of ISS initiatives.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.013 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| 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".