Uncovering success stories: how to resuscitate in situ simulation initiatives in Canadian emergency departments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it