Is in situ simulation in emergency medicine safe? A scoping review
Bibliographic record
Abstract
OBJECTIVES: To provide an overview of the available evidence regarding the safety of in situ simulation (ISS) in the emergency department (ED). DESIGN: Scoping review. METHODS: Original articles published before March 2021 were included if they investigated the use of ISS in the field of emergency medicine. INFORMATION SOURCES: MEDLINE, EMBASE, Cochrane and Web of Science. RESULTS: A total of 4077 records were identified by our search strategy and 2476 abstracts were screened. One hundred and thirty full articles were reviewed and 81 full articles were included. Only 33 studies (40%) assessed safety-related issues, among which 11 chose a safety-related primary outcome. Latent safety threats (LSTs) assessment was conducted in 24 studies (30%) and the cancellation rate was described in 9 studies (11%). The possible negative impact of ISS on real ED patients was assessed in two studies (2.5%), through a questionnaire and not through patient outcomes. CONCLUSION: Most studies use ISS for systems-based or education-based applications. Patient safety during ISS is often evaluated in the context of identifying or mitigating LSTs and rarely on the potential impact and risks to patients simultaneously receiving care in the ED. Our scoping review identified knowledge gaps related to the safe conduct of ISS in the ED, which may warrant further investigation.
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 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 | 0.015 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".