Evaluation of the feasibility and impacts of in situ simulation in emergency medicine—a mixed-method study protocol
Bibliographic record
Abstract
INTRODUCTION: In situ simulation (ISS) consists of performing a simulation in the everyday working environment with the usual team members. The feasibility of ISS in emergency medicine is an important research question, because ISS offers the possibility for repetitive, regular simulation training consistent with specific local needs. However, ISS also raises the issue of safety, since it might negatively impact the care of other patients in the emergency department (ED). Our hypothesis is that ISS in an academic high-volume ED is feasible, safe and associated with benefits for both staff and patients. METHODS: A mixed-method, including a qualitative method for the assessment of feasibility and acceptability and a quantitative method for the assessment of patients' safety and participants' psychosocial risks, will be used in this study.Two distinct phases are planned in the ED of the CHU de Québec-Université Laval (Hôpital de l'Enfant-Jésus) between March 2021 and October 2021. Phase 1: an ISS programme will be implemented with selected ED professionals to assess its acceptability and safety and prove the validity of our educational concept. The number of cancelled sessions and the reasons for cancellation will be collected to establish feasibility criteria. Semistructured interviews will evaluate the acceptability of the intervention. We will compare unannounced and announced ISS. Phase 2: the impact of the ISS programme will be measured with validated questionnaires for the assessment of psychosocial risks, self-confidence and perceived stress among nonselected ED professionals, with comparison between those exposed to ISS and those that were not. ETHICS AND DISSEMINATION: The CHU de Québec-Université Laval Research ethics board has approved this protocol (#2020-5000). Results will be presented to key professionals from our institution to improve patient safety. We also aim to publish our results in peer-reviewed journals and will submit abstracts to international conferences to disseminate our findings.
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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 | 0.084 | 0.040 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.005 |
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".