Impact of Unexpected Death in a Simulation Scenario on Skill Retention, Stress, and Emotions: A Simulation-Based Randomized Controlled Trial
Bibliographic record
Abstract
Introduction The simulation of patient death remains controversial in simulation-based education. We investigated the effect of simulated patient death on learners' skill retention, stress levels, and emotions. Methods After ethics approval, we recruited residents at two Canadian universities. Participants were randomized to manage a simulated cardiac arrest ending with either the unexpected death (intervention group) or survival (control group) of the simulated patient (i.e., manikin). Three months later, all participants performed the same scenario but with the opposite outcome. Blinded video raters assessed participants' non-technical and technical crisis resource management (CRM) skills at both time points. Stress levels (represented by anxiety level, salivary cortisol concentration, and cognitive appraisal) and emotional valence were measured. Outcomes were analyzed using analysis of covariance (ANCOVA) or generalized estimating equations as appropriate. Results The analysis included 46 participants (intervention: n=24; control: n=22). Simulated death neither affected retention of non-technical CRM skills (mean retention Ottawa Global Rating Scale score in the death group [29.4, 95% CI: 27.0, 31.8] versus control group [29.4, 95% CI: 26.8, 32.0; p=0.87]) nor technical CRM skills (mean retention task-specific checklist score in the manikin death group [11.8, 95% CI: 10.5, 13.0] versus the control group [12.5, 95% CI: 11.3, 13.7; p=0.69]). The simulated death had negative effects on participants' anxiety levels, cognitive appraisal, and emotions. Conclusion Simulated patient death did not affect the retention of non-technical or technical CRM skills but led to greater levels of short-term anxiety, stress, and negative emotions among learners.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".