Impact of stress on resident performance in simulated trauma scenarios
Bibliographic record
Abstract
BACKGROUND: Training and practice in medicine are inherently stressful. The effects of stress on performance in clinical situations are poorly understood. The purpose of this study was to examine the stress responses and clinical performance of residents during low and high stress (HS) simulated trauma resuscitations. METHODS: Thirteen emergency medicine and general surgery residents were evaluated in HS and low stress (LS) trauma resuscitation simulations. Subjective and physiologic (heart rate, salivary cortisol) responses were measured at baseline and in response to the scenarios. Performance was assessed with global rating and checklist scores of technical performance, time to record critical information, and the Anesthesia Non-Technical Skills tool. Post-scenario recall was assessed with the completion of a standardized trauma history form. RESULTS: Post-scenario subjective stress and cortisol levels were higher in the HS scenario compared with the LS scenario (p < 0.05). Checklist performance scores and post-scenario recall were significantly lower in the HS compared with the LS condition (p < 0.05). CONCLUSION: In trainees, some aspects of performance and immediate recall appear to be impaired in complex clinical scenarios in which they exhibit elevated subjective and physiologic stress responses. The findings of this study highlight a potential threat to patient safety and demand further investigation. Future studies should strive to further elucidate the effects of stress on specific components of performance and investigate ways to reduce its negative impact.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".