An adaptive simulation intervention decreases emergency physician physiologic stress while caring for patients during COVID-19: A randomized clinical trial
Bibliographic record
Abstract
BACKGROUND: Stressful work environments and burnout in emergency medicine (EM) physicians adversely impact patient care quality. The future EM workforce will need to prioritize clinician well-being to ensure optimal patient care. METHODS: This prospective, randomized, controlled study aimed to determine whether an adaptive simulation intervention, COVID-19 Responsive Intervention: Systems Improvement Simulations (CRI:SIS), decreased physiologic stress as measured by heart rate variability (HRV) in front-line EM physicians during the COVID-19 pandemic. HRV was measured with smart shirts and self-reported State-Trait Anxiety Inventory (STAI) were collected at baseline and during four 8-hour clinical shifts for all participants. The intervention group (n = 40) received a 3-hour virtual educational simulation intervention consisting of four simulation scenarios (CRI:SIS). The control group (n = 41) received no simulation intervention. RESULTS: There were no significant differences in demographics between groups. HRV data collected from 81 physicians across a total of 324 clinical shifts showed an increase in HRV (decrease in physiologic stress) in shifts immediately following CRI:SIS in the intervention group as measured by a root mean square standard deviation (RMSSD) difference of 11.55 ms (95% CI, -19.90 to -3.20; P = 0.007) compared to the control group. Post-intervention STAI did not significantly differ between intervention and control. CONCLUSION: An adaptive simulation-based educational intervention led to decreased physiologic stress (increased HRV) among emergency physicians who received a simulation education intervention. Reduced physiologic stress generated by adaptive simulation interventions may improve both patient safety and clinician well-being.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".