Impact of Acute Stress on Resident Performance During Simulated Resuscitation Episodes: A Prospective Randomized Cross-Over Study
Bibliographic record
Abstract
BACKGROUND: Medical trainees have identified stress as an important contributor to their medical errors in acute care environments. PURPOSES: The objective of this study was to determine if the addition of acute stressors to simulated resuscitation scenarios would impact on residents' simulated clinical performance. METHODS: Fifty-four residents completed a control and a high-stress simulated scenario on separate visits. Stress measures were collected before and after scenarios. Two assessors independently evaluated residents' videotaped performance. RESULTS: Both control and high-stress scenarios triggered significant stress responses among participants; however, stress responses were not significantly different between control and high-stress conditions. No difference in performance was found between control and high-stress conditions (F value = 2.84, p = .098). CONCLUSIONS: Residents exposed to simulated resuscitation scenarios experienced significant stress responses irrespective of the presence of acute stressors during these scenarios. This anticipatory stressful response could impact on resident learning and performance and should be further explored.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".