The Effects of Acute Stress on Performance: Implications for Health Professions Education
Bibliographic record
Abstract
PURPOSE: This paper is a review of representative research on the impact of acute stressors on the clinical performance of individuals and teams. METHOD: The Sciences Citation Index, Medline, and Psychinfo were used to search for articles up to and including 2008. The search terms were stress/tension/arousal/anxiety/cortisol/threat, cognition/skills/memory/attention/problem solving/decision making/performance, stress reduction/stress exposure/stress management/stress inoculation, and health professionals/medicine/medical students/residents/physicians/teams. The search was limited to papers in English from all developed countries. Secondary references were selected from primary papers. RESULTS: Elevated stress levels can impede performance on tasks that require divided attention, working memory, retrieval of information from memory, and decision making. These effects appear to be determined by the individual's appraisal of the demands and resources of a situation, the relationship between the stressor and the task, and factors such as coping styles, locus of control, and social supports. CONCLUSIONS: Given the potential negative impact of stress on performance, and the individualistic way in which people respond, medical educators might want to consider avenues for training learners in stress management. More research is needed to fully understand the contributions of personal factors such as coping style and locus of control, as well as the relationship of perceptions of stress to issues such as fatigue.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".