Threat and challenge: cognitive appraisal and stress responses in simulated trauma resuscitations
Bibliographic record
Abstract
OBJECTIVES Training and practice in medicine are inherently stressful. Research into the effects of acute stressors has revealed significant variability in individual responses to stressors, with performance impairments occurring in those who demonstrate elevated subjective and physiological responses. Cognitive appraisals (subjective assessment of situational demands and available resources) of a stressor have been proposed as a predictor variable in stress responses. However, the relationship between cognitive appraisal and stress responses has not been tested empirically in complex realistic situations. The purpose of this study was to determine the extent to which cognitive appraisal affects a medical trainee's subjective and physiological stress responses to high-acuity simulated clinical situations. METHODS Thirteen emergency medicine and general surgery residents participated in high (HS) and low (LS) stress trauma resuscitation simulations. Subjective (cognitive appraisal and State-Trait Anxiety Inventory [STAI]) and physiological (salivary cortisol) measures were collected at baseline and in response to participation in each scenario. RESULTS Post-scenario STAI scores, cognitive appraisal and cortisol levels were higher in the HS scenario compared with the LS scenario. For the participants who appraised the scenarios as 'threats' (in which the demands outweighed the resources), the ratio of perceived demands to resources was positively correlated with cortisol levels (r = 0.59, p < 0.05) and STAI responses (r = 0.64, p < 0.05). By contrast, for the participants who appraised the scenarios as 'challenges' (in which resources were sufficient to meet the demands), the perceived ratio of demands to resources was not correlated with either the STAI scores or cortisol levels. CONCLUSIONS Subjective appraisals of a situation appear to play an important role in stress responses, which have previously been shown to impair performance. As such, training for high-acuity events should include interventions targeting stress management skills.
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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 | 0.006 |
| 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 | 0.001 | 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".