Paramedic Performance in Calculating Drug Dosages Following Stressful Scenarios in a Human Patient Simulator
Bibliographic record
Abstract
BACKGROUND: Paramedics face many stressors in their work environment. Studies have shown that stress can have a negative effect on the psychological well-being of health professionals. However, there is little published research regarding the effects of stress on the cognitive skills necessary for optimal patient care. OBJECTIVES: The primary purpose of this study was to investigate the effects of acute stress on the emotional response and performance of paramedics. Furthermore, the authors explored whether a paramedic's level of training or years of experience would mediate the effects of stress on performance. METHODS: Paramedic performances in calculating drug dosages were compared in two stress conditions. In the low-stress condition, 30 paramedics calculated the drug dosages in a quiet classroom free of any stressor. In the high-stress condition, the same paramedics calculated comparable drug dosages immediately after working through a challenging scenario with a human patient simulator. RESULTS: The paramedics obtained lower accuracy scores in the high-stress condition than in the low-stress condition [43% (95% confidence interval [CI]: 36.9-49.2) vs. 58% (95% CI: 48.6-67.1), p < 0.01 based on univariate analysis]. Neither work experience nor level of training predicted the individual differences in the stress-induced performance decrements. CONCLUSION: These results suggest that the types of stressors encountered in clinical situations can increase medical errors, even in highly experienced individuals. These findings underline the need for more research to determine the mechanisms by which stress influences clinical performance, with the ultimate goal of targeting education or technologic interventions to those tasks, situations, and individuals most likely to benefit from such interventions.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".