Examining the effects of stress on the driving abilities of paramedic students
Bibliographic record
Abstract
Background: Research has suggested that stress may have a negative effect on paramedics' clinical performance. Stress has also been demonstrated to negatively affect the driving abilities of the general population, increasing the number of driving errors. However, no studies have explored stress and its potential impact on the non-clinical performance of paramedics, particularly their driving abilities. Methods: Paramedic students underwent emergency driving assessment in a driving simulator before and after exposure to a stressful medical scenario. The number and type of errors were documented before and after through the use of both driving simulator software and observation by two members of the research team. The NASA Task Load Index (TLX) was used to record self-reported stress levels. Results: Thirty-six students participated in the study. Following exposure to a stressful medical scenario, paramedic students had no rise in overall error rate, but demonstrated increases in three critical driving errors: namely, failure to wear a seat belt (three baseline versus 10 after stress); failing to stop for red lights or stop signs (seven versus 35); and losing control of the vehicle (two versus 11). Self-reported stress levels also increased after the clinical scenario, particularly in the area of mental (cognitive) demand. Conclusion: Paramedics are routinely exposed to acute stress in their everyday work, and this stress could affect their non-clinical performance. The critical errors committed by participants in the present study closely matched those considered to be contributory factors in many ambulance collisions. These results illustrate the need for further research into the effects of stress on non-clinical performance in general, and highlight the potential need to consider additional driver training and stress management education to mitigate the frequency and severity of driving errors among paramedics.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".