Driving me crazy: the effects of stress on the driving abilities of paramedic students
Bibliographic record
Abstract
Background Previous research has demonstrated that stress has a negative impact on the performance of paramedics while performing medical related tasks. Acute stress has also been shown to negatively impact the driving abilities of the general population increasing the number of critical driving errors performed. No literature was discovered that discussed the effects of stress on the driving abilities of paramedics. Methods Paramedic students underwent a driving ability assessment in a driving simulator. We then exposed them to a stress inducing medical scenario. Another driving assessment was then conducted. The numbers, and types of errors were documented before and after the scenario. Results 36 students participated in the study. Paramedic students demonstrated no increase in overall error rate after a stressful scenario, but demonstrated an increase in three critical driving errors; failure to wear a seatbelt (3 baseline v 10 post stress, p= 0.0087), failing to stop for red lights or stop signs (7 v 35, p= <0.0001), and losing controlling of the vehicle (2 v 11, p= 0.0052). Conclusion Paramedic students demonstrated an increase in critical driving errors after a stressful simulated clinical scenario. Paramedics are routinely exposed to acute stress during the course of their working day. This stress could increase the number of critical driving errors that occur. These results reinforce the need for further research, and highlight the potential need for increased driver training and stress management education in order to mitigate the frequency and severity of driving errors made by 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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".