The impact of mental readiness on driving performance and traffic safety
Bibliographic record
Abstract
The construct 'Mental Readiness' has been used to explain variations in peak performance within different task environments. In particular, research, conducted in diverse work settings such as sports, military and medicine, integrated mental readiness dimensions to increase human performance in critical task episodes. Mental readiness is a complex construct which encompasses sub-dimensions such as attentional control, goal-setting, relaxation, activation, self-confidence, self-talk and imagery. So far, mental readiness has not been used to predict traffic safety and driving performance of young drivers in particular. However, young drivers are involved in a huge amount of traffic accidents and therefore represent a major threat to traffic safety. One explanation is that the insufficient driving performance of young drivers is due to a lack of mental readiness when they enter the street. In this paper, we present the development of a mental readiness measure for student drivers. Hence, 167 student drivers were surveyed regarding mental readiness dimensions, driving performance and perceived stress before and after their final driving test. Data analysis revealed acceptable and even excellent internal consistency of these subscales. A validation study with four safety-related criterion measures (objective driving performance, subjective assessment of driving performance, perceived stress during test preparation, and perceived stress during driving test) showed mixed results. While some scales did not significantly correlate with driving behavior and stress indicators, other subscales like attentional control revealed good prediction coefficients. The current results can be used to train young drivers in raising their mental readiness level which could affect driving performance and road safety positively. In addition, the transfer of the central outcomes of this study to other safety-critical task environments, are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".