Examining the association between driver perceptions with clinical tests and simulated driving performance in Canadian long-haul truck drivers
Bibliographic record
Abstract
Background Long-haul truck drivers (LHTD) routinely navigate challenging driving environments, which requires high levels of visual, cognitive, and physical functioning. While prior studies in older adults have demonstrated significant associations between functional abilities, self-perceptions, and driving performance, these relationships have not been examined among LHTD. Objectives The objectives of this study are to: (1) describe LHTD perceptions; (2) examine the associations between clinical test scores and driver perceptions; and (3) examine the associations between driver perceptions and simulated driving errors. Methods LHTD were recruited from various provincial and federal trucking associations and trucking companies across Canada. A sample of 36 LHTD completed a demographic questionnaire; objective health measures; Driving Comfort Scales (DCS) and Perceived Driving Abilities Scale (PDA); cognitive, visual, and motor tests; and two simulated drives with different environmental conditions. Results The mean age of the sample was 47.9 ± 12.3 years (range 22–69); 94.4 % were men. LHTD were highly comfortable driving during the day (DCS-D: 85.3 ± 9.2) and at night (DCS-N: 80.8 ± 12.3), however, their DCS-N scores were significantly positively associated with vehicle position, speed regulation, and adjustment to stimuli errors. Poorer scores on the RPWT, TMTA, and TMTB were significantly associated with lower driving comfort in scenarios that require quick decision-making (e.g., heavy traffic, other drivers not signaling). Conclusions The study highlights the significant role of physical and cognitive abilities in shaping LHTD self-perceptions, particularly in challenging conditions. To improve cognitive and on-road driving performance, future research should explore the development of a continuing education course for LHTD.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".