Examining the association between clinical tests and simulated driving performance in long-haul truck drivers
Bibliographic record
Abstract
OBJECTIVES: The objectives of this study are to profile LHTD health, clinical test scores, and simulated driving performance; examine associations between clinical test scores and simulated driving errors in LHTD; and examine differences between LHTD simulated driving performance in different weather, lighting, and traffic conditions. 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, a battery of 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. When operating in nighttime, rural, and winter conditions, LHTD made significantly more lane maintenance and speed regulation errors compared to the daytime, urban, and summer drive. In contrast, LHTD made significantly more signaling errors during the daytime urban drive compared to the nighttime, winter drive. Together these findings show that LHTD face significant challenges in a variety of driving environments. When combining both simulated drives, our findings show that poorer TMTA scores were significantly associated with more speed regulation (i.e., over-speeding and hard braking) and total driving errors. Additionally, poorer TMTB and UFOV-2 scores were significantly associated with more adjustment to stimuli, speed regulation, and total driving errors. CONCLUSIONS: Our study highlights the critical role of visual search, processing speed, and divided attention on driving performance, and the significant impact of environmental factors (e.g., lighting; weather; traffic) on the occurrence of specific driving errors and crashes. The integration of cognitive assessments (e.g., UFOV; TMTB) should be considered for inclusion as part of the mandatory medical examinations to ensure LHTD can safely operate their commercial motor vehicle.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".