Advancing the Safety, Health, and Well-Being of Commercial Driving Teams Who Sleep in Moving Semi-Trucks
Bibliographic record
Abstract
OBJECTIVE: To test the feasibility, acceptability, and potential effectiveness of engineering and behavioral interventions to improve the sleep, health, and well-being of team truck drivers (dyads) who sleep in moving semi-trucks. METHODS: Drivers (n = 16) were exposed to Condition A: a new innerspring mattress, and Condition B: a novel therapeutic mattress. A subsample of drivers (n = 8) were also exposed to Condition C: use of their preferred mattress (all chose to keep B), switching to an active suspension driver's seat, and completing a behavioral sleep-health program. Primary outcomes were sleep duration, sleep quality, and fatigue. Behavioral program targets included physical activity and sleep hygiene. RESULTS: Self-reported sleep and fatigue improved with mattress A, and improved further with mattress B which altered vibration exposures and was universally preferred and kept by all drivers. Condition C improved additional targets and produced larger effect sizes for most outcomes. CONCLUSIONS: Results support these interventions as promising for advancing team truck drivers' sleep, health, and well-being.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".