Sleep Quality of Heavy Vehicles’ Professional Drivers: An Analysis Based on Self-Perceived Feedback
Bibliographic record
Abstract
Abstract Introduction Sleep is a crucial biological need for all individuals, being reparative on a physical and mental level. Driving heavy vehicles is a task that requires constant attention and vigilance, and sleep deprivation leads to behavioral and physiological changes that can develop sleep disorders which can put lives at risk. Objectives The main objectives of this study are to describe and evaluate sleep quality, excessive daytime sleepiness, circadian preference, and risk of suffering from obstructive sleep apnea in a population of Portuguese professional drivers. Methods To fulfill the objectives, 43 Portuguese professional drivers, between 23 and 63 years old, answered validated questionnaires: Epworth Sleepiness Scale, Morningness–Eveningness, Stop-Bang Questionnaire, and Pittsburgh Sleep Quality Index. Results Results indicated that older drivers tend to experience higher daytime sleepiness (11 ± 3.4; p = 0.002) and obstructive sleep apnea risk (4.5 ± 1.5; p = 0.03). Regarding sleep quality, the majority of drivers were classified with poor sleep quality (74.4%). It was possible to infer statistical differences between groups based on body mass index ( p = 0.037), the type of route ( p = 0.01), and physical activity ( p = 0.005). Conclusion Drivers have an indifferent circadian preference and small-course drivers have a worse sleep health perception. Therefore, it is essential to implement prevention programs, promoting the basic rules for better sleep quality as well as identifying sleep disorders to minimize possible road accidents.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".