The Impact of COVID-19 on the Work Environment in Long-Haul Truck Drivers
Bibliographic record
Abstract
OBJECTIVE: To describe and compare the working conditions of long-haul truck drivers (LHTD) before and during the Coronavirus (COVID-19) pandemic and to assess the perceptions of LHTDs on accessing food, restrooms, and parking. METHODS: An online survey was disseminated between August 2020 and March 2021 to various trucking organizations across Canada to collect data on health and wellness during COVID-19. Data were analyzed using descriptive and inferential statistics, and thematic analysis for open-ended responses. RESULTS: The sample included 146 LHTD (mean age 48.1 ± 11.8; 82.2% were men). Participants reported issues with finding parking, washrooms, and food. Compared with before COVID-19, LHTD worked significantly more hours and consumed more caffeine; and more than 50% reported being fatigued. CONCLUSIONS: Improving the working conditions of LHTD is critical to support their health and wellbeing, both during and after the pandemic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".