Health and wellness of Canadian commercial motor vehicle drivers
Bibliographic record
Abstract
Purpose The purpose of this paper is to solicit perspectives from stakeholders concerning health, environmental and operational challenges among Commercial motor vehicle (CMV) drivers in Canada (truck and bus drivers). Design/methodology/approach Two focus groups and one interview were conducted with key industry, government and advocacy groups representing or working with CMV drivers. Perspectives pertaining to working conditions, health issues, driver recruitment and retention, and other key issues in the CMV sector were obtained. Findings The findings show that undesirable working conditions are primary issues that impact recruitment and retention, as well as health and wellness (H&W), and productivity of drivers in both the truck and bus sectors. Compared to our US counterparts, finding parking areas and rest stops were seen as a major issue for Canadian truckers (particularly in the north). Unfortunately, there is limited or out-dated information on drivers and companies in Canada. Stakeholders stated the need for more information from both carriers/companies and from drivers themselves (particularly long-haul drivers). Research limitations/implications This study identifies gaps and key priority research areas pertaining to the H&W of the CMV sector in Canada that require further investigation. Originality/value CMV drivers are considered a vulnerable sector of the population. While drivers themselves have reported on undesirable work conditions leading to poor health, prior studies have not assessed the awareness or perspective of stakeholders involved in the CMV sector. This is the first study to capture stakeholder perspectives of the working conditions and health outcomes of CMV drivers.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".