Lone workers attitudes towards their health: views of Ontario truck drivers and their managers
Bibliographic record
Abstract
BACKGROUND: Truck driving is the second most common occupation among Canadian men. Transportation of goods via roads is of crucial importance for the Canadian economy. The industry is responsible annually for $17 billion in GDP and is projected to increase by 28% over the next 10 years. Recruitment is an issue with 20% of drivers projected to retire or leave the profession in the next 10 years. Despite the reliance on transport truck drivers for the delivery of goods which affects Canada's economy and daily living of residents, little is known about the health care needs of this large cohort of primarily male lone workers from a drivers' perspective. Transport truck drivers are independent workers whose non traditional workplace is their tractor, the truck stops and the journey on the road.The objective of this study was to obtain a contextually informed description of lifestyle issues, health and disease risk factors experienced by drivers and perceived by their managers in the truck driving occupation. METHODS: Using a grounded theory approach, 4 focus groups were conducted with drivers (n = 16) and managers (n = 10) from two trucking companies in Southwestern Ontario to identify the lived experience of the drivers as it relates to preventable risks to health and wellness. A semi structured guided interview was used to explore the lifestyle context of transport truck driving and organizational aspects of the occupation (workplace culture, working conditions and health and wellness promotion). RESULTS: The predominant themes described stress, workplace, communication, lifestyle, driving culture, family, and fatigue concerns. In terms of the transportation work environment, drivers and managers were aware of the profession's potential to foster lifestyle related chronic diseases but described challenges in making the profession more amenable to a healthy lifestyle. CONCLUSIONS: Workplace environmental determinants are significant in shaping health behaviours. Chronic disease health risks were the main health concerns identified. Health risks were exacerbated by working conditions (job demands, work hours, financial pressure and the sedentary nature of the job). Workplace health strategies will need to take into account the unique challenges of the occupation.
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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 | 0.002 | 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 | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".