Occupational health profile of Canadian Maritimes truck drivers
Bibliographic record
Abstract
BACKGROUND: There are over 12,000 professional truck drivers in the Canadian Maritime provinces, with the majority being in New Brunswick and Nova Scotia. Previous studies have focused on the health of Canadian and American truck drivers but the occupational health status of truck drivers in the Maritime Provinces remains undocumented. OBJECTIVE: The objective of this cross-sectional study was to provide a general, occupational health and demographic characteristics description of professional truck drivers in the Maritimes. METHODS: One-hundred and four male truck drivers from the Canadian Maritime Provinces volunteered for this study. Nine occupational health indicators were measured (seven were self-reported via questionnaire and two were physical measurements). Participants self-reported their age, years of truck driving experience and education. RESULTS: Only one-quarter of the current sample had no health conditions. In contrast, more than half were obese, one third had back problems, and one-sixth had a high risk of developing cardiovascular disease (CVD). The group comparison analysis showed that the group without health condition was younger and more educated than the group with multiple health conditions. For this study, age and low rate of education were associated with an increased number of health conditions. CONCLUSIONS: Similar to health profiles of other populations of North American truck drivers, this study suggests that the majority of truck drivers in the Canadian Maritime Provinces have at least one poor indicator of occupational health.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".