Enhancing physical activity knowledge exchange strategies for Canadian long-haul truck drivers
Bibliographic record
Abstract
Purpose Canadian long-haul truck drivers lead sedentary lives, but are receptive to receiving physical activity information to address health risks. This study examined how Canadian long-haul truck drivers would like to receive physical activity information in order to improve their overall health. The purpose of this study was twofold: 1) explore barriers Canadian long-haul truck drivers have to receiving and using physical activity information and 2) understand how physical activity information should be structured and delivered to these drivers to overcome these barriers. Design/methodology/approach Semi-structured interviews were conducted with 12 Canadian long-haul truck drivers. Drivers had, on average, 14.3 years of professional long-haul driving experience. Findings Few drivers had received any physical activity information. Drivers discussed a culture where they perceived both employers and drivers to be lacking awareness of the importance of physical activity and its impact on health. Drivers explained they were too busy, stressed or tired to be active or to learn about physical activity. Information received by some drivers on this topic was too general to be helpful in changing physical activity behaviours. Drivers mentioned that personalized and accessible physical activity information should be provided to them through multiple methods by their employers, as an aspect of occupational health and safety. Practical implications Future physical activity information strategies should use both passive and interactive mediums to promote physical activity to Canadian long-haul truck drivers. Originality/value This is the first study to assess how Canadian long-haul truck drivers would like to receive trustworthy information that can lead to healthful improvements in physical activity behaviour.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".