Fostering mental health and chronic diseases self-management among professional truck drivers
Bibliographic record
Abstract
Abstract Most passengers and goods in Canada travel by road. The trucking industry is the backbone of the tangible goods economy. However, the health and well-being of this aging workforce is in jeopardy. Recent data reveals that 86% of the truckers’ community are 50 years old and over, 4% are female and 5% are immigrants. Moreover, 75% of the male truckers self-reported one or more health condition (54% obesity, 19% hyperlipidemia, 18 % high blood pressure, 11 % type II diabetes). Despite, the high prevalence of risk factors (e.g. stress, depression/anxiety, lower level of education, social isolation and financial challenges) and preventable chronic diseases among truckers, in New Brunswick and elsewhere in Canada, there is a lack of on-the-road accessible lifestyle change programs. Therefore, tailored interventions are needed to appropriately support them adopt healthy behaviors. Using the Re-AIM Framework, we carried out 23 semi-structured interviews to inform the development of tailored educational material. The aims were: to describe the needs and challenges and to design a truckers-sensitive educational intervention. The theoretical foundation of this qualitative study is articulated around concepts extracted from cognitive and behavioural theories (transtheoretical model of behaviour change). Qualitative analysis of verbatims identified four major themes: Lifestyle challenges, Social and individual representation of healthy behaviors, Health education strategies and communication and Motivational and engagement strategies. Drawing upon these findings we developed tailored educational material and pre-validated them with a small group of professional truck drivers. Our findings informed the development of an educational intervention to support truckers manage and improve their mental health and self-management of chronic diseases. The next step is to implement a randomized clinical trial to test and assess acceptability, feasibility, and effectiveness of our intervention. Key messages
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.003 |
| 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".