8138250 Truck stop infrastructure do not meet the needs of truck drivers in Canada
Bibliographic record
Abstract
<h3>Objectives</h3> The objectives of this study were to: 1) determine the average distance between truck stops in Canada; 2) describe the current state of truck stops; 3) compare the available amenities (e.g., number and type of restaurants) within walking distance of truck stops along major routes/highways; and 4) determine opportunities for truck stop improvement in Canada. <h3>Material and Methods</h3> For objectives 1 to 2, an environmental scan leveraged multiple data sources (e.g., books, websites) to collect data from over 700 truck stops in Canada. Data were collected on truck stop amenities (e.g., parking availability, nutrition options) and distance between truck stops. For objective 3, long-haul truck drivers (LHTD) were recruited from various provincial/federal trucking associations and trucking companies across Canada. A sample of 406 LHTD completed an online survey that consisted of questions on demographics, health conditions and behaviours, and preferences for truck stop amenities. Inferential data analysis was performed using SPSS; statistical significance was set at p ≤ 0.05. <h3>Results</h3> LHTD reported that parking, healthy and affordable foods, showers, washrooms, and exercise facilities with basic equipment are the most important amenities needed at truck stops. However, 33.3% of all truck stops are closed at night, only 13% have access to Wi-Fi, less than 50.0% have shower facilities, and more than half of truck stops do not provide healthy and affordable foods. The average distance between truck stops are 200-300 kilometers apart. Recommendations include investing in truck stop infrastructure to include additional parking, healthier food options, WiFi, and developing safety standards for truck stop services and amenities. <h3>Conclusions</h3> Improvements to truck stop environments are needed to support the health and wellness of LHTD. The findings show there are significant gaps between what truck drivers need versus what is often available.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".