Impact of limited rest areas on truck driver crashes in Saskatchewan: a mixed-methods approach
Bibliographic record
Abstract
BACKGROUND: Long-haul truck drivers (LHTDs) suffer from long work hours often resulting in fatigue. Although several studies have reported that fatigue can contribute to crashes, no study has identified the location and patterns of fatigue-related crashes and solicited truck driver feedback on potential mitigation strategies. The purpose of this study is 1) to map the location of fatigue-related crashes and 2) examine the perceptions of truck drivers concerning fatigue-related crashes. METHODS: Using databases from the Saskatchewan Government Insurance, information on LHTD demographics, crashes and their causes, as well as crash location was analyzed. All fatigue-related crashes were then documented and mapped. Additionally, we interviewed 67 LHTDs (mean age = 53.0 ± 12.9; range 23-89; 95% were men) asking questions about fatigue, access to truck stops/rest areas, and provided recommendations for improvement. All interviews were subsequently analyzed using thematic analyses. RESULTS: On average, there were 20 fatigue-related crashes per year over the 10-year period. Fatigue-related crashes were common across Saskatchewan, however, there was a concentration of crashes along major roadways between major cities. There was a significant association between crashes with age and experience. Despite many LHTDs being fatigued, there was a lack of truck stops/rest areas along highway routes. LHTDs suggested having access to truck stops/rest areas 250-400 km apart with running water and washrooms available. CONCLUSIONS: Additional truck stops and rest areas are needed in Saskatchewan to ensure LHTDs have more opportunities for rest to reduce fatigue in general, as well as to reduce the risk of fatigue-related crashes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".