Long-Haul Truck Driver Training Does Not Meet Driver Needs in Canada
Bibliographic record
Abstract
INTRODUCTION: Training standards for long-haul truck drivers (LHTD) are rapidly evolving in Canada, yet the opinions of the drivers themselves have not been adequately considered. The purpose was to survey LHTD on their work training history and to examine LHTD perceptions of driver training and licensing protocols. METHODS: LHTD were recruited across two Western Canadian provinces from seven different truck stops. The sample completed 207 surveys and 67 semi-structured interviews. RESULTS: The average age of the participants was 52.5 ± 11.5 years (range 24-79); 96% were men. Approximately 33% of the LHTD had at least one crash. Those who did not receive formal driver training were significantly more likely to crash than those who had received training. Participants stated that current training standards are inadequate for the industry, particularly for new drivers. According to participants, entry-level curriculums should consist of both classroom and practical training, as well as on-road observation with a senior mentor. LHTD reported that many new drivers are not equipped to drive in various contexts and settings (e.g., mountains, slippery roads). CONCLUSIONS: LHTD are not confident in the current training guidelines for novice truck drivers. Revisions to the training curriculum and standardization across Canada should be considered. PRACTICAL APPLICATION: A federal mandatory entry-level training program is needed in Canada to ensure that all new LHTD ascertain the necessary skills to drive safely. Such a program requires government involvement and input from LHTD to facilitate appropriate licensure and consistent training for all drivers.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".