Canadian population-based survey of commercial drivers during the COVID-19 pandemic: Health- and safety-related factors affecting collision risk
Bibliographic record
Abstract
Commercial motor vehicles are imperative to Canada to deliver goods and services. Timely delivery during the COVID-19 pandemic meant commercial drivers had to work longer hours in difficult conditions, with increased risk of COVID-19 exposure, morbidity and mortality. were to: (1) compare drivers with commercial drivers' licences with matched drivers without commercial drivers' licences on health and safety factors and driving during the pandemic; (2) examine predictors of collisions since the pandemic among drivers with commercial drivers’ licences. A sub-analysis of a population-based online survey of Canadian drivers was conducted examining impact of COVID-19 on health and safety factors and driving. Socio-demographics, health and driving variables were compared between matched drivers with and without commercial licences and logistic regression analysis assessed the impact of COVID-19-related health and safety factors on likelihood of commercial driver involvement in collisions. Commercial drivers drove significantly more kilometres, were more likely to have been stopped by police, and more likely to have had at least one collision during the pandemic than non-commercial drivers. No between group differences were found for distress, worry about COVID-19, vaccine status and testing positive for COVID-19, speeding, driving after alcohol or cannabis use. Drivers with commercial licences who scored higher on distress, reported less worry about COVID-19, increased speeding and being stopped by the police were all significantly associated with more self-reported collisions. Health and safety factors need to be considered for drivers with commercial licences for collision involvement in future pandemics. • Commercial licensed (CL) drivers drove more during COVID-19 than non-CL drivers. • CL drivers were equally vaccinated against COVID as non-CL drivers. • No group differences for distress, speeding and driving after alcohol or cannabis. • More CL drivers reported police stops and collisions during the pandemic. • Distress, less COVID worry, speeding and police stops predicted CL driver crashes.
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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 | 0.002 | 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 | 0.001 | 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".