Canadian population-based survey of commercial drivers during the COVID-19 pandemic: Health- and safety-related factors affecting collision risk
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».