Distracted Driving Among Patients with Trauma Attending Fracture Clinics in Canada
Notice bibliographique
Résumé
BACKGROUND: Globally, every 25 seconds, a person dies in a motor vehicle crash (MVC) and 58 people get injured. Adding to the rising distracted-driving rates is the rapid growth of the number of cars in circulation globally. This study examined the proportion of distracted drivers among patients attending orthopaedic fracture clinics, as well as associated factors. METHODS: In this large, multicenter, cross-sectional study, we recruited 1,378 patients across 4 Canadian fracture clinics. Eligible patients completed an anonymous questionnaire about distracted driving. We calculated the percentages of specific distractions. Using questionnaire responses and published crash risk odds ratios (ORs), patients were grouped as distraction-prone and distraction-averse. Regression analyses to determine the association of demographic characteristics with distracting behaviors and the odds of being in a distraction-related crash were performed. RESULTS: In total, 1,358 patients (99.7%) self-reported distracted driving. Prevalent distractions included talking to passengers (98.7%), distractions outside the vehicle (95.5%), listening to the radio (97.6%), adjusting the radio (93.8%), and daydreaming (61.2%). Of the 1,354 patients who acknowledged mobile phone distractions, 889 (65.7%) accepted phone calls and continued driving, 675 (49.8%) read electronic messages, and 475 (35.1%) sent electronic messages. Younger age (OR, 0.94 [95% confidence interval (CI), 0.91 to 0.97]; p < 0.001) and household incomes of $80,000 to <$100,000 (OR, 1.92 [95% CI, 1.17 to 3.14]; p = 0.01) and ≥$100,000 (OR, 2.48 [95% CI, 1.57 to 3.91]; p < 0.001) were associated with being in the distraction-prone group. Distraction-prone patients were twice as likely to be in a distraction-related MVC (OR, 1.98 [95% CI, 1.43 to 2.74]; p < 0.001). Of 113 drivers who sustained injuries from MVCs, 20 (17.7%) acknowledged being distracted. Of 729 patients who reported being the driver in a previous MVC in their lifetime, 226 (31.0%) confirmed being distracted. CONCLUSIONS: This survey-based study showed that driving distractions were near universally acknowledged. The pervasiveness of distractions held true even when only the more dangerous distractions were considered. One in 6 patients in MVCs reported being distracted in their current crash, and 1 in 3 patients disclosed being distracted in an MVC during their lifetime.
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 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 ».