Sociodemographic and psychological factors affecting motor vehicle crashes (MVCs): a classification analysis based on the contextual-mediated model of traffic-accident involvement
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Published:2024-06-27
Issue:31
Volume:43
Page:25683-25703
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ISSN:1046-1310
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Container-title:Current Psychology
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language:en
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Short-container-title:Curr Psychol
Author:
Tinella LuigiORCID, Bosco AndreaORCID, Koppel SjaanORCID, Lopez AntonellaORCID, Spano GiuseppinaORCID, Ricciardi ElisabettaORCID, Traficante Sergio, Napoletano Rosa, Grattagliano IgnazioORCID, Caffò Alessandro OronzoORCID
Abstract
AbstractThe study aimed to determine the sociodemographic and psychological profiles of drivers with a history of motor vehicle crashes (MVCs), following the contextual-mediated model of crash involvement, and trying to define similarities and differences with drivers without MVCs. Although road trauma prevention has become a central public health issue, the study of psychological determinants of MVCs does not have consistent results due to methodological and theoretical weaknesses. Three-hundred and forty-five active drivers (20% females) completed an extensive office-based fitness-to-drive evaluation including measures of cognition, personality, self-reported driving-related behaviors, attitudes, as well as computerized measures of driving performance. The Classification and Regression Tree method (CART) was used to identify discriminant predictors. The classification identified several relevant predictors; the personality trait of Discostraint (as a distal context variable; cut-point: 50 T points) and motor speed (as a proximal context variable; cut-point: 64 percentile ranks). The global classification model increased approximately 3 times the probability of identifying people with a history of MVC involvement, starting from an estimated prevalence of being involved in an MVC in a period of five years in the population of active drivers. Consistent with the ‘contextual-mediated model of traffic accident involvement’, the results of the present study suggest that road trauma analysis should focus on both distal and proximal driver-related factors by paying attention to their association in determining MVCs. These results represent a valuable source of knowledge for researchers and practitioners for preventing road trauma.
Funder
Università degli Studi di Salerno
Publisher
Springer Science and Business Media LLC
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