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Identification of Factors Contributing to Traffic Accidents amongst Girls in Tehran with Specific Focus on Psychosocial Factors

OBJECTIVE: To identify the human factors contributing to traffic accidents with a special focus on psychosocial factors amongst young girls of Tehran, Iran. METHODS: In a descriptive study conducted in Tehran, Iran in 2013, 108 girls aged 18-24 were enrolled by using a stratified cluster sampling me...

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Detalles Bibliográficos
Autores principales: Tahmasebi, Siyamak, Javadi, Seyyed Mohammad Hossein, Azari Arghun, Tahereh, Edrisi, Forughe, Tajlili, Alireza
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Shiraz University of Medical Sciences 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7071933/
https://www.ncbi.nlm.nih.gov/pubmed/32201698
http://dx.doi.org/10.29252/beat-080104
Descripción
Sumario:OBJECTIVE: To identify the human factors contributing to traffic accidents with a special focus on psychosocial factors amongst young girls of Tehran, Iran. METHODS: In a descriptive study conducted in Tehran, Iran in 2013, 108 girls aged 18-24 were enrolled by using a stratified cluster sampling method. Participants filled a wide range of validated questionnaires about traffic psychology. RESULTS: The developed psychological model about behaviors of drivers’ factors as well as agreeable and aggressive personality trait with B coefficient of 0.25% and 0.37% were able to predict violation, driving style, perception of police laws, and off hook scheme and the mistrust with B coefficient of 0.33%, 0.23% and 0.28% in the level of 0.1 were able to predict violations and lapses of sample group, respectively. Extroversion with B coefficient of 0.27% also predicted unintentional violations of girls. B coefficient for perception of police laws was 0.22%. This was 0.25% for openness to experiences. Concerning driving accidents, the perception of police rules has the highest predictability. CONCLUSION: According to the results of the current research amongst girls in Tehran, a gender-sensitive interventional model can be designed for reduction of traffic accidents for this population group.