Road transport safety is a major concern across Europe due to the human and socio-economic costs associated with work-related traffic accidents. Traditional approaches have adopted regulatory and technical measures to prevent road accidents leaving aside the organizational factors that might contribute to road transport safety. However, contemporary sociotechnical systems theory acknowledges the need to take into account organizational factors. This study adopts a sociotechnical approach and it examines the relationship between a number of organizational factors (organizational learning and training) and road traffic accidents in the organizations under study. Our sample was composed of 107 road transportation organizations from Spain. Binary logistic regression analyses were carried out to test our hypotheses. Organizational size and type of transport (goods or passengers) were included in the model as control variables. Results showed that in those organizations where organizational learning was supported, the occurrence of traffic accidents was less likely. Unexpectedly, the relationship between training and the occurrence of traffic accidents was not significant. Thus, findings partially supported the formulated hypothesis. Future research should shed light on the relationship between training and traffic accidents taking into account potential intervening variables.
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http://dx.doi.org/10.3390/ijerph15091938 | DOI Listing |
PLoS One
January 2025
Graduate Institute of Injury Prevention and Control, College of Public Health, Taipei Medical University, Taipei City, Taiwan.
Background And Objective: Relevant research has provided valuable insights into risk factors for bicycle crashes at intersections. However, few studies have focused explicitly on three common types of bicycle crashes on road segments: overtaking, rear-end, and door crashes. This study aims to identify risk factors for overtaking, rear-end, and door crashes that occur on road segments.
View Article and Find Full Text PDFPLoS One
January 2025
School of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing, Jiangsu, China.
A classification prediction model is established based on a nonlinear method-Gradient Boosting Decision Tree (GBDT) to investigate the factors contributing to a perpetrator's escape behavior in hit-and-run crashes. Given the U.S.
View Article and Find Full Text PDFTunis Med
December 2024
Department of Anesthesiology and Intensive Care, Sahloul University Hospital, Sousse, Tunisia.
Unlabelled: Introduction-Aim: Validated triage tools such as the Vittel criteria are essential to improve the care of trauma patients. The aim of this study was to evaluate the correlation between the Vittel triage criteria and the Injury Severity Score (ISS) to improve the accuracy of pre-hospital triage.
Methods: We conducted a longitudinal study of all trauma patients transported by EMS over a two-year period (November 2021- November 2023).
PLoS One
January 2025
Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran.
Predicting incident duration and understanding incident types are essential in traffic management for resource optimization and disruption minimization. Precise predictions enable the efficient deployment of response teams and strategic traffic rerouting, leading to reduced congestion and enhanced safety. Furthermore, an in-depth understanding of incident types helps in implementing preventive measures and formulating strategies to alleviate their influence on road networks.
View Article and Find Full Text PDFFront Immunol
January 2025
Institute of Pharmaceutical Sciences, University of Veterinary and Animal Sciences, Lahore, Punjab, Pakistan.
Introduction: Tetanus, caused by , poses a life-threatening risk by affecting the nervous system and inducing muscle tightness. The objective of this study is to examine the knowledge, attitudes, and behaviors of non-medical university students regarding the tetanus vaccine in the context of post-road accidents.
Methods: A descriptive cross-sectional study was conducted in 2023, involving 378 students from non-medical disciplines, primarily from information technology, business administration, and engineering faculties, with a mean age of 20.
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