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西班牙教师职业交通事故。

Occupational Traffic Accidents among Teachers in Spain.

机构信息

Ph.D. Program Mechatronics Engineering, School of Industrial of Industrial Engineers, University of Málaga, 29071 Malaga, Spain.

Consejería de Educacion y Deporte, 29071 Malaga, Spain.

出版信息

Int J Environ Res Public Health. 2022 Apr 24;19(9):5175. doi: 10.3390/ijerph19095175.

DOI:10.3390/ijerph19095175
PMID:35564569
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9105916/
Abstract

Occupational traffic accidents are a leading cause of injuries or deaths among workers. Teachers in Spain are especially concerned about the problem of commuting due to their particular labor conditions. Multiple work-related factors are associated with the risk and severity of occupational traffic-related motor vehicle crashes. The objective of this research is to analyze the influence of the variables associated with the severity of occupational traffic accidents among teachers in Spain. A logistic regression model was used for the current study. The odds ratio (OR) and confidence interval (CI) were calculated for the injured worker on a sample of 20,190 occupational traffic accidents suffered by teachers. The results showed that women, Spanish nationality, younger than 55 years, and those driving a car were more likely to suffer a light crash. In contrast, men, foreign nationalities, older than 55 years, and those riding a motorbike were more likely to suffer a serious crash. Based on these findings, motor vehicle safety training could be designed and adapted to the riskiest profiles. Additionally, effective mobility plans for commuting could help reduce work-related traffic accidents.

摘要

职业交通事故是工人受伤或死亡的主要原因之一。西班牙的教师特别关注通勤问题,因为他们的工作条件特殊。多种与工作相关的因素与职业交通相关的机动车碰撞的风险和严重程度有关。本研究的目的是分析与西班牙教师职业交通事故严重程度相关的变量的影响。本研究使用了逻辑回归模型。对 20190 名教师职业交通事故受伤工人的样本进行了比值比(OR)和置信区间(CI)的计算。结果表明,女性、西班牙国籍、55 岁以下以及驾驶汽车的人更有可能发生轻微碰撞。相比之下,男性、外国国籍、55 岁以上以及骑摩托车的人更有可能发生严重碰撞。基于这些发现,可以设计和调整针对风险最高人群的汽车安全培训。此外,有效的通勤出行计划有助于减少与工作相关的交通事故。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84a3/9105916/163390473b7f/ijerph-19-05175-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84a3/9105916/49c2b44ce280/ijerph-19-05175-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84a3/9105916/331e152ba3d5/ijerph-19-05175-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84a3/9105916/163390473b7f/ijerph-19-05175-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84a3/9105916/49c2b44ce280/ijerph-19-05175-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84a3/9105916/331e152ba3d5/ijerph-19-05175-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84a3/9105916/163390473b7f/ijerph-19-05175-g003.jpg

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Health Care Women Int. 2022 Sep;43(9):1084-1094. doi: 10.1080/07399332.2021.1963731. Epub 2021 Sep 17.
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Int J Environ Res Public Health. 2021 Mar 31;18(7):3611. doi: 10.3390/ijerph18073611.
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Modeling crash severity by considering risk indicators of driver and roadway: A Bayesian network approach.考虑驾驶员和道路风险指标的事故严重程度建模:贝叶斯网络方法。
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