Literature DB >> 15545234

The relationships between organizational and individual variables to on-the-job driver accidents and accident-free kilometres.

J K Caird1, T J Kline.   

Abstract

Highway fatalities are the leading cause of fatal work injuries in the US, accounting for approximately 1 in 4 of the 5900 job-related deaths during 2001. The present study focused on the contribution of organizational factors and driver behaviours to on-the-job driving accidents in a large Western Canadian corporation. A structural equation modelling (SEM) approach was used which allows researchers to test a complex set of relationships within a global theoretical framework. A number of scales were used to assess organizational support, driver errors, and driver behaviours. The sample of professional drivers that participated allowed the recording of on-the-job accidents and accident-free kilometres from their personnel files. The pattern of relationships in the fitted model, after controlling for exposure and social desirability, provides insight into the role of organizational support, planning, environment adaptations, fatigue, speed, errors and moving citations to on-the-job accidents and accident-free kilometres. For example, organizational support affected the capacity to plan. Time to plan work-related driving was found to predict accidents, fatigue and adaptations to the environment. Other interesting model paths, SEM limitations, future research and recommendations are elaborated.

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Mesh:

Year:  2004        PMID: 15545234     DOI: 10.1080/00140130412331293355

Source DB:  PubMed          Journal:  Ergonomics        ISSN: 0014-0139            Impact factor:   2.778


  3 in total

Review 1.  Characterizing the Interrelationships of Prescription Opioid and Benzodiazepine Drugs With Worker Health and Workplace Hazards.

Authors:  Michele Kowalski-McGraw; Judith Green-McKenzie; Sudha P Pandalai; Paul A Schulte
Journal:  J Occup Environ Med       Date:  2017-11       Impact factor: 2.162

2.  Falling asleep at the wheel and distracted driving. The High-Risk Professional Drivers study.

Authors:  Gian Luca Rosso; Stefano Candura; Massimo Perotto; Michele Caramella; Cristina Montomoli
Journal:  Med Lav       Date:  2018-03-27       Impact factor: 1.275

3.  Predicting Crashes Using Traffic Offences. A Meta-Analysis that Examines Potential Bias between Self-Report and Archival Data.

Authors:  Peter Barraclough; Anders Af Wåhlberg; James Freeman; Barry Watson; Angela Watson
Journal:  PLoS One       Date:  2016-04-29       Impact factor: 3.240

  3 in total

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