Which drivers are at risk? Factors that determine the profile of the reoffender driver. (October 2018)

• Record Type: Journal Article
• Authors: Padilla, Jose-Luis; Doncel, Pablo; Gugliotta, Andres; Castro, Candida
• Is Part Of: Accident analysis and prevention. Volume 119(2018)
• Page Start: 237
• Page End: 247
• Publication Date: 2018-10
• Subjects: Recidivist -- Road safety -- Anger -- Risk perception -- Sensitivity to punishment -- Sensitivity to reward -- Driving under the influence (DUI) -- Driving while intoxicated (DWI); Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2018.07.021
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: Alcohol Consumption, Incautious Driving Style, Sensitivity to Reward and Recreational Risk Infraestimation predict recidivism. Reoffender drivers' assessment should be based on DWI (Driving While Intoxicated) and the practice of incautious driving styles. And interventions should be based on detoxification programmes and the introduction of reinforcement measures. Abstract: Finding appropriate assessment tools to predict recidivism is a difficult aim, which may lead to actions with unintended consequences. Aims don't have consequences. At times, the research has been used to justify penalising reoffenders with punitive measures rather than treating them with effective psychological interventions. This study aims to contribute to untangling and assessing the potential predictors of reoffender drivers. In this study, 296 drivers: 86 reoffenders (7 women and 79 men) and 206 non-reoffenders (105 women and 101 men) responded to a battery of assessment questionnaires in which they were asked for demographic data (i.e. gender and age), alcohol consumption habits, driving styles, general estimation of risk in everyday life, sensitivity to reward and punishment and anger while driving. The results provided a logistical regression model capable of predicting reoffending and explaining 34% of variability, successfully classifying 77.6% of participants. In this model, the best predictor of reoffending is higher consumption of alcohol (Alcohol Use Disorders, AUD), followed by incautious driving (since cautious driving style correlates negatively with reoffending) and to a lesser extent, infraestimation of recreational risk and a greater sensitivity to reward. Relying on results to predict recidivism could be important to plan better interventions to prevent it.