Medical, Social and Psychological Predictors of an Individual's Readiness to Drive a Vehicle While Intoxicated

The Introduction. The frequency of alcohol-impaired driving accidents has decreased both in Russia and around the world; however, the indicators remain high. Many researchers tend to believe that effective prevention of drunk driving should include not only legal measures but also medical-psychological and social influence.

The aim of the study was to explore predictors of traffic violations related to driving under the influence.

Materials and methods. The sample included drivers having driving licenses revoked due to alcohol-impaired driving who contacted addiction treatment services in order to apply for a driving license after the end of a withdrawal period due to driving while intoxicated (427 participants aged 20-72), as well as drivers with no administrative penalty who contacted addiction treatment services to renew a driving license after its expiration date (84 participants aged 18-70). The data was recorded using the NS-Psychotest Medical computer complex. Statistical data processing performed in IBM SPSS 23 included descriptive statistics, the Mann-Whitney U test for large samples, Pearson's chi-square test (differences in the distribution of nominal data), exploratory factor analysis, and regression analysis.

Results. Deep statistical analysis allowed to identify predictors of driving under the influence: high levels of distress and stress overload, difficulties with emotional self-regulation, and demonstrative behavior with hyperthymia as a personality type increased the risk of illegal driving behavior, while changes in some cognitive functions and slower reaction times reduced the risk of traffic violation related to intoxication.

Conclusion. Risk and protective factors for driving under the influence were identified. A recommendation to include prognostic variables related to personal and moral values that correspond to the biological, psychological, social, and moral model of addictive behavior in the analysis of predictors was given.

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