Potential Benefits of Case Management for Recidivist Traffic Offenders

Despite extensive penalties and sanctions, a small group of drivers persistently reoffend, contributing to many road injuries each year. While transport authority data identifies these drivers as predominantly male, young, low-socioeconomic area residents, and commonly unlicensed, wider research reveals strong links to general disadvantage. This includes low education, unemployment, cultural and ethnic minority backgrounds, and co-occurring personality, cognitive and/or mental health challenges. As such, an in-depth literature review was undertaken to explore whether adopting a case management approach – to work one-on-one with these individuals to address wider contributing factors – has potential to reduce reoffending and associated road trauma. Transport, science and education literature databases and key transport authority and research centre websites were searched during June-July 2019. As traffic offender literature mostly focused on driving-under-the-influence, a systematic search of interventions to reduce alcohol use was also undertaken, yielding 21 meta-analyses and 3 additional studies. Research quality was assessed via the Maryland Scale for Scientific Rigor (individual studies) or AMSTAR2 (systematic reviews). Offender case management findings were predominantly based on violent, sex offender or mixed offender populations, but with overlapping predictive attributes to recidivist traffic offenders. Best-practice was identified as the Risk-Need-Responsivity (RNR) model, which focuses on identifying what risks the offender presents, what offender needs underlie these risks and what responsivity options, including psychology-based treatment tailored to the individual, will reduce reoffending. The focus is less on justice and deterrence models and more on respectful and otherwise ‘normal’ treatment of the individual, including addressing health and welfare needs, to address the underlying reasons for why they reoffend. More recent models were the Good Lives Model and, a hybrid of this and the RNR, the Risk-Need-Responsivity-Motivation model, which place more emphasis on boosting the individuals’ strengths to redirect them towards a ‘good life’. Traffic offender applications of case management were found only for driving-under-the-influence recidivists and associated with considerable reductions in recidivism, other convictions and prison time. Case management included a mix of individual and group-based therapies, education programs and intensive monitoring requirements, including alcohol interlocks, daily breath tests, alcohol monitoring bracelets and/or home confinement; commonly to reduce prison time, which also contributed to strong cost-benefits. Other supports included referrals to employment, education, financial, housing and legal services, and grief and domestic violence counselling. Several cognitive behavioural and motivation-based therapies showed success in reducing alcohol use, if not sobriety, offering ready options to include in case management initiatives. The review provides strong support for implementing and evaluating a case management approach for recidivist traffic offenders, with many relevant programs and services already available. Addressing wider contributing health and welfare factors could result in both improved quality of life for the individuals and reduced road trauma, potentially resulting in considerable cost-benefits. In particular, early attention to young people failing to navigate through graduated driver licensing systems or repeatedly offending has potential to prevent a lifetime of disadvantage and recidivism. Better integration of overlapping roles in government departments of transport, justice, health and human services is likely needed.

Language

  • English

Media Info

  • Media Type: Digital/other

Subject/Index Terms

Filing Info

  • Accession Number: 01764303
  • Record Type: Publication
  • Report/Paper Numbers: TRBAM-21-01082
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 23 2020 11:24AM