Evaluating Reentry Programs Using Data and Science

JOHN WETZEL: We’re fortunate in Pennsylvania that we have a lot of programs. We’re also fortunate that we have a great research shop so we measure programs all the time. From drug and alcohol to trauma-type programs to cognitive behavior programs to vocational programs, we have a full myriad of programs and we measure it.

GRANT DUWE: The Minnesota DOC uses data and science to make a number of decisions relating to the prisoner population. It uses validated risk and needs assessments to make decisions pertaining to who should the DOC prioritize for programming, which individuals should be placed on varying levels of post-release supervision. It also uses data and science to identify the types of programs that the that the agency delivers to inmates, relying on what the literature has indicated as to what’s effective and what’s not effective in terms of providing that programming. The programs that the Minnesota DOC provides that have shown to be most effective in producing good outcomes for the prisoner population have generally been those that-- that target known risk factors for recidivism; those risk factors being things like criminal thinking, anti-social peers, substance abuse, education, employment. So the programs that target these known risk factors have generally been effective in reducing recidivism and producing good employment outcomes.