Officer/staff attitudes. Officer/staff practices. Offender attitudes. Risk reduction. Recidivism

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2 Officer/staff attitudes Officer/staff practices Offender attitudes Risk reduction Recidivism

3 Views, ideas opinions regarding: Use of punishment Use of case planning Use of various assessments Risk Need Responsivity Use of rehabilitation Belief in offender change Job satisfaction

4 Content of case plan Criminogenic targets Use of case plan Extent to which it is a living document Extent to which it reflects dynamic change in offender Inclusion of assessment information

5 Use of assessment instruments Accuracy Level of confidence in utility Use of overrides if applicable Content of officer/offender interactions Primary emphasis on criminogenic needs/targets? Tone/tenor of officer/offender interactions

6 Views toward supervision Views toward engagement in treatment Views toward fine & restitution obligation Views toward officer/staff Self-reflection regarding their desire and belief in success Engagement in meeting with officer, promptness, responsiveness, etc.

7 As assessed by risk assessments and need assessments Questions to ask: What are we using, why are we using it, and are these instruments sensitive enough? Static vs. Dynamic predictors Predictive validity of assessments (does high, medium and low have real meaning and utility)? Can these instruments really gauge change over time, and with intervention? Are the majority of the items we measure DYNAMIC?

8 If the instruments we use have predictive validity And if the instruments we use are dynamic in terms of what they measure and how its measured Then with these instruments we should be able to measure real change real risk reduction regarding recidivism over time, and with the right interventions.

9 Case outcome Type of termination (i.e., rates of revocation/failure) Technical violations Dirty UA s New arrest New conviction (New) incarceration Every offender that remains in community means potentially big savings for any state corr. system

10 Two sites: Maricopa County, Arizona Orange County, California Examining outcomes from two cohorts at each site Pre-EBP implementation Post-EBP implementation

11 For each site: Demographic data Assessment data (risk & need information) Case outcome and/or Recidivism data One of a handful of objectives: Compare outcomes pre- and post-implementation of EBP

12 PRE-EBP IMPLEMENTATION (N=1411) POST-EBP IMPLEMENTATION (N=1386) Avg. age: 32 years 54% white 77% male Avg. age years 49% white 77% male 27% Low risk 73% Medium risk* 23% Low risk 77% Medium risk* *Due to changes in assessment processes, two forms of assessment were combined. As such, risk levels should be interpreted with caution

13 PRE-EBP IMPLEMENTATION 69% terminated successfully POST-EBP IMPLEMENTATION 76% terminated successfully 47% had at least one petition to revoke filed during supervision 41% had at least one petition to revoke filed during supervision These differences were statistically significant at p <.001. The differences observed were unlikely to have occurred simply by chance alone

14 Cohort year as a variable was a significant (p <.001) predictor in bivariate models i.e., being in the Post-EBP cohort was significantly related to success, relative to the Pre-EBP cohort Cohort year remained statistically significant when controlling for age, race, gender, instant offense, supervision level, risk level, and time under supervision

15 PRE-EBP IMPLEMENTATION (N=2762) POST-EBP IMPLEMENTATION (N=2139) Avg. age: 30 years 41% white; 45% Hisp. 78% male Person-offense 16% High risk level 66% High need level 26% Avg. age: 30 years 43% white; 40% Hisp. 76% male Person-offense 16% High risk level 92% High need level 30%

16 PRE-EBP IMPLEMENTATION POST-EBP IMPLEMENTATION New offense (any) 18% New offense (any) 11% New offense (fel) 18% New offense (fel) 10% Avg. 1 st reass: 0.26 Avg. 1 st reass: 0.16

17 Restructuring datafile in order to utilize cohort membership as predictor, for Orange County Exploring change in need score over time (comparing pre- and post-ebp) for Orange County Obtaining recidivism (e.g., new arrest) data (Maricopa county) to supplement case outcomes Additional multivariate models for both sites

18 Utilize longer and standardized follow-up period Utilize several measures of outcome Including arrest, conviction, type of offense, (re)- incarceration Change in dynamic risk/need assessment instruments Track offender and officer data as well Attitudes, views