Straight from the Source: Using Client Strengths and Risks to Predict Future Supervision Violations

The number of individuals convicted of crime in the United States is large, and re-conviction rates among these individuals is even higher. Criminal conviction and recidivism rates are concerning for a variety of reasons, most of which are related to the various deleterious outcomes found to be associated with criminal justice involvement. The extant literature on factors related to recidivism focus primarily on unalterable risk factors and alterable risk factors; however, there is still a dearth of research on which alterable strength factors are associated with recidivism, and how patterns of risks and strengths relate to recidivism. The present research addresses some of these gaps by investigating: (a) if established strengths-based internal assets scales are internally reliable for use with criminal justice-involved populations; (b) if classes of clients can be ascertained on a number of alterable strengths (i.e., twelve-step support, self-efficacy, cognitive reappraisal), alterable risks (i.e., difficulty with: transportation, housing, employment, substance use), and an unalterable risk measure (i.e., a standardized risk assessment score derived from prior convictions and personal history; Recidivism Risk from the COMPAS); (c) if ethnicity functions as a significant covariate between emerging classes; and (d) if the emerging classes significantly predict recidivism (i.e., supervision violations) within three-months post-completion of the initial survey. The initial sample for the reliability analyses consisted of N = 333 clients serving time under community supervision at a probation agency in California, due to primarily substance-related convictions. Clients were all identified as male, 58% were identified as Hispanic and 42% White, M = 39 years old. After variables were selected for use in the LCA and observations with missing data were eliminated, the final sample utilized in the LCA was N = 262. The internal reliability analyses revealed very high internal reliability statistics (α = .91 to .95) on the internal asset scales examined (self-efficacy, self-awareness, cognitive reappraisal, self-regulation). This is an important contribution due to the limited number of studies that examine strengths-based and internal asset scales with criminal justice-involved populations; future research would benefit from continuing to explore these measures and their utility with this population. Next, the LCA analyses revealed strong fit indices for a three-class model that was delineated as representing: Low Strengths, High Risks (45%); High Strengths, Low Risks (29%), and Very Low Strengths, Low Risks (26%). Of note was that the unalterable risk factor (i.e., Recidivism Risk) was not a notable factor in distinguishing class membership. In the covariate analysis, class membership was not found to be divergent by ethnicity. When recidivism (i.e., acquisition of supervision violations within three months of post-completion of the survey) was added as a distal outcome to the three-class solution, significant differences between the three classes emerged on the probability of whether or not an individual was likely to acquire supervision violations. Specifically, the Low Strengths, High Risks population was significantly more likely to acquire supervision violations than the other two classes of clients (High Strengths, Low Risks; Very Low Strengths, Low Risks). The research suggests that clients may be able to be screened by use of alterable risk and alterable strengths in preventing or identifying propensity toward recidivism. Based on the lack of discriminant ability of the unalterable risk factor (based largely on criminal history), it is unclear now what the utility of an unalterable measure may be in such a screening tool. The use of LCA in this study provides an innovative way to bridge research into practice, in that practitioners and individuals in direct contact with similar criminal justice-involved clients could utilize results of these analyses to develop client profiles to intervene or provide additional supports to clients, in an effort to prevent recidivism. This research has important implications due to the various gaps in the literature; the potential for this research to be immediately useful and applicable for practitioners, policymakers, and researchers; and the general lack of strengths-based approaches used with criminal-justice involved populations that may help to better understand factors related to recidivism, and thus help deter the various negative outcomes that are associated with continued criminal justice involvement

School Sense of Community, Teacher Support, and Students\u2019 School Safety Perceptions

This study examined the association between two characteristics of school climate (sense of community and teacher support, measured both at the individual and at the school level) and students\u2019 feelings of being unsafe at school. The study involved a sample of 49,638 students aged 10\u201318 years who participated in the 2010\u20132012 California Healthy Kids Survey. Using hierarchical linear modeling (HLM), our findings revealed that, at the individual level, students perceiving higher levels of sense of community and teacher support at school were less likely to feel unsafe within the school environment. At the school level, sense of community was negatively associated with unsafe feelings, whereas there was no association between school-level teacher support and feelings of being unsafe at school