Objective: In community-based corrections, reassessment of dynamic risk factors improves the prediction of recidivism relative to initial risk assessment at the time of release. However, there is less evidence for predictions of violent recidivism. We examined whether reassessment proximity or aggregation of reassessments improved the prediction of imminent violence in a sample of paroled individuals on community supervision. Hypotheses: We hypothesized that reassessment of dynamic risk would better predict violent recidivism than initial risk assessment at the time of release. Examination of aggregation and individual risk-factor domains was exploratory. Method: In a prospective study of violent recidivism in a sample of individuals on community supervision in New Zealand (75,917 assessments from 3,421 participants; 92.8% men), we used supervision officers’ ratings of dynamic risk (assessed using Dynamic Risk Assessment for Offender Re-entry [DRAOR]) and static risk scores (using the Risk of ReConviction × Risk of Imprisonment) to predict imminent violence (within 2 weeks). Results: Individuals who recidivated violently had higher initial risk ratings (DRAOR Stable d = 0.36, 95% CI [0.17, 0.55]; DRAOR Acute d = 0.45, 95% CI [0.26, 0.64]) and showed more week-to-week fluctuations in risk ratings (DRAOR Stable d = 0.21, 95% CI [0.04, 0.41]; DRAOR Acute d = 0.26, 95% CI [0.06,0.46]). Total averages of faster-changing acute risk factors best predicted violence (c-index = 0.68), with changes in these factors incrementally predicting violence over well-established predictors (criminal history) and initial scores (Δχ2 = 15.54, df = 3). The constructs that best discriminated violence were consistent with social cognition explanations of violence. Conclusions: Because client consistency as determined through score aggregation was more important than current presentation, supervision officers should consider overall patterns of interpersonal hostility and reactivity rather than assuming the emerging presence of these factors will signal imminent violence among previously violent individuals.

The complex system conception of group social dynamics often involves not only changing individual characteristics, but also changing within-group relationships. Recent advances in stochastic dynamic network modeling allow these interdependencies to be modeled from data. This methodology is discussed within a context of other mathematical and statistical approaches that have been or could be applied to study the temporal evolution of relationships and behaviors within small- to medium-sized groups. An example model is presented, based on a pilot study of five Oxford House recovery homes, sober living environments for individuals following release from acute substance abuse treatment. This model demonstrates how dynamic network modeling can be applied to such systems, examines and discusses several options for pooling, and shows how results are interpreted in line with complex system concepts. Results suggest that this approach (a) is a credible modeling framework for studying group dynamics even with limited data, (b) improves upon the most common alternatives, and (c) is especially well-suited to complex system conceptions. Continuing improvements in stochastic models and associated software may finally lead to mainstream use of these techniques for the study of group dynamics, a shift already occurring in related fields of behavioral science.

Those who study treatment and recovery from alcohol use disorder (AUD) and substance use disorder (SUD) generally agree that an individual’s social context impacts his or her success (or failure) in recovery. Recently, as the use of social network analysis has increased, studies on SUD recovery and treatment have adopted ego networks as a research tool. This review aims to tie together a thread of research for an efficient and effective summary. We selected peer-reviewed articles on individuals receiving treatment an intervention for SUD or AUD that used ego network measures of individual social networks. Ego networks have been studied as treatment outcomes, predictors of treatment outcomes in general, and how an individual’s ego network might be used to predict what specific treatment is most likely to succeed. We discuss relevant findings of studies using ego networks, the strengths and weaknesses of ego network approaches, and how future studies may benefit from the use of ego networks.

Objective Few studies consider the retention of the individuals (alters) comprising the social networks of people in recovery. We conducted a longitudinal study exploring several possible factors predicting whether alters were retained six months after participants completed treatment. Method The Important Person Inventory was given to 270 ex-offenders (224 men, 46 women) transitioning from treatment to Oxford House residences, Safe Haven therapeutic communities, or to usual aftercare. A 6-month follow-up was completed by 176 participants (137 men, 39 women). Results We found that alters who were related to the participant, did not use drugs, were embedded in smaller networks, and had more frequent contact with the participant were significantly more likely to be retained as important people over 6 months, but found no effects based on alters’ drinking or criminal history. Conclusions Certain characteristics of important people are related to their retention in a social network. Understanding these relationships is essential for creating effective social interventions for addictions.

Objective: Clinicians often rely on readily observable intermediate outcomes (e.g., symptoms) to assess the likelihood of events that occur outside of treatment (e.g., relapse). Similarly, those monitoring clients with histories of criminal involvement attempt to prevent adverse outcomes considered likely and intervene when symptoms/risk factors fluctuate. Our aim was to develop a stronger understanding of associations between evolving symptoms/risk factors and case outcomes, yielding clearer practice implications. Method: We used longitudinal, multiple reassessment risk data from 3,421 individuals paroled in New Zealand. We used joint modeling to test the association between individual trajectories of psychosocial risk factor scores, assessed using Dynamic Risk Assessment for Offender Re-entry, and recidivism (official records of parole violations or criminal charges resulting in reconviction). We examined whether recent clinically relevant features of risk presentation (e.g., current levels, recent rate of change) predicted recidivism better than the entirety of the risk assessment trajectory. Results: Although each model demonstrated similar predictive validity, measures of model fit indicated that models using current trajectory features outperformed those using the entire assessment history to predict recidivism. Conclusions: Change in dynamic risk factors is consistently associated with recidivism outcomes. When using changeable factors to monitor clients’ current risk for recidivism, practitioners should focus on current presentation rather than the entire assessment history, although differences in predictive discrimination are small.