Procedural Justice Versus Risk Factors for Offending: Predicting Recidivism in Youth

Theories of procedural justice suggest that individuals who experience respectful and fair legal decision-making procedures are more likely to believe in the legitimacy of the law, and, in turn, are less likely to reoffend. However, few studies have examined these relationships in youth. To begin to fill this gap in the literature, in the current study the authors studied 92 youth (67 male, 25 female) on probation regarding their perceptions of procedural justice and legitimacy, and then monitored their offending over the subsequent six months. Results indicated that perceptions of procedural justice predicted self-reported offending at three months but not at six months, and that youths’ beliefs about the legitimacy of the law did not mediate this relationship. Furthermore, procedural justice continued to account for unique variance in self-reported offending over and above the predictive power of well-established risk factors for offending (i.e., peer delinquency, substance abuse, psychopathy, and age at first contact with the law). Theoretically, the current study provides evidence that models of procedural justice developed for adults are only partially replicated in a sample of youth; practically, this research suggests that by treating adolescents in a fair and just manner, justice professionals may be able to reduce the likelihood that adolescents will reoffend, at least in the short term.&nbsp

Reining in Punitive Discipline: Recent Trends in Exclusionary School Discipline Disparities

Concerns around disparities in suspensions and expulsions from schools in the United States have resulted in a concerted effort to reduce the use of exclusionary school discipline. In this article, the authors describe trends in the use of exclusionary discipline in Indiana and Oregon, two U.S. states with different school discipline policy climates. The findings point to a substantial decline in the use of suspensions and other forms of exclusionary discipline in both states. The authors further find that racial and socioeconomic disparities have recently narrowed in both states, though Black students and students who were identified as economically disadvantaged remain likely to be disproportionately exposed to exclusionary discipline. These trends, and their timing, illustrate the broad-based change in disciplinary norms that has occurred in the U.S. over the past decade

A Machine Learning Algorithm to Identify Patients at Risk of Unplanned Subsequent Surgery After Intramedullary Nailing for Tibial Shaft Fractures

Objectives: In the SPRINT trial, 18% of patients with a tibial shaft fracture (TSF) treated with intramedullary nailing (IMN) had one or more unplanned subsequent surgical procedures. It is clinically relevant for surgeon and patient to anticipate unplanned secondary procedures, other than operations that can be readily expected such as reconstructive procedures for soft tissue defects. Therefore, the objective of this study was to develop a machine learning (ML) prediction model using the SPRINT data that can give individual patients and their care team an estimate of their particular probability of an unplanned second surgery. Methods: Patients from the SPRINT trial with unilateral TSFs were randomly divided into a training set (80%) and test set (20%). Five ML algorithms were trained in recognizing patterns associated with subsequent surgery in the training set based on a subset of variables identified by random forest algorithms. Performance of each ML algorithm was evaluated and compared based on (1) area under the ROC curve, (2) calibration slope and intercept, and (3) the Brier score. Results: Total data set comprised 1198 patients, of whom 214 patients (18%) underwent subsequent surgery. Seven variables were used to train ML algorithms: (1) Gustilo-Anderson classification, (2) Tscherne classification, (3) fracture location, (4) fracture gap, (5) polytrauma, (6) injury mechanism, and (7) OTA/AO classification. The best-performing ML algorithm had an area under the ROC curve, calibration slope, calibration intercept, and the Brier score of 0.766, 0.954, -0.002, and 0.120 in the training set and 0.773, 0.922, 0, and 0.119 in the test set, respectively. Conclusions: An ML algorithm was developed to predict the probability of subsequent surgery after IMN for TSFs. This ML algorithm may assist surgeons to inform patients about the probability of subsequent surgery and might help to identify patients who need a different perioperative plan or a more intensive approach.Orthopaedics, Trauma Surgery and Rehabilitatio