Relationship between expressions of aggression and individual characteristics in correctional settings: A path analysis

The Predictive Value of Criminogenic Needs on Institutional Misconduct in a Dutch Forensic Hospital

According to the Risk-Need-Responsivity model, criminogenic needs are important in predicting violent behavior. Eight criminogenic needs are considered strong predictors: history of antisocial behavior, antisocial personality traits, criminal attitudes, criminal associates, substance abuse, family problems, poor work performance, and lack of involvement in prosocial leisure/recreation activities. The purpose of the current study was to examine whether seven criminogenic needs predict institutional misconduct in the first year of admission of Dutch patients who were admitted to a forensic hospital. Hospital records of 234 male patients were used to retrieve criminogenic needs assessed with the HCR-20V3, with exception of criminal associates which was coded based on file review. The frequency of institutional misconduct was rated for verbal aggression, physical aggression, and sexual aggression. Exploratory analyses examined whether there was a (predictive) relation between psychopathy facets (PCL-R factors) and HCR-20V3 clinical and risk management scales, and institutional misconduct. This study finds that criminogenic needs did not predict physical and sexual institutional misconduct, but a history of antisocial behavior and criminal attitudes were negative predictors for verbal aggression. Psychopathy facets and HCR-20V3 scales did not predict institutional misconduct.

Offenders and non-offenders with schizophrenia spectrum disorders: Do they really differ in known risk factors for aggression?

Introduction

Individuals with schizophrenia spectrum disorders (SSD) have an elevated risk for aggressive behavior, and several factors contributing to this risk have been identified, e. g. comorbid substance use disorders. From this knowledge, it could be inferred that offender patients show a higher expression of said risk factors than non-offender patients. Yet, there is a lack of comparative studies between those two groups, and findings gathered from one of the two are not directly applicable to the other due to numerous structural differences. The aim of this study therefore was to identify key differences in offender patients and non-offender patients regarding aggressive behavior through application of supervised machine learning, and to quantify the performance of the model.

Methods

For this purpose, we applied seven different (ML) algorithms on a dataset comprising 370 offender patients and a comparison group of 370 non-offender patients, both with a schizophrenia spectrum disorder.

Results

With a balanced accuracy of 79.9%, an AUC of 0.87, a sensitivity of 77.3% and a specificity of 82.5%, gradient boosting emerged as best performing model and was able to correctly identify offender patients in over 4/5 the cases. Out of 69 possible predictor variables, the following emerged as the ones with the most indicative power in distinguishing between the two groups: olanzapine equivalent dose at the time of discharge from the referenced hospitalization, failures during temporary leave, being born outside of Switzerland, lack of compulsory school graduation, out- and inpatient treatment(s) prior to the referenced hospitalization, physical or neurological illness as well as medication compliance.

Discussion

Interestingly, both factors related to psychopathology and to the frequency and expression of aggression itself did not yield a high indicative power in the interplay of variables, thus suggesting that while they individually contribute to aggression as a negative outcome, they are compensable through certain interventions. The findings contribute to our understanding of differences between offenders and non-offenders with SSD, showing that previously described risk factors of aggression may be counteracted through sufficient treatment and integration in the mental health care system.