Screening for Light Personalities in Portugal: A Cross-Cultural Validation of the Light Triad Scale With an At-risk-of-delinquency Sample.

The Light Triad of personality refers to three prosocial personality traits-Faith in Humanity, Humanism, and Kantianism-that promote the worth and dignity of other people, focus on ethical behavior and empathy, and confidence that other people are naturally good. The aim of the present study is to examine the psychometric properties of the Light Triad Scale (LTS)-Portuguese version. Our convenience sample consisted of 242 male and female participants (M = 30.19 years, SD = 12.78, range = 16-77) from Portugal. The proposed latent structure models of the LTS obtained adequate fits. Internal consistency/reliability, as measured by the alpha and omega coefficients, was adequate to good. Construct validity with other psychometric measures (i.e., empathy, dark traits of personality, propensity to morally disengage, and antisociality/criminality measures) and criterion-related validity (with justice involvement variables such as problems with the law, arrested by the police, sentenced to prison and alcohol/drug abuse variables) were demonstrated. Cross-gender measurement invariance was established, with females scoring higher than males. The findings support the use of the LTS as a valid and reliable measure.

Towards more accurate classification of risk of arrest among offenders on community supervision: An application of machine learning versus logistic regression.

BACKGROUND: Although there is general consensus about the behavioural, clinical and sociodemographic variables that are risk factors for reoffending, optimal statistical modelling of these variables is less clear. Machine learning techniques offer an approach that may provide greater accuracy than traditional methods. AIM: To compare the performance of advanced machine learning techniques (classification trees and random forests) to logistic regression in classifying correlates of rearrest among adult probationers and parolees in the United States. METHOD: Data were from the subgroup of people on probation or parole who had taken part in the National Survey on Drug Use and Health for the years 2015-2019. We compared the performance of logistic regression, classification trees and random forests, using receiver operating characteristic curves, to examine the correlates of arrest within the past 12 months. RESULTS: We found that machine learning techniques, specifically random forests, possessed significantly greater accuracy than logistic regression in classifying correlates of arrest. CONCLUSIONS: Our findings suggest the potential for enhanced risk classification. The next step would be to develop applications for criminal justice and clinical practice to inform better support and management strategies for former offenders in the community.

Homicidal Ideation and Forensic Psychopathology: Evidence From the 2016 Nationwide Emergency Department Sample (NEDS).

Homicide is the most serious and costly criminal offense and better forensic and criminological understanding of homicidal ideation as a potential psychobehavioral precursor to homicidal conduct is critical. Using data from the 2016 Nationwide Emergency Department Sample (NEDS) from the Healthcare Cost and Utilization Project (HCUP) distributed by the Agency for Healthcare Research and Quality (AHRQ), we found 64,910 cases of homicidal ideation among a sample of 25.6 + million-a prevalence of 0.25%. Numerous conditions conferred increased substantially the likelihood of homicidal ideation including antisocial personality disorder (2406%), schizoaffective disorder (1821%), borderline personality disorder (1557%), paranoid personality disorder (1,504%), schizophrenia (1,143%), obsessive-compulsive personality disorder (921%), brief psychotic disorder (771%), unspecified psychosis (737%), avoidant personality disorder (596%), and schizoid personality disorder (571%), delusional disorder (546%), and other psychotic disorder (504%). Homicidal ideation is comorbid with serious psychiatric and behavioral problems and has important implications for offender typologies and homicidality.

Is Female Psychopathy Linked with Child Abuse? An Empirical Investigation using a Person-Centered Approach.

Childhood abuse is associated with increased psychopathic features among girls, but most prior research is based on data from correctional samples of female delinquents and less is known about how specific forms of childhood abuse affect specific features of psychopathy. Using a school-based community sample of 696 girls aged 9-17 years from Barbados and Grenada, the current study examined latent profiles of psychopathic personality traits and their associations with physical, emotional, and sexual abuse. Latent profile analysis (LPA) revealed four distinct psychopathy groups among girls, including a 'low psychopathy' group (41.9% of girls), 'high psychopathy' group (4.8%), 'high interpersonal manipulation and egocentricity' group (37.4%), and a 'moderate psychopathy' group (16%). There was considerable evidence of physical abuse, emotional abuse, and sexual abuse among participants. Sexual abuse was associated with a 116% increased likelihood of membership in the high psychopathy group and a 57% increased likelihood of membership in the high interpersonal manipulation and egocentricity group. These results indicate that sexual abuse is a powerful distal factor in the development of psychopathic personality functioning, especially more severe variants.

Toward a Bioecological Model of School Engagement: A Biometric Analysis of Gene and Environmental Factors.

School disengagement is associated with poor academic achievement, dropout, and risk behaviors such as truancy, delinquency, and substance use. Despite empirical research identifying risk correlates of school disengagement across the ecology, it is unclear from which domain these correlates arise. To redress this issue, the current study used intraclass correlation and DeFries-Fulker analyses to longitudinally decompose variance in three domains of engagement (academic, behavioral, and emotional) using data from the National Longitudinal Study of Adolescent Health. Findings suggest that nonshared environmental factors (that is, environmental contexts and experiences that are unique to each sibling) account for approximately half of the variance in indicators of school disengagement when controlling for genetic influences, and that this variance increases as adolescents grow older and rely less on their immediate family. The present study contributes new evidence on the biosocial underpinnings of school engagement and highlights the importance of interventions targeting factors in the nonshared environment.

Does childhood sexual abuse victimization translate into juvenile sexual offending? New evidence.

The cycle of violence thesis posits that early exposure to maltreatment increases the likelihood of later maladaptive and antisocial behaviors. Childhood sexual abuse (CSA) specifically has been shown to increase the likelihood of sexual offending, although less is known about its linkages to other forms of crime. Based on data from 2,520 incarcerated male juvenile offenders from a large southern state, hierarchical logistic regression models suggested that CSA increased the likelihood of later sexual offending nearly sixfold (467% increase). However, CSA was associated with an 83% reduced likelihood of homicide offending and 68% reduced likelihood of serious person/property offending. These findings suggest further support for the cycle of violence where CSA promotes sexual offending but novel findings regarding the linkages between CSA and other forms of crime.