Identifying multilevel predictors of trajectories of psychopathology and resilience among juvenile offenders: A machine learning approach.

Autor: Liu H; Department of Special Education and Counselling, The Education University of Hong Kong, Hong Kong SAR, China.; Centre for Psychosocial Health, The Education University of Hong Kong, Hong Kong SAR, China., Hou WK; Centre for Psychosocial Health, The Education University of Hong Kong, Hong Kong SAR, China.; Department of Psychology, The Education University of Hong Kong, Hong Kong SAR, China., Lau EYY; Centre for Psychosocial Health, The Education University of Hong Kong, Hong Kong SAR, China.; Department of Psychology, The Education University of Hong Kong, Hong Kong SAR, China., Birk JL; Center for Behavioral Cardiovascular Health (CBCH), Columbia University Irving Medical Center, New York, NY, USA., Bonanno GA; Department of Counseling and Clinical Psychology, Teachers College, Columbia University, New York, NY, USA.
Jazyk: angličtina
Zdroj: Development and psychopathology [Dev Psychopathol] 2024 Aug; Vol. 36 (3), pp. 1503-1519. Date of Electronic Publication: 2023 Aug 22.
DOI: 10.1017/S0954579423000755
Abstrakt: Mental ill health is more common among juvenile offenders relative to adolescents in general. Little is known about individual differences in their long-term psychological adaptation and its predictors from multiple aspects of their life. This study aims to identify heterogeneous trajectories of probable psychiatric conditions and their predictors. Participants included 574 juvenile offenders who were first convicted for serious crimes and without detention history. The participants were assessed at 11 timepoints over seven years (2000-2010). Growth mixture modeling revealed the same three trajectories for both probable anxiety and probable depression: stable low trajectory (75.96%; 75.78%), stable high trajectory (15.16%; 10.98%), and recovery (8.89%, 13.24%). Least absolute shrinkage and selection operator (LASSO) logistic regression identified three multilevel predictors for memberships of different trajectories. Risk factors against stable low trajectory lay within personal (e.g., neuroticism), relationship (e.g., parental hostility), and contextual levels (e.g., chaotic neighborhood). Resilience factors for stable low trajectory included strong work orientation and low education level of father. Recovery was predicted by Black race, self-identity, high education level of father, and nonincarcerated sentencing. Our findings suggest that both psychopathology and psychological resilience could be predicted by multiple personal, relationship, and contextual factors in the social ecology of juvenile offenders.
Databáze: MEDLINE