Predictive analysis of bullying victimization trajectory in a Chinese early adolescent cohort based on machine...

The development of bullying victimization among adolescents displays significant individual variability, with general, group-based interventions often proving insufficient for partial victims. This study aimed to conduct a machine learning-based predictive analysis of bullying victimization trajectories among Chinese early adolescents and to examine the underlying determinants.

Data were collected from 1549 students who completed three assessments of bullying victimization from 2019 to 2021. Self-reported questionnaires were used to measure bullying victimization and its associated risk and protective factors. Trajectories were classified using the Group-based Trajectory Model (GBTM), while a Random Forest algorithm was employed to develop a predictive model. Associations between baseline characteristics and victimization trajectories were evaluated via multiple logistic regression analysis.

The GBTM identified four distinct victimization trajectories, with the predictive model demonstrating adequate accuracy across these trajectories, ranging from 0.812 to 0.990. Predictors exhibited varying influences across different trajectory subgroups. Odds ratios (ORs) were notably higher in the persistent severe victimization group compared to the low victimization group (OR for adverse school experiences: 3.698 vs. 1.386; for age: 2.160 vs. 1.252; for irritability traits: 1.867 vs. 1.270). Adolescents reporting lower school satisfaction and higher borderline personality features showed a greater likelihood of persistent severe victimization, while those with lower peer satisfaction faced increased victimization over time.

The machine learning-based predictive model facilitates the identification of adolescents across different victimization trajectory groups, offering insights for designing targeted interventions. The identified risk factors are instrumental in guiding effective intervention strategies.