Predicting bullying victimization among adolescents using the risk and protective factor framework: a large-scale machine learning approach.

BACKGROUND

Bullying, encompassing physical, psychological, social, or educational harm, affects approximately 1 in 20 United States teens aged 12-18. The prevalence and impact of bullying, including online bullying, necessitate a deeper understanding of risk and protective factors to enhance prevention efforts. This study investigated the key risk and protective factors most highly associated with adolescent bullying victimization.

METHODS

Data from the Student Health and Risk Prevention (SHARP) survey, collected from 345,506 student respondents in Utah from 2009 to 2021, were analyzed using a machine learning approach. The survey included 135 questions assessing demographics, health outcomes, and adolescent risk and protective factors. LightGBM was used to create the model, achieving 70% accuracy, and SHapley Additive exPlanations (SHAP) values were utilized to interpret model predictions and to identify risk and protective predictors most highly associated with bullying victimization.

RESULTS

Younger grade levels, feeling left out, and family issues (severity and frequent arguments, family member insulting each other, and family drug use) are strongly associated with increased bullying victimization - whether in person or online. Gender analysis showed that for male and females, family issues and hating school were most highly predictive. Online bullying victimization was most highly associated with early onset of drinking.

CONCLUSIONS

This study provides a risk and protective factor profile for adolescent bullying victimization. Key risk and protective factors were identified across demographics with findings underscoring the important role of family relationships, social inclusion, and demographic variables in bullying victimization. These resulting risk and protective factor profiles emphasize the need for prevention programming that addresses family dynamics and social support. Future research should expand to diverse geographical areas and include longitudinal data to better understand causal relationships.