ArticleBMC public health2025
Predicting bullying victimization among adolescents using the risk and protective factor framework: a large-scale machine learning approach.
Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Does Tooth Loss Lead to School Bullying? Evidence from the Longitudinal Study of Australian Children.JDR clinical and translational research · 2026Article
- Peer Victimization and Psychological Outcomes in Adolescents with Pubertal Gynecomastia: A Case-Control StudyJournal of clinical research in pediatric endocrinology · 2026Article
- When more is not always better: exercise dose and children's cognitive development from an interpretable machine learning perspective.Frontiers in psychiatry · 2026Article
- Child Maltreatment and the Mediating Effects of Bullying Victimization on School Engagement Among South Korean Youth in Orphanages and a National Sample.Children (Basel, Switzerland) · 2025Article
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9 authors.
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Abstract
backgroundBullying, 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.
methodsData 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.
resultsYounger 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.
conclusionsThis 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.
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