Low Ethan, Monsen Joshua, Schow Lindsay, Roberts Rachel, Collins Lucy, Johnson Hayden, Hanson Carl L, Snell Quinn, Tass E Shannon
Computer Science, Brigham Young University, Provo, 84602, Utah, USA.
Public Health, Brigham Young University, Provo, 84602, Utah, USA.
BMC Public Health. 2025 Jan 25;25(1):321. doi: 10.1186/s12889-025-21521-0.
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.
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.
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.
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.
欺凌行为包括身体、心理、社交或教育方面的伤害，影响着美国约二十分之一的12至18岁青少年。欺凌行为（包括网络欺凌）的流行程度和影响，需要我们更深入地了解风险因素和保护因素，以加强预防工作。本研究调查了与青少年欺凌受害最密切相关的关键风险因素和保护因素。
使用机器学习方法分析了2009年至2021年从犹他州345,506名学生受访者那里收集的学生健康与风险预防（SHARP）调查数据。该调查包括135个问题，评估人口统计学、健康结果以及青少年风险因素和保护因素。使用LightGBM创建模型，准确率达到70%，并利用SHapley加性解释（SHAP）值来解释模型预测结果，识别与欺凌受害最密切相关的风险预测因素和保护预测因素。
较低的年级、被冷落的感觉以及家庭问题（严重程度和频繁争吵、家庭成员互相侮辱以及家庭吸毒）与欺凌受害的增加密切相关——无论是在现实中还是在网络上。性别分析表明，对于男性和女性来说，家庭问题和讨厌上学的预测性最高。网络欺凌受害与饮酒早发的关联最为密切。
本研究提供了青少年欺凌受害的风险因素和保护因素概况。在人口统计学特征中确定了关键的风险因素和保护因素，研究结果强调了家庭关系、社会包容和人口统计学变量在欺凌受害中的重要作用。这些得出的风险因素和保护因素概况强调了开展针对家庭动态和社会支持的预防项目的必要性。未来的研究应扩展到不同的地理区域，并纳入纵向数据，以更好地理解因果关系。
