基于机器学习的预测模型有助于识别青少年欺凌受害的发展轨迹，为针对性干预提供指导

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

基于机器学习的中国早期青少年欺凌受害轨迹的预测分析

Author: Xue Wen, Ting Tang, Xinhui Wang, Yingying Tong, Dongxue Zhu, Fan Wang, Han Ding, Puyu Su, Gengfu Wang

Source: Journal of Affective Disorders

DOI: 10.1016/j.jad.2024.11.057

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

摘要

背景：青少年欺凌行为的发展具有显著的个体差异性，基于群体的普遍干预措施往往不足以解决部分受害者的问题。本研究旨在利用机器学习模型对中国青少年受欺凌的轨迹进行预测，并研究其潜在的决定因素。

方法：本研究纳入1549名初中生，参与者在2019年至2021年期间完成了三次欺凌受害评估。问卷调查包括欺凌受害情况及其相关风险和保护因素。使用基于群体的轨迹模型（GBTM）对轨迹进行分类，同时采用随机森林算法预测欺凌受害轨迹并探究重要预测因子。运用多元逻辑回归分析评估了重要预测因子与受害轨迹之间的关联。

结果：GBTM 确定了四种不同的受害轨迹，预测模型在这些轨迹中表现出较高的准确性，准确率在0.812到0.990之间。在不同的轨迹亚组中，预测因子表现出不同的影响。与受害程度低的群体相比，持续严重受害群体的比率（ORs）明显更高（不良学校经历的OR为 3.698 vs. 1.698；受害者年龄：2.160 vs. 1.252；易怒特征：1.867 vs. 1.270）。报告学校满意度较低和边缘型人格特征较高的青少年更有可能持续受到严重伤害，而同伴满意度较低的青少年随着时间的推移受害程度会增加。

结论：基于机器学习的预测模型有助于识别不同受害轨迹群体的青少年，为设计有针对性的干预措施提供启示。识别出的风险因素可用于指导干预策略的制定。