基于机器学习的中国早期青少年队伍中的欺凌受害轨迹的预测分析
Xue Wen1, Ting Tang1, Xinhui Wang1
1Department of Maternal, Child and Adolescent Health, School of Public Health, Anhui Medical University, No.81 Meishan Road, Hefei 230032, Anhui, China.
Journal of affective disorders
|November 22, 2024
概括
机器学习准确地预测了中国青少年的欺凌轨迹. 学校满意度和个性特征等关键因素指导针对持续严重受害的有针对性的干预措施.
科学领域:
- 青少年心理学 青少年心理学
- 机器学习应用 机器学习应用
- 公共卫生 公共卫生
背景情况:
- 欺凌受害者表现出个体的变化,挑战一般干预措施.
- 部分受害者需要量身定制的方法,因为总体策略不足.
- 了解中国早期青少年的欺凌模式至关重要.
研究的目的:
- 执行基于机器学习的欺凌受害轨迹的预测分析.
- 为了确定欺凌受害的潜在决定因素.
- 为青少年提供有针对性的干预策略.
主要方法:
- 在三个评估 (2019-2021) 中收集了1549名中国早期青少年的数据.
- 使用基于组的轨迹模型 (GBTM) 进行轨迹分类.
- 采用随机森林算法用于预测建模和分析后勤回归.
主要成果:
- 确定了四种不同的欺凌受害轨迹,具有高预测准确度 (0.812-0.990).
- 不良的学校经历,年龄和易怒的特征显著预测了持续的严重受害.
- 较低的学校满意度和较高的边界人格特征增加了风险;较低的同行满意度与随着时间的推移增加的受害度相关.
结论:
- 机器学习模型有效地识别了不同受害轨迹中的青少年.
- 确定的风险因素为设计有针对性的欺凌干预提供了关键的见解.
- 个性化策略对于解决各种欺凌受害者模式至关重要.
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