Department of Psychology, University of Oslo, Oslo, Norway.
Department of Psychology, Copenhagen University, Copenhagen, Denmark.
Res Child Adolesc Psychopathol. 2023 Nov;51(11):1699-1714. doi: 10.1007/s10802-023-01105-5. Epub 2023 Aug 3.
We examined the relationship between adolescents' extremist attitudes with a multitude of mental health, well-being, psycho-social, environmental, and lifestyle variables, using state-of-the-art machine learning procedure and nationally representative survey dataset of Norwegian adolescents (N = 11,397). Three key research questions were addressed: 1) can adolescents with extremist attitudes be distinguished from those without, using psycho-socio-environmental survey items, 2) what are the most important predictors of adolescents' extremist attitudes, and 3) whether the identified predictors correspond to specific latent factorial structures? Of the total sample, 17.6% showed elevated levels of extremist attitudes. The prevalence was significantly higher among boys and younger adolescents than girls and older adolescents, respectively. The machine learning model reached an AUC of 76.7%, with an equal sensitivity and specificity of 70.5% in the test dataset, demonstrating a satisfactory performance for the model. Items reflecting on positive parenting, quality of relationships with parents and peers, externalizing behavior, and well-being emerged as significant predictors of extremism. Exploratory factor analysis partially supported the suggested latent clusters. Out of the 550 psycho-socio-environmental variables analyzed, behavioral problems, individual and social well-being, along with basic needs such as a secure family environment and interpersonal relationships with parents and peers emerged as significant factors contributing to susceptibility to extremism among adolescents.
我们使用最先进的机器学习程序和挪威青少年的全国代表性调查数据集（N=11397），研究了青少年极端态度与心理健康、幸福感、心理社会、环境和生活方式等多种变量之间的关系。提出了三个关键研究问题：1）能否使用心理社会环境调查项目将具有极端态度的青少年与没有极端态度的青少年区分开来；2）青少年极端态度的最重要预测因素是什么；3）所确定的预测因素是否对应于特定的潜在因子结构？在总样本中，有 17.6%的青少年表现出较高水平的极端态度。与女孩和年龄较大的青少年相比，男孩和年龄较小的青少年的患病率明显更高。该机器学习模型在测试数据集中的 AUC 达到 76.7%，其灵敏度和特异性均为 70.5%，表明该模型表现良好。反映积极养育、与父母和同伴关系质量、外化行为和幸福感的项目被证明是极端主义的重要预测因素。探索性因素分析部分支持了所建议的潜在聚类。在分析的 550 个心理社会环境变量中，行为问题、个人和社会幸福感，以及诸如安全的家庭环境和与父母及同伴的人际关系等基本需求，被确定为导致青少年易受极端主义影响的重要因素。
