Mental Health, Well-Being, and Adolescent Extremism: A Machine Learning Study on Risk and Protective Factors
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Mental Health, Well-Being, and Adolescent Extremism : A Machine Learning Study on Risk and Protective Factors. / Haghish, E. F.; Obaidi, Milan; Strømme, Thea; Bjørgo, Tore; Grønnerød, Cato.
I: Research on Child and Adolescent Psychopathology, Bind 51, 03.08.2023, s. 1699-1714.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Mental Health, Well-Being, and Adolescent Extremism
T2 - A Machine Learning Study on Risk and Protective Factors
AU - Haghish, E. F.
AU - Obaidi, Milan
AU - Strømme, Thea
AU - Bjørgo, Tore
AU - Grønnerød, Cato
PY - 2023/8/3
Y1 - 2023/8/3
N2 - 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.
AB - 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.
U2 - 10.1007/s10802-023-01105-5
DO - 10.1007/s10802-023-01105-5
M3 - Journal article
C2 - 37535227
VL - 51
SP - 1699
EP - 1714
JO - Research on Child and Adolescent Psychopathology
JF - Research on Child and Adolescent Psychopathology
SN - 2730-7166
ER -
ID: 364545429