Quantifying the Importance of Non-Suicidal Self-Injury Characteristics in Predicting Different Clinical Outcomes: Using Random Forest Model.
Existing research on non-suicidal self-injury (NSSI) among adolescents has primarily concentrated on general risk factors, leaving a significant gap in understanding the specific NSSI characteristics that predict diverse psychopathological outcomes. This study aims to address this gap by using Rando...
| Published in: | Journal of Youth & Adolescence Vol. 53; no. 7; pp. 1615 - 1630 |
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| Main Authors: | , , , , , , |
| Format: | Article |
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Springer Nature
Jul2024
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=177538232&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 177538232 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00472891 JYA jtl: Journal of Youth & Adolescence issn: 00472891 maglogo: N pubinfo: dt: Jul2024 vid: 53 iid: 7 pid: 237 pub: Springer Nature artinfo: ui: 177538232 10.1007/s10964-023-01926-z ppf: 1615 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P size: 902KB tig: atl: Quantifying the Importance of Non-Suicidal Self-Injury Characteristics in Predicting Different Clinical Outcomes: Using Random Forest Model. aug: au: Wang, Zhenhai Chen, Yanrong Tao, Zhiyuan Yang, Maomei Li, Dongjie Jiang, Liyun Zhang, Wei affil: https://ror.org/01kq0pv72 Center for Studies of Psychological Application, School of Psychology, South China Normal University, Guangzhou, China Tangxia No.2 Junior High School, Dongguan, Guangdong, China su: Suicidal ideation Anxiety Self-mutilation Teenagers' conduct of life Mental depression Psychosocial factors Random forest algorithms Prediction models T-test (Statistics) Research funding Questionnaires Treatment effectiveness Chi-squared test Longitudinal method Research Data analysis software Algorithms sug: subj: Suicidal ideation Anxiety Self-mutilation Teenagers' conduct of life Mental depression Psychosocial factors Random forest algorithms Prediction models T-test (Statistics) Research funding Questionnaires Treatment effectiveness Chi-squared test Longitudinal method Research Data analysis software Algorithms keyword: Data mining Depression Non-suicidal self-injury Random forest Suicide Data mining Depression Non-suicidal self-injury Random forest Suicide ab: Existing research on non-suicidal self-injury (NSSI) among adolescents has primarily concentrated on general risk factors, leaving a significant gap in understanding the specific NSSI characteristics that predict diverse psychopathological outcomes. This study aims to address this gap by using Random Forests to discern the significant predictors of different clinical outcomes. The study tracked 348 adolescents (64.7% girls; mean age = 13.31, SD = 0.91) over 6 months. Initially, 46 characteristics of NSSI were evaluated for their potential to predict the repetition of NSSI, as well as depression, anxiety, and suicidal risks at a follow-up (T2). The findings revealed distinct predictors for each psychopathology. Specifically, psychological pain was identified as a significant predictor for depression, anxiety, and suicidal risks, while the perceived effectiveness of NSSI was crucial in forecasting its repetition. These findings imply that it is feasible to identify high-risk individuals by assessing key NSSI characteristics, and also highlight the importance of considering diverse NSSI characteristics when working with self-injurers. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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