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Use of latent profile analysis and k-means clustering to identify student anxiety profiles

BACKGROUND: Anxiety disorders are often the first presentation of psychopathology in youth and are considered the most common psychiatric disorders in children and adolescents. This study aimed to identify distinct student anxiety profiles to develop targeted interventions. METHODS: A cross-sectiona...

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Autores principales: Liu, Fang, Yang, Dan, Liu, Yueguang, Zhang, Qin, Chen, Shiyu, Li, Wanxia, Ren, Jidong, Tian, Xiaobin, Wang, Xin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8728926/
https://www.ncbi.nlm.nih.gov/pubmed/34986837
http://dx.doi.org/10.1186/s12888-021-03648-7
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author Liu, Fang
Yang, Dan
Liu, Yueguang
Zhang, Qin
Chen, Shiyu
Li, Wanxia
Ren, Jidong
Tian, Xiaobin
Wang, Xin
author_facet Liu, Fang
Yang, Dan
Liu, Yueguang
Zhang, Qin
Chen, Shiyu
Li, Wanxia
Ren, Jidong
Tian, Xiaobin
Wang, Xin
author_sort Liu, Fang
collection PubMed
description BACKGROUND: Anxiety disorders are often the first presentation of psychopathology in youth and are considered the most common psychiatric disorders in children and adolescents. This study aimed to identify distinct student anxiety profiles to develop targeted interventions. METHODS: A cross-sectional study was conducted with 9738 students in Yingshan County. Background characteristics were collected and Mental Health Test (MHT) were completed. Latent profile analysis (LPA) was applied to define student anxiety profiles, and then the analysis was repeated using k-means clustering. RESULTS: LPA yielded 3 profiles: the low-risk, mild-risk and high-risk groups, which comprised 29.5, 38.1 and 32.4% of the sample, respectively. Repeating the analysis using k-means clustering resulted in similar groupings. Most students in a particular k-means cluster were primarily in a single LPA-derived student profile. The multinomial ordinal logistic regression results showed that the high-risk group was more likely to be female, junior, and introverted, to live in a town, to have lower or average academic performance, to have heavy or average academic pressure, and to be in schools that have never or occasionally have organized mental health education activities. CONCLUSIONS: The findings suggest that students with anxiety symptoms may be categorized into distinct profiles that are amenable to varying strategies for coordinated interventions. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12888-021-03648-7.
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spelling pubmed-87289262022-01-06 Use of latent profile analysis and k-means clustering to identify student anxiety profiles Liu, Fang Yang, Dan Liu, Yueguang Zhang, Qin Chen, Shiyu Li, Wanxia Ren, Jidong Tian, Xiaobin Wang, Xin BMC Psychiatry Research BACKGROUND: Anxiety disorders are often the first presentation of psychopathology in youth and are considered the most common psychiatric disorders in children and adolescents. This study aimed to identify distinct student anxiety profiles to develop targeted interventions. METHODS: A cross-sectional study was conducted with 9738 students in Yingshan County. Background characteristics were collected and Mental Health Test (MHT) were completed. Latent profile analysis (LPA) was applied to define student anxiety profiles, and then the analysis was repeated using k-means clustering. RESULTS: LPA yielded 3 profiles: the low-risk, mild-risk and high-risk groups, which comprised 29.5, 38.1 and 32.4% of the sample, respectively. Repeating the analysis using k-means clustering resulted in similar groupings. Most students in a particular k-means cluster were primarily in a single LPA-derived student profile. The multinomial ordinal logistic regression results showed that the high-risk group was more likely to be female, junior, and introverted, to live in a town, to have lower or average academic performance, to have heavy or average academic pressure, and to be in schools that have never or occasionally have organized mental health education activities. CONCLUSIONS: The findings suggest that students with anxiety symptoms may be categorized into distinct profiles that are amenable to varying strategies for coordinated interventions. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12888-021-03648-7. BioMed Central 2022-01-05 /pmc/articles/PMC8728926/ /pubmed/34986837 http://dx.doi.org/10.1186/s12888-021-03648-7 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Liu, Fang
Yang, Dan
Liu, Yueguang
Zhang, Qin
Chen, Shiyu
Li, Wanxia
Ren, Jidong
Tian, Xiaobin
Wang, Xin
Use of latent profile analysis and k-means clustering to identify student anxiety profiles
title Use of latent profile analysis and k-means clustering to identify student anxiety profiles
title_full Use of latent profile analysis and k-means clustering to identify student anxiety profiles
title_fullStr Use of latent profile analysis and k-means clustering to identify student anxiety profiles
title_full_unstemmed Use of latent profile analysis and k-means clustering to identify student anxiety profiles
title_short Use of latent profile analysis and k-means clustering to identify student anxiety profiles
title_sort use of latent profile analysis and k-means clustering to identify student anxiety profiles
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8728926/
https://www.ncbi.nlm.nih.gov/pubmed/34986837
http://dx.doi.org/10.1186/s12888-021-03648-7
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