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Risk factors and prediction model for mental health in Chinese soldiers
INTRODUCTION: This study aimed to explore potential risk factors for mental health concerns, and the prediction model for mental health concerns in Chinese soldiers was constructed through combined eligible risk factors. METHODS: This cross-sectional study was performed on soldiers under direct comm...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10196266/ https://www.ncbi.nlm.nih.gov/pubmed/37215678 http://dx.doi.org/10.3389/fpsyt.2023.1125411 |
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author | Zhao, Mengxue He, Ying Tang, Quan Wang, Ni Zheng, Haoxin Feng, Zhengzhi |
author_facet | Zhao, Mengxue He, Ying Tang, Quan Wang, Ni Zheng, Haoxin Feng, Zhengzhi |
author_sort | Zhao, Mengxue |
collection | PubMed |
description | INTRODUCTION: This study aimed to explore potential risk factors for mental health concerns, and the prediction model for mental health concerns in Chinese soldiers was constructed through combined eligible risk factors. METHODS: This cross-sectional study was performed on soldiers under direct command from Gansu, Sichuan, and Chongqing in China, and the soldiers were selected by cluster convenient sampling from 16 October 2018 to 10 December 2018. The Symptom Checklist-90 (SCL-90) and three questionnaires (Military Mental Health Status Questionnaire, Military Mental Health Ability Questionnaire, and Mental Quality Questionnaire for Army Men) were administered, including demographics, military careers, and 18 factors. RESULTS: Of 1,430 Chinese soldiers, 162 soldiers presented mental disorders, with a prevalence of 11.33%. A total of five risk factors were identified, including serving place (Sichuan vs. Gansu: OR, 1.846, 95% CI: 1.028–3.315, P = 0.038; Chongqing vs. Gansu: OR, 3.129, 95% CI, 1.669–5.869, P = 0.003), psychosis (OR, 1.491, 95% CI, 1.152–1.928, P = 0.002), depression (OR, 1.482, 95% CI, 1.349–1.629, P < 0.001), sleep problems (OR, 1.235, 95% CI, 1.162–1.311, P < 0.001), and frustration (OR, 1.050, 95% CI, 1.015–1.087, P = 0.005). The area under the ROC curve by combining these factors was 0.930 (95% CI: 0.907–0.952) for predicting mental disorders in Chinese soldiers. CONCLUSION: The findings of this study demonstrate that mental disorders and onset in Chinese soldiers can be predicted on the basis of these three questionnaires, and the predictive value of the combined model was high. |
format | Online Article Text |
id | pubmed-10196266 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-101962662023-05-20 Risk factors and prediction model for mental health in Chinese soldiers Zhao, Mengxue He, Ying Tang, Quan Wang, Ni Zheng, Haoxin Feng, Zhengzhi Front Psychiatry Psychiatry INTRODUCTION: This study aimed to explore potential risk factors for mental health concerns, and the prediction model for mental health concerns in Chinese soldiers was constructed through combined eligible risk factors. METHODS: This cross-sectional study was performed on soldiers under direct command from Gansu, Sichuan, and Chongqing in China, and the soldiers were selected by cluster convenient sampling from 16 October 2018 to 10 December 2018. The Symptom Checklist-90 (SCL-90) and three questionnaires (Military Mental Health Status Questionnaire, Military Mental Health Ability Questionnaire, and Mental Quality Questionnaire for Army Men) were administered, including demographics, military careers, and 18 factors. RESULTS: Of 1,430 Chinese soldiers, 162 soldiers presented mental disorders, with a prevalence of 11.33%. A total of five risk factors were identified, including serving place (Sichuan vs. Gansu: OR, 1.846, 95% CI: 1.028–3.315, P = 0.038; Chongqing vs. Gansu: OR, 3.129, 95% CI, 1.669–5.869, P = 0.003), psychosis (OR, 1.491, 95% CI, 1.152–1.928, P = 0.002), depression (OR, 1.482, 95% CI, 1.349–1.629, P < 0.001), sleep problems (OR, 1.235, 95% CI, 1.162–1.311, P < 0.001), and frustration (OR, 1.050, 95% CI, 1.015–1.087, P = 0.005). The area under the ROC curve by combining these factors was 0.930 (95% CI: 0.907–0.952) for predicting mental disorders in Chinese soldiers. CONCLUSION: The findings of this study demonstrate that mental disorders and onset in Chinese soldiers can be predicted on the basis of these three questionnaires, and the predictive value of the combined model was high. Frontiers Media S.A. 2023-05-05 /pmc/articles/PMC10196266/ /pubmed/37215678 http://dx.doi.org/10.3389/fpsyt.2023.1125411 Text en Copyright © 2023 Zhao, He, Tang, Wang, Zheng and Feng. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Psychiatry Zhao, Mengxue He, Ying Tang, Quan Wang, Ni Zheng, Haoxin Feng, Zhengzhi Risk factors and prediction model for mental health in Chinese soldiers |
title | Risk factors and prediction model for mental health in Chinese soldiers |
title_full | Risk factors and prediction model for mental health in Chinese soldiers |
title_fullStr | Risk factors and prediction model for mental health in Chinese soldiers |
title_full_unstemmed | Risk factors and prediction model for mental health in Chinese soldiers |
title_short | Risk factors and prediction model for mental health in Chinese soldiers |
title_sort | risk factors and prediction model for mental health in chinese soldiers |
topic | Psychiatry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10196266/ https://www.ncbi.nlm.nih.gov/pubmed/37215678 http://dx.doi.org/10.3389/fpsyt.2023.1125411 |
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