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Development and validation of a prediction nomogram for academic burnout among Chinese adolescents: a cross-sectional study

OBJECTIVES: This study aimed to screen the potential risk factors for academic burnout among adolescents during the COVID-19 pandemic, develop and validate a predictive tool based on the risk factors for predicting academic burnout. DESIGN: This article presents a cross-sectional study. SETTING: Thi...

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Autores principales: Ye, Sheng, Wang, Rui, Pan, Huiqing, Zhao, Feiyang, Li, Weijia, Xing, Jingjing, Wu, Jinting
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10163519/
https://www.ncbi.nlm.nih.gov/pubmed/37130664
http://dx.doi.org/10.1136/bmjopen-2022-068370
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author Ye, Sheng
Wang, Rui
Pan, Huiqing
Zhao, Feiyang
Li, Weijia
Xing, Jingjing
Wu, Jinting
author_facet Ye, Sheng
Wang, Rui
Pan, Huiqing
Zhao, Feiyang
Li, Weijia
Xing, Jingjing
Wu, Jinting
author_sort Ye, Sheng
collection PubMed
description OBJECTIVES: This study aimed to screen the potential risk factors for academic burnout among adolescents during the COVID-19 pandemic, develop and validate a predictive tool based on the risk factors for predicting academic burnout. DESIGN: This article presents a cross-sectional study. SETTING: This study surveyed two high schools in Anhui Province, China. PARTICIPANTS: A total of 1472 adolescents were enrolled in this study. OUTCOME MEASURES: The questionnaires included demographic characteristic variables, living and learning states and adolescents’ academic burnout scale. Least absolute shrinkage and selection operator and multivariate logistic regression analyses were employed to screen the risk factors for academic burnout and develop a predictive model. The receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to assess the accuracy and discrimination of the nomogram. RESULTS: In this study, 21.70% of adolescents reported academic burnout. Multivariable logistic regression analysis showed that single-child family (OR=1.742, 95% CI: 1.243 to 2.441, p=0.001), domestic violence (OR=1.694, 95% CI: 1.159 to 2.476, p=0.007), online entertainment (>8 hours/day, OR=3.058, 95% CI: 1.634 to 5.720, p<0.001), physical activity (<3 hours/week, OR=1.686, 95% CI: 1.032 to 2.754, p=0.037), sleep duration (<6 hours/night, OR=2.342, 95% CI: 1.315 to 4.170, p=0.004) and academic performance (<400 score, OR=2.180, 95% CI: 1.201 to 3.958, p=0.010) were independent significant risk factors associated with academic burnout. The area under the curve of ROC with the nomogram was 0.686 in the training set and 0.706 in the validation set. Furthermore, DCA demonstrated that the nomogram had good clinical utility for both sets. CONCLUSIONS: The developed nomogram was a useful predictive model for academic burnout among adolescents during the COVID-19 pandemic. It is essential to emphasise the importance of mental health and promote a healthy lifestyle among adolescents during the future pandemic.
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spelling pubmed-101635192023-05-07 Development and validation of a prediction nomogram for academic burnout among Chinese adolescents: a cross-sectional study Ye, Sheng Wang, Rui Pan, Huiqing Zhao, Feiyang Li, Weijia Xing, Jingjing Wu, Jinting BMJ Open Global Health OBJECTIVES: This study aimed to screen the potential risk factors for academic burnout among adolescents during the COVID-19 pandemic, develop and validate a predictive tool based on the risk factors for predicting academic burnout. DESIGN: This article presents a cross-sectional study. SETTING: This study surveyed two high schools in Anhui Province, China. PARTICIPANTS: A total of 1472 adolescents were enrolled in this study. OUTCOME MEASURES: The questionnaires included demographic characteristic variables, living and learning states and adolescents’ academic burnout scale. Least absolute shrinkage and selection operator and multivariate logistic regression analyses were employed to screen the risk factors for academic burnout and develop a predictive model. The receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to assess the accuracy and discrimination of the nomogram. RESULTS: In this study, 21.70% of adolescents reported academic burnout. Multivariable logistic regression analysis showed that single-child family (OR=1.742, 95% CI: 1.243 to 2.441, p=0.001), domestic violence (OR=1.694, 95% CI: 1.159 to 2.476, p=0.007), online entertainment (>8 hours/day, OR=3.058, 95% CI: 1.634 to 5.720, p<0.001), physical activity (<3 hours/week, OR=1.686, 95% CI: 1.032 to 2.754, p=0.037), sleep duration (<6 hours/night, OR=2.342, 95% CI: 1.315 to 4.170, p=0.004) and academic performance (<400 score, OR=2.180, 95% CI: 1.201 to 3.958, p=0.010) were independent significant risk factors associated with academic burnout. The area under the curve of ROC with the nomogram was 0.686 in the training set and 0.706 in the validation set. Furthermore, DCA demonstrated that the nomogram had good clinical utility for both sets. CONCLUSIONS: The developed nomogram was a useful predictive model for academic burnout among adolescents during the COVID-19 pandemic. It is essential to emphasise the importance of mental health and promote a healthy lifestyle among adolescents during the future pandemic. BMJ Publishing Group 2023-05-02 /pmc/articles/PMC10163519/ /pubmed/37130664 http://dx.doi.org/10.1136/bmjopen-2022-068370 Text en © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle Global Health
Ye, Sheng
Wang, Rui
Pan, Huiqing
Zhao, Feiyang
Li, Weijia
Xing, Jingjing
Wu, Jinting
Development and validation of a prediction nomogram for academic burnout among Chinese adolescents: a cross-sectional study
title Development and validation of a prediction nomogram for academic burnout among Chinese adolescents: a cross-sectional study
title_full Development and validation of a prediction nomogram for academic burnout among Chinese adolescents: a cross-sectional study
title_fullStr Development and validation of a prediction nomogram for academic burnout among Chinese adolescents: a cross-sectional study
title_full_unstemmed Development and validation of a prediction nomogram for academic burnout among Chinese adolescents: a cross-sectional study
title_short Development and validation of a prediction nomogram for academic burnout among Chinese adolescents: a cross-sectional study
title_sort development and validation of a prediction nomogram for academic burnout among chinese adolescents: a cross-sectional study
topic Global Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10163519/
https://www.ncbi.nlm.nih.gov/pubmed/37130664
http://dx.doi.org/10.1136/bmjopen-2022-068370
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