ArticleBMJ open2023
Development and validation of a prediction nomogram for academic burnout among Chinese adolescents: a cross-sectional study.
Article in BMJ open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed, 2 citations in OpenAlex.
- Identifying foreign language learning burnout: latent profiles, cutoff points, and an explainable web-based calculator.Frontiers in psychology · 2026Article
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Authors and funding
7 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
objectivesThis 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.
designThis article presents a cross-sectional study.
settingThis study surveyed two high schools in Anhui Province, China.
participantsA 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.
resultsIn 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.
conclusionsThe 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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