Network Analysis of Stress, Anxiety, Depression, Burnout, Insomnia, Quality of Life, and Dropout Intention Among Chinese Medical Postgraduates.
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| Title: | Network Analysis of Stress, Anxiety, Depression, Burnout, Insomnia, Quality of Life, and Dropout Intention Among Chinese Medical Postgraduates. |
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| Authors: | Wu, Min (AUTHOR), Li, Zejun (AUTHOR), Wang, Xin (AUTHOR), Xu, Huixue (AUTHOR), Liu, Yueheng (AUTHOR), Li, Manyun (AUTHOR), Yuan, Wenjing (AUTHOR), Peng, Hanrui (AUTHOR), Wu, Qiuxia (AUTHOR), Liu, Tieqiao (AUTHOR), Bosurgi, Raffaella (AUTHOR) |
| Source: | Depression & Anxiety (1091-4269). 3/31/2026, Vol. 2026, p1-14. 14p. |
| Subjects: | Psychological stress, Psychological burnout, Anxiety, Quality of life, School dropouts, Data analysis, Insomnia, Mental depression |
| Abstract: | Background: Chinese medical postgraduate students are subjected to significant academic and professional pressures, leading to heightened levels of stress, anxiety, depression, burnout, and insomnia (SADBI). While network analysis offers a robust framework for examining the complex interrelationships among these symptoms, no prior studies have applied this methodology to this specific population. This study aimed to investigate the interconnections among SADBI symptoms, identify central and bridge symptoms within the network, and explore their associations with quality of life (QOL) and dropout intention (DI). Methods: A cross‐sectional survey was conducted from November 2023 to June 2024, enrolling 1228 Chinese medical postgraduates through snowball sampling via the Wenjuanxing platform. The psychological symptoms were assessed using validated instruments, including 4‐item Perceived Stress Scale (PSS‐4), 7‐item Generalized Anxiety Disorder Scale (GAD‐7), 9‐item Patient Health Questionnaire (PHQ‐9), Oldenburg Burnout Inventory (OLBI), and Insomnia Severity Index (ISI). Network analysis was employed to model the interactions among symptoms and their associations with adverse outcomes (QOL and DI). Central 0and bridge symptoms were identified using expected influence (EI) and bridge EI (BEI) indices, respectively. Results: Within the SADBI network, exhaustion (EX) demonstrated the highest EI, establishing it as the most central symptom. Perceived stress (PS) and EX were identified as the primary bridge symptoms with the highest BEI. PS, EX, and sleep dissatisfaction (ISI4) exhibited the strongest negative correlations with QOL. Additionally, disengagement (DEM), suicidal ideation (PHQ9), and worthlessness (PHQ6) were strongly correlated with DI. The constructed SADBI network exhibited excellent stability and accuracy. Conclusion: PS, EX, and DEM emerged as pivotal symptoms within the SADBI network, highlighting their critical role in the mental health challenges faced by Chinese medical postgraduate students. These findings underscore the importance of targeting these symptoms in prevention and intervention strategies to mitigate mental distress, enhance QOL, and reduce DI in this population. [ABSTRACT FROM AUTHOR] |
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| Database: | Psychology and Behavioral Sciences Collection |
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| Abstract: | Background: Chinese medical postgraduate students are subjected to significant academic and professional pressures, leading to heightened levels of stress, anxiety, depression, burnout, and insomnia (SADBI). While network analysis offers a robust framework for examining the complex interrelationships among these symptoms, no prior studies have applied this methodology to this specific population. This study aimed to investigate the interconnections among SADBI symptoms, identify central and bridge symptoms within the network, and explore their associations with quality of life (QOL) and dropout intention (DI). Methods: A cross‐sectional survey was conducted from November 2023 to June 2024, enrolling 1228 Chinese medical postgraduates through snowball sampling via the Wenjuanxing platform. The psychological symptoms were assessed using validated instruments, including 4‐item Perceived Stress Scale (PSS‐4), 7‐item Generalized Anxiety Disorder Scale (GAD‐7), 9‐item Patient Health Questionnaire (PHQ‐9), Oldenburg Burnout Inventory (OLBI), and Insomnia Severity Index (ISI). Network analysis was employed to model the interactions among symptoms and their associations with adverse outcomes (QOL and DI). Central 0and bridge symptoms were identified using expected influence (EI) and bridge EI (BEI) indices, respectively. Results: Within the SADBI network, exhaustion (EX) demonstrated the highest EI, establishing it as the most central symptom. Perceived stress (PS) and EX were identified as the primary bridge symptoms with the highest BEI. PS, EX, and sleep dissatisfaction (ISI4) exhibited the strongest negative correlations with QOL. Additionally, disengagement (DEM), suicidal ideation (PHQ9), and worthlessness (PHQ6) were strongly correlated with DI. The constructed SADBI network exhibited excellent stability and accuracy. Conclusion: PS, EX, and DEM emerged as pivotal symptoms within the SADBI network, highlighting their critical role in the mental health challenges faced by Chinese medical postgraduate students. These findings underscore the importance of targeting these symptoms in prevention and intervention strategies to mitigate mental distress, enhance QOL, and reduce DI in this population. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 10914269 |
| DOI: | 10.1155/da/3424408 |
