Comparing the centrality symptoms of major depressive disorder samples across junior high school students, senior high school students, college students and elderly adults during city lockdown of COVID-19 pandemic—A network analysis

Tao, Yanqiang; Hou, Wenxin; Niu, Haiqun; Ma, Zijuan; Zheng, Zhigang; Wang, Shujian; Liu, Xiangping; Zhang, Liang

doi:10.1016/j.jad.2022.12.120

Cited by 19 publications

(8 citation statements)

References 78 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Network analysis has more practical utility in psychotherapy, because this approach can locate core symptoms (Marchetti, 2019) and highlight how symptom clusters are connected (Garabiles et al, 2019), which helps us grasp the detailed symptom structure (van Bork et al, 2021). The network approach has been used in several studies to examine the connection between anxiety‐depression symptoms (Sánchez Hernández et al, 2021; Tao, Hou, Niu, et al, 2023) and suicidal ideation (Li et al, 2022). According to a network analysis of depression and anxiety, “sad mood” was the core symptom and was related significantly to suicidal ideation in a psychiatric sample (Beard et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Centrality depression–anxiety symptoms linked to suicidal ideation among depressed college students––A network approach

et al. 2023

Self Cite

View full text Add to dashboard Cite

The long‐term effects of the COVID‐19 pandemic have caused severe mental health problems among college students, which can eventually cause suicidal ideation. Therefore, through network analysis, this study aims to explore the new characteristics of the depression–anxiety symptom network that arose during the long‐term lockdown of the COVID‐19 pandemic and to identify the most influential symptoms linked to suicidal ideation. We used a Patient Health Questionnaire (PHQ‐9) score above 10 as the cutoff and screened 622 participants with an inclination toward depressive disorders from 7976 college students, and then divided the sample into suicidal and nonsuicidal groups based on the presence or absence of suicidal ideation. The General Anxiety Disorder scale (GAD‐7) was also used. Network analysis was used to identify the network structure of anxiety–depression and which symptoms were directly related to suicidal ideation in the network. The prevalence of depression and anxiety among Chinese college students in the late stage of the COVID‐19 pandemic was 7.8% and 17.8%, respectively. The most central symptoms in the nonsuicidal group were “excessive worry,” “uncontrollable worry,” and “nervousness,” and in the suicidal group they were “excessive worry,” “motor function,” and “irritability.” The network of the suicidal group was denser than that of the nonsuicidal group. The most influential symptom directly related to suicidal ideation was “guilt.” The most influential central symptom of depression–anxiety comorbidity characteristics of Chinese adolescents showed a tendency to shift from depression‐oriented (i.e., sad mood) to anxiety‐oriented (i.e., excessive worry) with the continuation of the COVID‐19 pandemic. Treatments or interventions focused on these critical symptoms could be useful in preventing college students from suicide risk.

show abstract

Section: Introductionmentioning

confidence: 99%

Centrality depression–anxiety symptoms linked to suicidal ideation among depressed college students––A network approach

et al. 2023

Self Cite

View full text Add to dashboard Cite

show abstract

“…The network visually presents symptom-to-symptom interactions, and interventions targeting the central symptoms may be more effective because they play a primary role. 83 Strength centrality receives more attention because it was the most stable in both this study and earlier network analyses. 84 Different interventions can be used to treat different core symptoms of TB.…”

Section: Discussionmentioning

confidence: 57%

The Symptom Network Structure of Teachers’ Burnout and Its Connection to Psychological Capital

Xue,

Sun,

et al. 2023

PRBM

View full text Add to dashboard Cite

Background Teacher burnout is characterized by emotional and physical exhaustion resulting from excessive work-related stress. Previous research based on traditional latent variable theory has revealed a significant negative relationship between TB and psychological capital (PsyCap). This study explored the complex symptoms of TB and the contact points of PsyCap in reducing TB using psychometric network analysis. Methods A total of 3991 teachers completed the burnout subscale of the Professional Quality of Life and Psychological Capital Scale. Results The results showed that: (a) In the TB network, the core symptoms displayed by teachers due to burnout are difficulty feeling “I am a very caring person”, “I am happy”, and “I am the person I always wanted to be”; (b) The TB-PsyCap network was closely connected, and the symptoms affected each other. PsyCap affected the TB network through “I feel optimistic and happy almost every day” and “I often feel that there is a future as a teacher”; (c) PsyCap’s bridge symptoms had a negative impact on TB, and PsyCap may reduce TB primarily through optimistic, hopeful dimensions. Conclusion and Implications Psychometric network analysis helps us understand the complex symptoms of TB and the contact points of PsyCap in reducing TB. This study offers valuable insights into the prevention of, and intervention in, burnout within the teaching community.

show abstract

“…Each item is rated on a scale from 0 (not at all) to 3 (nearly every day), resulting in a total score ranging from 0 to 27. The Chinese version of the PHQ-9 has been validated as an effective screening tool for depression in the general Chinese population [47]. In the present study, the PHQ-9 demonstrated a high internal consistency (α = 0.88).…”

Section: Patient Health Questionnaire (Phq-9)mentioning

confidence: 51%

The Impact of Long-Term Online Learning on Internet Addiction Symptoms among Depressed Secondary School Students: Insights from a Cross-Panel Network Analysis

Tao

Tang

Zou

et al. 2023

Behavioral Sciences

Self Cite

View full text Add to dashboard Cite

Background: The COVID-19 pandemic and the shift to online learning have increased the risk of Internet addiction (IA) among adolescents, especially those who are depressed. This study aims to identify the core symptoms of IA among depressed adolescents using a cross-lagged panel network framework, offering a fresh perspective on understanding the interconnectedness of IA symptoms. Methods: Participants completed the Internet addiction test and the Patient Health Questionnaire-9. A total of 2415 students were initially included, and after matching, only 342 students (a cutoff score of 8) were retained for the final data analysis. A cross-lagged panel network analysis was conducted to examine the autoregressive and cross-lagged trajectories of IA symptoms over time. Results: The incidence rate of depression rose remarkably from 14.16% (N = 342) to 17.64% (N = 426) after the four-month online learning. The symptom of “Anticipation” exhibited the highest out-expected influence within the IA network, followed by “Stay online longer” and “Job performance or productivity suffer”. Regarding the symptom network of depression, “Job performance or productivity suffer” had the highest in-expected influence, followed by “Life boring and empty”, “Snap or act annoyed if bothered”, “Check email/SNS before doing things”, and “School grades suffer”. No significant differences were found in global network strength and network structure between waves 1 and 2. Conclusion: These findings prove the negative effects of online learning on secondary students’ mental health and have important implications for developing more effective interventions and policies to mitigate IA levels among depressed adolescents undergoing online learning.

show abstract

Comparing the centrality symptoms of major depressive disorder samples across junior high school students, senior high school students, college students and elderly adults during city lockdown of COVID-19 pandemic—A network analysis

Cited by 19 publications

References 78 publications

Centrality depression–anxiety symptoms linked to suicidal ideation among depressed college students––A network approach

Centrality depression–anxiety symptoms linked to suicidal ideation among depressed college students––A network approach

The Symptom Network Structure of Teachers’ Burnout and Its Connection to Psychological Capital

The Impact of Long-Term Online Learning on Internet Addiction Symptoms among Depressed Secondary School Students: Insights from a Cross-Panel Network Analysis

Contact Info

Product

Resources

About