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      Network of burnout, depression, anxiety, and dropout intention in medical undergraduates

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          Abstract

          Background:

          Burnout, depression, and anxiety are highly prevalent among medical students, which often leads to their attrition. We aim to assess the inter-relationships of depression, burnout, and anxiety symptoms with dropout intention among Chinese medical undergraduates using the network analysis.

          Method:

          A total of 3,648 Chinese medical undergraduates were recruited through snowball sampling. Learning burnout scale, 9-item Patient Health Questionnaire (PHQ-9), and General Anxiety Disorder Scale (GAD-7) was used to assess burnout, depression, and anxiety symptoms, respectively. We used the EBICglasso model to estimate the network. We compared the network based on gender, study phase, and clinical experience.

          Results:

          After removing repeated submissions and incorrect responses to the trap question, 3,536 participants were included in the final analysis. The prevalence of burnout, depression, anxiety, and dropout intention was 38, 62.7, 38.4, and 39% respectively, which is consistent with previous findings. Network analysis suggested that anxiety and depression items clustered together and displayed several strong bridge connections, while burnout items formed another cluster. All the strongest edges were within the respective distress. Cynicism symptoms ‘I am fed up with study’ and ‘I want to study but I feel that studying is boring’ were the most central symptoms, while ‘fatigue’ and ‘worthless’ were the bridge symptoms within the burnout-depression-anxiety network. Other central symptoms included ‘worthless’, ‘I can handle my courses’, ‘nervous’, and ‘uncontrollable worry’. Cynicism symptoms ‘I am interested in my major’ and ‘I feel that the knowledge I have learned is useless’ were mostly related to dropout intention. Gender, study phase, and clinical experience didn’t affect the global strength of the burnout-depression-anxiety network.

          Conclusion:

          Our results indicated the predominance of cynicism symptoms within the burnout-depression-anxiety network and its substantial impact on dropout intention, suggesting that early detection and intervention for cynicism symptoms in Chinese medical students are in urgent need. Other central and bridge symptoms might also serve as potential targets for the prevention and treatment of burnout, depression, and anxiety among medical students. For example, studies suggest cognitive-behavioral therapy could quickly improve ‘worthless’, which might be beneficial in treating burnout, depression, and anxiety in medical students.

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          Most cited references47

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          Estimating psychological networks and their accuracy: A tutorial paper

          The usage of psychological networks that conceptualize behavior as a complex interplay of psychological and other components has gained increasing popularity in various research fields. While prior publications have tackled the topics of estimating and interpreting such networks, little work has been conducted to check how accurate (i.e., prone to sampling variation) networks are estimated, and how stable (i.e., interpretation remains similar with less observations) inferences from the network structure (such as centrality indices) are. In this tutorial paper, we aim to introduce the reader to this field and tackle the problem of accuracy under sampling variation. We first introduce the current state-of-the-art of network estimation. Second, we provide a rationale why researchers should investigate the accuracy of psychological networks. Third, we describe how bootstrap routines can be used to (A) assess the accuracy of estimated network connections, (B) investigate the stability of centrality indices, and (C) test whether network connections and centrality estimates for different variables differ from each other. We introduce two novel statistical methods: for (B) the correlation stability coefficient, and for (C) the bootstrapped difference test for edge-weights and centrality indices. We conducted and present simulation studies to assess the performance of both methods. Finally, we developed the free R-package bootnet that allows for estimating psychological networks in a generalized framework in addition to the proposed bootstrap methods. We showcase bootnet in a tutorial, accompanied by R syntax, in which we analyze a dataset of 359 women with posttraumatic stress disorder available online. Electronic supplementary material The online version of this article (doi:10.3758/s13428-017-0862-1) contains supplementary material, which is available to authorized users.
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            Network analysis: an integrative approach to the structure of psychopathology.

            In network approaches to psychopathology, disorders result from the causal interplay between symptoms (e.g., worry → insomnia → fatigue), possibly involving feedback loops (e.g., a person may engage in substance abuse to forget the problems that arose due to substance abuse). The present review examines methodologies suited to identify such symptom networks and discusses network analysis techniques that may be used to extract clinically and scientifically useful information from such networks (e.g., which symptom is most central in a person's network). The authors also show how network analysis techniques may be used to construct simulation models that mimic symptom dynamics. Network approaches naturally explain the limited success of traditional research strategies, which are typically based on the idea that symptoms are manifestations of some common underlying factor, while offering promising methodological alternatives. In addition, these techniques may offer possibilities to guide and evaluate therapeutic interventions.
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              Is Open Access

              The Relationship Between Burnout, Depression, and Anxiety: A Systematic Review and Meta-Analysis

              Background: Burnout is a psychological syndrome characterized by emotional exhaustion, feelings of cynicism and reduced personal accomplishment. In the past years there has been disagreement on whether burnout and depression are the same or different constructs, as they appear to share some common features (e.g., loss of interest and impaired concentration). However, the results so far are inconclusive and researchers disagree with regard to the degree to which we should expect such overlap. The aim of this systematic review and meta-analysis is to examine the relationship between burnout and depression. Additionally, given that burnout is the result of chronic stress and that working environments can often trigger anxious reactions, we also investigated the relationship between burnout and anxiety. Method: We searched the online databases SCOPUS, Web of Science, MEDLINE (PubMed), and Google Scholar for studies examining the relationship between burnout and depression and burnout and anxiety, which were published between January 2007 and August 2018. Inclusion criteria were used for all studies and included both cross-sectional and longitudinal designs, published and unpublished research articles, full-text articles, articles written in the English language, studies that present the effects sizes of their findings and that used reliable research tools. Results: Our results showed a significant association between burnout and depression (r = 0.520, SE = 0.012, 95% CI = 0.492, 0.547) and burnout and anxiety (r = 0.460, SE = 0.014, 95% CI = 0.421, 0.497). However, moderation analysis for both burnout–depression and burnout–anxiety relationships revealed that the studies in which either the MBI test was used or were rated as having better quality showed lower effect sizes. Conclusions: Our research aims to clarify the relationship between burnout–depression and burnout–anxiety relationships. Our findings revealed no conclusive overlap between burnout and depression and burnout and anxiety, indicating that they are different and robust constructs. Future studies should focus on utilizing more longitudinal designs in order to assess the causal relationships between these variables.
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                Author and article information

                Contributors
                Journal
                International Journal of Social Psychiatry
                Int J Soc Psychiatry
                SAGE Publications
                0020-7640
                1741-2854
                April 24 2023
                : 002076402311666
                Affiliations
                [1 ]Department of Psychiatry and National Clinical Research Center for Mental Disorders, Second Xiangya Hospital, Central South University, Changsha, China
                [2 ]Department of Psychiatry, Hunan Brain Hospital (Hunan Second People’s Hospital), Changsha, China
                [3 ]College of Health Solutions, Arizona State University, Phoenix, AZ, USA
                [4 ]Meditation Research Program, Massachusetts General Hospital, Boston, MA, USA
                [5 ]Department of Psychiatry, Harvard Medical School, Boston, MA, USA
                Article
                10.1177/00207640231166629
                37092762
                dbdb4d06-6827-410d-a2b5-e3db8a7eafa7
                © 2023

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