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      Connecting the dots: A network approach to post‐traumatic stress symptoms in Chinese healthcare workers during the peak of the Coronavirus Disease 2019 outbreak

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          Abstract

          Healthcare workers are at elevated risk to develop symptoms of post‐traumatic stress disorder (PTSD) in response to an outbreak of a highly infectious disease. The current study set‐out to model the complex interrelations between PTSD symptoms during the peak of the Coronavirus Disease 2019 outbreak in 291 Chinese healthcare workers and 291 matched control cases that were selected from the general population. For this purpose, we estimated regularized partial correlation networks. Within the network of healthcare workers, we observed a central role for avoidance of reminders of the traumatic event, physiological cue reactivity, anger/irritability, re‐experiencing, and startle. We identified three clusters of closely interconnected PTSD symptoms in healthcare workers, consisting of (a) symptoms of re‐experiencing and anxious arousal, (b) symptoms of avoidance and amnesia and (c) symptoms of emotional numbing and dysphoric arousal. Respectively, startle, avoidance of reminders and feeling detached emerged as bridging nodes in these communities. Although yielding highly similar network models, the PTSD symptom structure of healthcare workers showed several unique features compared to the matched control sample. This is informative for interventions aimed at targeting PTSD symptoms in healthcare workers in the context of a public health emergency.

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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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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              qgraph: Network Visualizations of Relationships in Psychometric Data

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                Author and article information

                Contributors
                kristof.hoorelbeke@ugent.be
                daiqin101@hotmail.com
                Journal
                Stress Health
                Stress Health
                10.1002/(ISSN)1532-2998
                SMI
                Stress and Health
                John Wiley and Sons Inc. (Hoboken )
                1532-3005
                1532-2998
                04 February 2021
                : 10.1002/smi.3027
                Affiliations
                [ 1 ] Department of Experimental Clinical and Health Psychology Ghent University Ghent Belgium
                [ 2 ] Educational Center of Mental Health Army Medical University Chongqing China
                Author notes
                [*] [* ] Correspondence

                Qin Dai, Department of Psychology, Army Medical University, Chongqing, 400038, China.

                Email: daiqin101@ 123456hotmail.com

                Kristof Hoorelbeke, Faculteit Psychologie en Pedagogische Wetenschappen, Universiteit Gent, Henri‐Dunantlaan 2, 9000 Gent, Belgium.

                Email: kristof.hoorelbeke@ 123456ugent.be

                Author information
                https://orcid.org/0000-0002-8269-0441
                Article
                SMI3027
                10.1002/smi.3027
                8013316
                33434296
                f7ad003e-a572-43de-91f2-e77b17848ed5
                © 2021 John Wiley & Sons Ltd.

                This article is being made freely available through PubMed Central as part of the COVID-19 public health emergency response. It can be used for unrestricted research re-use and analysis in any form or by any means with acknowledgement of the original source, for the duration of the public health emergency.

                History
                : 17 December 2020
                : 30 September 2020
                : 31 December 2020
                Page count
                Figures: 3, Tables: 1, Pages: 14, Words: 10465
                Funding
                Funded by: Key projects of People's Liberation Army of China
                Award ID: BLJ19J009
                Funded by: Fonds Wetenschappelijk Onderzoek , open-funder-registry 10.13039/501100003130;
                Award ID: FWO.3EO.2018.0031.01
                Funded by: National Social Science Fund of China
                Award ID: 17XSH001
                Categories
                Research Article
                Research Article
                Custom metadata
                2.0
                corrected-proof
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.0.1 mode:remove_FC converted:01.04.2021

                covid‐19,healthcare workers,network analysis,ptsd,stress,symptom structure

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