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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Psychiatry</journal-id>
<journal-title>Frontiers in Psychiatry</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Psychiatry</abbrev-journal-title>
<issn pub-type="epub">1664-0640</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fpsyt.2025.1631050</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Psychiatry</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Gender differences in the prevalence and correlates of COVID-19 fear among mental health professionals: a network perspective based on a national survey in China</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" equal-contrib="yes">
<name>
<surname>Rao</surname>
<given-names>Shu-Ying</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="author-notes" rid="fn002">
<sup>&#x2020;</sup>
</xref>
<xref ref-type="author-notes" rid="fn003">
<sup>&#x2021;</sup>
</xref>
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</contrib>
<contrib contrib-type="author" equal-contrib="yes">
<name>
<surname>Zheng</surname>
<given-names>Mu-Rui</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="author-notes" rid="fn003">
<sup>&#x2020;</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/writing-original-draft/"/>
</contrib>
<contrib contrib-type="author" equal-contrib="yes">
<name>
<surname>An</surname>
<given-names>Feng-Rong</given-names>
</name>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
<xref ref-type="author-notes" rid="fn002">
<sup>&#x2020;</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
</contrib>
<contrib contrib-type="author" equal-contrib="yes">
<name>
<surname>Feng</surname>
<given-names>Yuan</given-names>
</name>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
<xref ref-type="author-notes" rid="fn002">
<sup>&#x2020;</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/917126/overview"/>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Su</surname>
<given-names>Zhaohui</given-names>
</name>
<xref ref-type="aff" rid="aff4">
<sup>4</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/1046370/overview"/>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Cheung</surname>
<given-names>Teris</given-names>
</name>
<xref ref-type="aff" rid="aff5">
<sup>5</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/565626/overview"/>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Ungvari</surname>
<given-names>Gabor S.</given-names>
</name>
<xref ref-type="aff" rid="aff6">
<sup>6</sup>
</xref>
<xref ref-type="aff" rid="aff7">
<sup>7</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/1554975/overview"/>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Ng</surname>
<given-names>Chee H.</given-names>
</name>
<xref ref-type="aff" rid="aff8">
<sup>8</sup>
</xref>
<xref ref-type="author-notes" rid="fn001">
<sup>*</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/501255/overview"/>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Xiang</surname>
<given-names>Yu-Tao</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="author-notes" rid="fn001">
<sup>*</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/797822/overview"/>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Wang</surname>
<given-names>Gang</given-names>
</name>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
<xref ref-type="author-notes" rid="fn001">
<sup>*</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/2558488/overview"/>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
</contrib>
</contrib-group>
<aff id="aff1">
<sup>1</sup>
<institution>Unit of Psychiatry, Department of Public Health and Medicinal Administration, &amp; Institute of Translational Medicine, Faculty of Health Sciences, University of Macau</institution>, <addr-line>Macao</addr-line>,&#xa0;<country>Macao SAR, China</country>
</aff>
<aff id="aff2">
<sup>2</sup>
<institution>Centre for Cognitive and Brain Sciences, University of Macau</institution>, <addr-line>Macao</addr-line>,&#xa0;<country>Macao SAR, China</country>
</aff>
<aff id="aff3">
<sup>3</sup>
<institution>Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders &amp; National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University</institution>, <addr-line>Beijing</addr-line>,&#xa0;<country>China</country>
</aff>
<aff id="aff4">
<sup>4</sup>
<institution>School of Public Health, Southeast University</institution>, <addr-line>Nanjing</addr-line>,&#xa0;<country>China</country>
</aff>
<aff id="aff5">
<sup>5</sup>
<institution>School of Nursing, Hong Kong Polytechnic University</institution>, <addr-line>Hong Kong</addr-line>,&#xa0;<country>Hong Kong SAR, China</country>
</aff>
<aff id="aff6">
<sup>6</sup>
<institution>Section of Psychiatry, University of Notre Dame Australia</institution>, <addr-line>Fremantle, WA</addr-line>,&#xa0;<country>Australia</country>
</aff>
<aff id="aff7">
<sup>7</sup>
<institution>Division of Psychiatry, School of Medicine, University of Western Australia</institution>, <addr-line>Perth, WA</addr-line>,&#xa0;<country>Australia</country>
</aff>
<aff id="aff8">
<sup>8</sup>
<institution>Department of Psychiatry, The Melbourne Clinic and St Vincent&#x2019;s Hospital, University of Melbourne</institution>, <addr-line>Richmond, VIC</addr-line>,&#xa0;<country>Australia</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>Edited by: Pv AshaRani, Institute of Mental Health, Singapore</p>
</fn>
<fn fn-type="edited-by">
<p>Reviewed by: Yikai Dou, Sichuan University, China</p>
<p>Blessing Osagumwendia Josiah, Turks and Caicos Islands Community College, Turks and Caicos Islands</p>
</fn>
<fn fn-type="corresp" id="fn001">
<p>*Correspondence: Yu-Tao Xiang, <email xlink:href="mailto:xyutly@gmail.com">xyutly@gmail.com</email>; Gang Wang, <email xlink:href="mailto:gangwangdoc@ccmu.edu.cn">gangwangdoc@ccmu.edu.cn</email>; Chee H. Ng, <email xlink:href="mailto:cng@unimelb.edu.au">cng@unimelb.edu.au</email>
</p>
</fn>
<fn fn-type="equal" id="fn002">
<p>&#x2020;These authors have contributed equally to this work</p>
</fn>
<fn fn-type="other" id="fn003">
<p>&#x2021;ORCID: Shu-Ying Rao, <uri xlink:href="https://orcid.org/0009-0007-7502-5877">orcid.org/0009-0007-7502-5877</uri>
</p>
</fn>
</author-notes>
<pub-date pub-type="epub">
<day>21</day>
<month>08</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="ecorrected">
<day>09</day>
<month>10</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="collection">
<year>2025</year>
</pub-date>
<volume>16</volume>
<elocation-id>1631050</elocation-id>
<history>
<date date-type="received">
<day>19</day>
<month>05</month>
<year>2025</year>
</date>
<date date-type="accepted">
<day>28</day>
<month>07</month>
<year>2025</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2025 Rao, Zheng, An, Feng, Su, Cheung, Ungvari, Ng, Xiang and Wang.</copyright-statement>
<copyright-year>2025</copyright-year>
<copyright-holder>Rao, Zheng, An, Feng, Su, Cheung, Ungvari, Ng, Xiang and Wang</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<sec>
<title>Background</title>
<p>Gender differences in COVID-19-related fear among mental health professionals (MHPs) have been inadequately studied. This study compared the gender differences in prevalence, correlates and network structure of COVID-19 fear among MHPs in China in the post-pandemic era.</p>
</sec>
<sec>
<title>Methods</title>
<p>A nationwide cross-sectional study was conducted between January 22 and February 10, 2023. Binary logistic regression was used to identify factors associated with COVID-19 fear. Expected Influence was used to identify the most central (influential) symptoms in gender-specific networks, while network comparison tests were conducted to assess the differences between male and female models.</p>
</sec>
<sec>
<title>Results</title>
<p>Among 7,199 MHPs, the overall prevalence of COVID-19 fear was 63.5% (95% CI: 62.3%-64.6%), with 61.7% (95% CI: 58.9%-64.4%) in males and 65.0% (95% CI: 63.7%-66.2%) in females. For male MHPs, factors associated with COVID-19 fear included having married status (OR: 1.39; 95% CI: 1.02&#x2013;1.90; P = 0.037), poorer economic status (poor vs. good: OR: 1.91; 95% CI: 1.23&#x2013;2.98; P = 0.004), more severe insomnia (OR: 1.04; 95% CI: 1.01&#x2013;1.07; P = 0.018), and depression (OR: 1.09; 95% CI: 1.05&#x2013;1.12; P &lt; 0.001). For female MHPs, the significant factors included having married status (OR: 1.21; 95% CI: 1.06&#x2013;1.37; P = 0.004), poorer economic status (poor vs. good: OR: 1.39; 95% CI: 1.11&#x2013;1.73; P = 0.004), more severe insomnia (OR: 1.05; 95% CI: 1.03&#x2013;1.06; P &lt; 0.001), depression (OR: 1.09; 95% CI: 1.08&#x2013;1.11; P &lt; 0.001), and quarantine experience (OR: 1.17; 95% CI: 1.04&#x2013;1.30; P = 0.006). Network analysis revealed that the most central symptom in the male network was FOC6 (sleep difficulties due to COVID-19 concerns), while the corresponding node in the female network model was FOC7 (palpitations when thinking about COVID-19).</p>
</sec>
<sec>
<title>Conclusion</title>
<p>COVID-19-related fear was more prevalent among female MHPs than males. Specific interventions targeting the central symptoms in each network should be implemented to alleviate COVID-19 fear effectively and improve the mental health of MHPs in the post-pandemic era.</p>
</sec>
</abstract>
<kwd-group>
<kwd>mental health professionals</kwd>
<kwd>gender difference</kwd>
<kwd>fear of COVID-19</kwd>
<kwd>network analysis</kwd>
<kwd>mental health</kwd>
</kwd-group>
<counts>
<fig-count count="3"/>
<table-count count="2"/>
<equation-count count="0"/>
<ref-count count="95"/>
<page-count count="12"/>
<word-count count="5746"/>
</counts>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-in-acceptance</meta-name>
<meta-value>Public Mental Health</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec id="s1" sec-type="intro">
<label>1</label>
<title>Introduction</title>
<p>In China, approximately 50% of the population had been infected COVID-19 by 2023 (<xref ref-type="bibr" rid="B1">1</xref>). The clinical impact of COVID-19 extends beyond the acute viral infection phase itself. Apart from physical symptoms, COVID-19 often leads to psychological problems and evokes widespread fear among the population. Fear associated with COVID-19 manifests across four dimensions: physical, interpersonal, cognitive and behavioral (<xref ref-type="bibr" rid="B2">2</xref>). The development of post-infection sequelae of COVID-19 (PISC), commonly referred to as Long COVID, is an emerging public health concern. Certain symptoms, such as sleep disturbances, myalgia, anosmia, dysgeusia, and headaches, often persist beyond several months, adversely impacting one&#x2019;s long-term mental health and quality of life (<xref ref-type="bibr" rid="B3">3</xref>, <xref ref-type="bibr" rid="B4">4</xref>). Therefore, it is crucial to examine the long-term influence of the pandemic on mental health among the population in the post-COVID era such as fear of COVID-19.</p>
<p>The mental health effects of the pandemic among mental health professionals (MHPs) have been previously studied (<xref ref-type="bibr" rid="B5">5</xref>&#x2013;<xref ref-type="bibr" rid="B7">7</xref>). A study conducted in the Netherlands found that 30% of MHPs in mental health institutions exhibited symptoms of depression, while 4.2% had considered leaving their positions during the pandemic (<xref ref-type="bibr" rid="B8">8</xref>). In China, MHPs predominantly comprise psychiatrists, nurses, or technicians, working in psychiatric hospitals or psychiatric departments of general hospitals (<xref ref-type="bibr" rid="B9">9</xref>). These professionals provide most of the mental health services in China (<xref ref-type="bibr" rid="B10">10</xref>). Notably, their clinical practice often occurs without specialized respiratory protection, a critical measure for COVID-19 prevention. Such resource limitation may exacerbate anxiety among MHPs when encountering potential COVID-19 cases. Previous studies on MHPs during the pandemic mostly focused on symptoms such as insomnia, anxiety and depression, and few of them addressed the prevalence and correlates of COVID-19-related fear, particularly in the post-COVID era. However, COVID-19 fear might indicate underlying mental health problems, for instance, a previous meta-analysis revealed that COVID-19 fear was significantly related with anxiety and depression, especially among healthcare professionals (HPs) (<xref ref-type="bibr" rid="B11">11</xref>).</p>
<p>Apart from the COVID-19 infection itself, the implementation of strict public health preventive measures during the pandemic might have prolonged adverse socio-occupational impacts on MHPs even in the post-pandemic era. These effects could have both psychological and physiological manifestations, such as depressive and anxiety symptoms, sleep disturbances, palpitations, and post-traumatic stress symptoms, with many HPs reporting persistent fear when recalling their experiences during the pandemic (<xref ref-type="bibr" rid="B12">12</xref>). Furthermore, the heightened prevalence of mental health problems among the population during and immediately after the pandemic would significantly increase the workload of MHPs, thereby exacerbating the risk of mental health problems including COVID-19 fear. Moreover, compared to most of other HPs, MHPs were exposed to patients&#x2019; pandemic-related trauma narratives even during the post-pandemic period, potentially leading to vicarious fear (<xref ref-type="bibr" rid="B13">13</xref>). Such sustained exposure might result in empathic overload that could affect their psychological well-being. Among the professional development skills for MHPs, the physiological consequences of emotion regulation might have unwanted impacts. Previous research indicated that emotion suppression strategies, often employed in clinical settings, might elevate diurnal cortisol secretion, a biomarker of chronic stress (<xref ref-type="bibr" rid="B14">14</xref>). Striving to suppress emotions could increase cortisol concentration, which might paradoxically amplify pandemic-related distress (<xref ref-type="bibr" rid="B15">15</xref>). Thus, understanding COVID-19-related fear among MHPs is crucial for addressing their mental well-being, professional competence, work satisfaction and retention, which might serve as a model for other HPs and ultimately benefiting patient care.</p>
<p>Previous research found gender differences in the prevalence and experience of long COVID, with females being more likely to report persistent symptoms compared to males (<xref ref-type="bibr" rid="B16">16</xref>). This might be attributed to females being more prone to experience symptoms of mood and hyper-arousal (<xref ref-type="bibr" rid="B17">17</xref>). However, previous findings have been inconsistent. Some studies reported that females had higher levels of COVID-19-related fear than males (<xref ref-type="bibr" rid="B18">18</xref>, <xref ref-type="bibr" rid="B19">19</xref>). Conversely, a study found that male HP expressed greater fear of COVID-19 than their female counterparts (<xref ref-type="bibr" rid="B20">20</xref>), while another study reported no significant gender differences in HPs during the pandemic (<xref ref-type="bibr" rid="B21">21</xref>). Such inconsistencies suggest that gender differences in COVID-19-related fear might vary across different populations and change over time. Therefore, it is important to further clarify gender differences in COVID-19-related fear.</p>
<p>It should be noted that most studies evaluated COVID-19 fear based solely on total scores of standard scales, weighing individual symptoms equally (<xref ref-type="bibr" rid="B22">22</xref>, <xref ref-type="bibr" rid="B23">23</xref>). However, this approach obscures the meaningful inter-relationships between individual symptoms and hinders the identification of influential symptoms that might play a more significant role in the maintenance of COVID-19 fear. To address this limitation, network analysis has been used as a novel statistical method that could examine how psychiatric symptoms interact with each other and identify the most central (influential) symptoms (<xref ref-type="bibr" rid="B24">24</xref>). Network models consist of nodes and edges; nodes represent psychiatric symptoms, while edges reflect their statistical associations (<xref ref-type="bibr" rid="B25">25</xref>). Using such an approach, it is possible to identify the most central nodes whose activation could directly influence nearby connected nodes (<xref ref-type="bibr" rid="B26">26</xref>), potentially preventing the exacerbation of certain disorders through targeted attention to these nodes (<xref ref-type="bibr" rid="B27">27</xref>). Network analysis concerning the COVID-19 fear have been applied to the study of the general population (<xref ref-type="bibr" rid="B28">28</xref>), as well as forensic healthcare workers (<xref ref-type="bibr" rid="B28">28</xref>).</p>
<p>There are several common limitations among previous studies. First, most have primarily focused on mental health issues, such as anxiety and depression, among MHPs, with limited attention given to COVID-19-related fear, particularly in the post-pandemic context. Second, gender differences in COVID-19-related fear remain poorly understood, with available findings showing inconsistencies across different populations. Third, previous research on COVID-19-related fear has predominantly relied on aggregate scale scores, neglecting symptom-level interactions, and thus failing to identify the most influential symptoms. To address these gaps, we compared gender differences in the prevalence, correlates and network structure of COVID-19-related fear among MHPs in the post-pandemic era.</p>
</sec>
<sec id="s2">
<label>2</label>
<title>Methods</title>
<sec id="s2_1">
<label>2.1</label>
<title>Study design and participants</title>
<p>The study assessed the mental health status of MHPs who experienced COVID-19 fear in the post-pandemic era. We conducted a cross-sectional national survey from January 22 to February 10, 2023, utilizing a snowball sampling method. A total of 7,199 MHPs across China participated in the survey. Following previous studies (<xref ref-type="bibr" rid="B29">29</xref>, <xref ref-type="bibr" rid="B30">30</xref>), we employed the &#x201c;Questionnaire Star&#x201d; program, which is widely used in epidemiological research in public mental health in China (<xref ref-type="bibr" rid="B31">31</xref>&#x2013;<xref ref-type="bibr" rid="B33">33</xref>). The program generated a Quick Response QR) Code for a specific questionnaire, enabling participants to scan and share the survey via WeChat, one of the most widely used social messaging platforms in China. Furthermore, WeChat served as a unique tool for reporting daily health status during the pandemic in China (<xref ref-type="bibr" rid="B34">34</xref>), ensuring widespread use among MHPs nationally.</p>
<p>Eligible participants were: (1) Individuals aged 18 years or older; (2) MHPs, including psychiatrists, nurses, or technicians, working in psychiatric hospitals or psychiatric departments of general hospitals across China; (3) Individuals who had recovered from COVID-19 by December 1, 2022; (4) MHPs with post-infection sequelae (PISC) of COVID-19; and (5) Individuals who could comprehend Chinese and provide electronic written informed consent. Exclusion criteria included: (1) Individuals with severe pre-existing health conditions (e.g., severe cardiovascular disease, respiratory disorders or immunosuppression) prior to the pandemic; (2) Pregnant individuals; and (3) MHPs who were not on duty during the COVID-19 pandemic. The Ethics Committee of Beijing Anding Hospital approved the study protocol (&#x201c;The Linshenwei No.: 26 in 2023&#x201d;), and all participants provided electronic informed consent.</p>
</sec>
<sec id="s2_2">
<label>2.2</label>
<title>Measures and assessments</title>
<p>The sociodemographic and health-related information of MHPs were collected, including age, gender, marital status, working years, living status, economic and health status, COVID-19 infection history, and quarantine experience. Fear-related symptoms of MHPs were evaluated with the validated Chinese version of the Fear of COVID-19 Scale (FCV-19S) (<xref ref-type="bibr" rid="B35">35</xref>, <xref ref-type="bibr" rid="B36">36</xref>), which consists seven items: (1) FOC1: Afraid of COVID-19 pandemic; (2) FOC2: Uncomfortable to think about COVID-19 pandemic; (3) FOC3: Clammy when thinking about COVID-19 pandemic; (4) FOC4: Afraid of losing life because of COVID-19 pandemic; (5) FOC5: Nervous when watching news about COVID-19 pandemic; (6) FOC6: Sleep difficulties caused by worried about COVID-19; (7) FOC7: Palpitation when thinking about COVID-19 pandemic. The FCV-19S items are categorized into two domains: physical response (FOC3, FOC6 and FOC7) and fear-related thoughts (FOC1, FOC2, FOC4 and FOC5). All seven items are rated on a 5-point Likert scale, with total scores ranging from 7 to 35, where higher scores indicate greater dread (<xref ref-type="bibr" rid="B36">36</xref>). Following the previous studies, a cutoff score of&#x2009;&#x2265;&#x2009;16 was used to define as having severe fear of COVID-19 pandemic (<xref ref-type="bibr" rid="B37">37</xref>, <xref ref-type="bibr" rid="B38">38</xref>).</p>
<p>Depressive symptoms were assessed with the validated Chinese version of the nine-item Patient Health Questionnaire (PHQ-9) (<xref ref-type="bibr" rid="B39">39</xref>, <xref ref-type="bibr" rid="B40">40</xref>). The PHQ-9 measures specific depression symptoms such as poor mood, loss of interest, and sleep disturbances (<xref ref-type="bibr" rid="B41">41</xref>). Each item is rated from 0 (not at all) to 3 (nearly every day), with a total score ranging from 0 to 27. A higher total score indicates a greater degree of depression.</p>
<p>Anxiety symptoms were measured using the validated Chinese version of the General Anxiety Disorder -7 (GAD-7) (<xref ref-type="bibr" rid="B42">42</xref>, <xref ref-type="bibr" rid="B43">43</xref>). The GAD-7 consists of seven items, and each item is scored from 0 (not at all) to 3 (nearly every day) with a total score ranging from 0 to 21. The items correspond to specific anxiety symptoms, including nervousness, anxiety, and excessive worry.</p>
<p>Insomnia symptoms were evaluated using the validated Chinese version of the Insomnia Severity Index (ISI-7) (<xref ref-type="bibr" rid="B44">44</xref>, <xref ref-type="bibr" rid="B45">45</xref>), which comprises seven items, and each is rated from 0 (no problem) to 4 (very severe problem). The total score ranges from 0 to 28, with higher scores indicating more severe insomnia symptoms. Each ISI-7 item assesses specific insomnia symptoms, such as difficulty falling asleep, maintaining sleep, and early awakenings.</p>
<p>These scales had satisfactory psychometric properties in studies among Chinese populations. In this study, the Cronbach&#x2019;s alpha values for the FCV-19S, PHQ-9, GAD-7 and ISI-7 were 0.93, 0.90, 0.93, 0.94, respectively.</p>
</sec>
<sec id="s2_3">
<label>2.3</label>
<title>Statistical analysis</title>
<p>All statistical analyses were performed using the R software program (Version 4.4.2) (<xref ref-type="bibr" rid="B46">46</xref>). The normality of distributions was assessed using Q-Q plot through the &#x201c;ggpubr&#x201d; package (Version 0.6.0) (<xref ref-type="bibr" rid="B47">47</xref>). Continuous variables that followed a normal distribution were compared between the groups with and without COVID-19 fear using t-test, while non-normally distributed variables were compared using Mann Whitney U test. Chi-square tests were employed for categorical variables. Additionally, a binary logistic regression was conducted to identify the factors that were significantly associated with COVID-19 fear in MHPs with statistically significant variables from the univariate analyses serving as independent variables. A two-tailed significance level of P &lt; 0.05 was used for all tests in this study.</p>
<p>In the network model, each node represented a specific COVID-19 fear-related symptom, with edges reflecting the associations between neighboring nodes. Thicker edges indicated stronger relationships between nodes. Green and red color edges represented positive and negative associations, respectively (<xref ref-type="bibr" rid="B48">48</xref>). We employed a Graphical Gaussian Model (GGM) to assess partial correlations among COVID-19 fear symptoms, establishing an undirected network model, thus enabling the exploration of nodes independently of causal relationships (<xref ref-type="bibr" rid="B49">49</xref>). To produce a sparse model, a graphic Least Absolute Shrinkage and Selection Operator (GLASSO) was applied with a tuning parameter of 0.5 to eliminate spurious edges by setting weak partial correlation coefficients to zero (<xref ref-type="bibr" rid="B50">50</xref>). Additionally, we used the Extended Bayesian Information Criterion (EBIC) to select the best-fitting network model (<xref ref-type="bibr" rid="B51">51</xref>). Following the EBICglasso procedure, the &#x201c;EstimateNetwork&#x201d; function from the &#x201c;bootnet&#x201d; package (Version 1.5.6) was used to identify the best-fit network model (<xref ref-type="bibr" rid="B49">49</xref>), while &#x201c;qgraph&#x201d; (Version 2.1.0) (<xref ref-type="bibr" rid="B48">48</xref>) and &#x201c;ggplot2&#x201d; (Version 3.5.0) (<xref ref-type="bibr" rid="B52">52</xref>) were used for network visualization.</p>
<p>To quantify the most important symptoms in the network, centrality indices were used. Specifically, we utilized the Expected Influence (EI), which rigorously evaluates the cumulative impact of the node&#x2019;s connections, accounting for both positive and negative edges (<xref ref-type="bibr" rid="B27">27</xref>) we used the package &#x201c;mgm&#x201d; (Version 1.2.0) (<xref ref-type="bibr" rid="B53">53</xref>) to compute the predictability, which indicates the internal connections between a node and its adjacent nodes.</p>
<p>The stability of the established network model was evaluated using the R-package &#x201c;bootnet&#x201d;, in which reliability of the weights of each edge in the network was assessed through the bootstrapped 95% confidence interval (95% CI). A narrow bootstrapped 95% CI that does not contain zero suggests a robust network (<xref ref-type="bibr" rid="B54">54</xref>). Further, centrality stability was assessed through case-dropping bootstrap and computing the correlation stability coefficient (CS-coefficient). A CS-coefficient above 0.25 indicates a stable result and a value exceeding 0.5 is preferred (<xref ref-type="bibr" rid="B49">49</xref>). Moreover, the significance of differences in edge weights and centrality indices was assessed through bootstrapped difference tests. More black boxes suggest that the strength of this connection remains consistent across multiple resampling, therefore the network structure is relatively stable (<xref ref-type="bibr" rid="B55">55</xref>).</p>
<p>To investigate potential differences in network characteristics between males and females, we conducted a network comparison test (NCT) using the package &#x201c;NetworkComparisonTest&#x201d; (Version 2.2.2) (<xref ref-type="bibr" rid="B56">56</xref>). The NCT method compares network structures based on three key components: network structure, edge strength and global strength. Specifically, network structure is assessed by comparing the distribution of edge weights, edge strength examines whether specific edges differ significantly between networks, and global strength represents the sum of all edge weights, thus providing insight into the overall connectivity of the networks (<xref ref-type="bibr" rid="B57">57</xref>).</p>
</sec>
</sec>
<sec id="s3" sec-type="results">
<label>3</label>
<title>Results</title>
<sec id="s3_1">
<label>3.1</label>
<title>Participant characteristics</title>
<p>From a total of 11,524 MHPs invited to participate in this study, 7,199 who met the study entry criteria were included for analyses. Demographic characteristics of the participants are summarized in <xref ref-type="table" rid="T1">
<bold>Table&#xa0;1</bold>
</xref>. Participants&#x2019; ages ranged from 18 to 66 years, with an average age of 35.1 years (SD = 8.3 years). Notably, over half had experienced at least a week of quarantine during the pandemic (n = 4,117; 57.2%).</p>
<table-wrap id="T1" position="float">
<label>Table&#xa0;1</label>
<caption>
<p>Demographic characteristics of mental health professionals with and without COVID-19 fear grouped by gender.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" rowspan="3" align="left">Variable</th>
<th valign="middle" rowspan="2" colspan="2" align="left">Total (N=7,199)</th>
<th valign="middle" colspan="4" align="left">Male</th>
<th valign="middle" rowspan="2" colspan="2" align="left">Univariate analyses</th>
<th valign="top" colspan="4" align="left">Female</th>
<th valign="top" rowspan="2" colspan="2" align="left">Univariate analyses</th>
</tr>
<tr>
<th valign="top" colspan="2" align="left">No-fear (N=442)</th>
<th valign="top" colspan="2" align="left">Fear (N=715)</th>
<th valign="top" colspan="2" align="left">No-fear (N=2,109)</th>
<th valign="top" colspan="2" align="left">Fear (N=3,933)</th>
</tr>
<tr>
<th valign="top" align="left">N</th>
<th valign="top" align="left">%</th>
<th valign="top" align="left">N</th>
<th valign="top" align="left">%</th>
<th valign="top" align="left">N</th>
<th valign="top" align="left">%</th>
<th valign="top" align="left">
<italic>&#x3c7;</italic>
<sup>2</sup>
</th>
<th valign="top" align="left">P</th>
<th valign="top" align="left">N</th>
<th valign="top" align="left">%</th>
<th valign="top" align="left">N</th>
<th valign="top" align="left">%</th>
<th valign="top" align="left">
<italic>&#x3c7;</italic>
<sup>2</sup>
</th>
<th valign="top" align="left">P</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">Married</td>
<td valign="top" align="left">5,302</td>
<td valign="top" align="left">73.6</td>
<td valign="top" align="left">256</td>
<td valign="top" align="left">57.9</td>
<td valign="top" align="left">478</td>
<td valign="top" align="left">66.9</td>
<td valign="top" align="left">9.4</td>
<td valign="top" align="left">
<bold>0.002</bold>
</td>
<td valign="top" align="left">1,554</td>
<td valign="top" align="left">73.7</td>
<td valign="top" align="left">3,014</td>
<td valign="top" align="left">76.6</td>
<td valign="top" align="left">6.4</td>
<td valign="top" align="left">
<bold>&lt;0.001</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">Living with others</td>
<td valign="top" align="left">6,464</td>
<td valign="top" align="left">89.8</td>
<td valign="top" align="left">374</td>
<td valign="top" align="left">84.6</td>
<td valign="top" align="left">604</td>
<td valign="top" align="left">84.5</td>
<td valign="top" align="left">0.004</td>
<td valign="top" align="left">0.949</td>
<td valign="top" align="left">1,908</td>
<td valign="top" align="left">90.5</td>
<td valign="top" align="left">3,578</td>
<td valign="top" align="left">91.0</td>
<td valign="top" align="left">0.4</td>
<td valign="top" align="left">0.518</td>
</tr>
<tr>
<td valign="top" align="left">At least 1-week quarantine during the COVID-19</td>
<td valign="top" align="left">4,116</td>
<td valign="top" align="left">57.2</td>
<td valign="top" align="left">263</td>
<td valign="top" align="left">59.5</td>
<td valign="top" align="left">432</td>
<td valign="top" align="left">60.7</td>
<td valign="top" align="left">0.09</td>
<td valign="top" align="left">0.757</td>
<td valign="top" align="left">1,114</td>
<td valign="top" align="left">52.8</td>
<td valign="top" align="left">2,307</td>
<td valign="top" align="left">58.7</td>
<td valign="top" align="left">19.0</td>
<td valign="top" align="left">
<bold>&lt;0.001</bold>
</td>
</tr>
<tr>
<th valign="top" colspan="15" align="left">Economic status</th>
</tr>
<tr>
<td valign="top" align="left">Poor</td>
<td valign="top" align="left">844</td>
<td valign="top" align="left">11.7</td>
<td valign="top" align="left">70</td>
<td valign="top" align="left">15.8</td>
<td valign="top" align="left">190</td>
<td valign="top" align="left">26.6</td>
<td valign="top" rowspan="3" align="left">25.2</td>
<td valign="top" rowspan="3" align="left">
<bold>&lt;0.001</bold>
</td>
<td valign="top" align="left">149</td>
<td valign="top" align="left">7.1</td>
<td valign="top" align="left">435</td>
<td valign="top" align="left">11.1</td>
<td valign="top" rowspan="3" align="left">40.1</td>
<td valign="top" rowspan="3" align="left">
<bold>&lt;0.001</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">Fair</td>
<td valign="top" align="left">5,963</td>
<td valign="top" align="left">82.8</td>
<td valign="top" align="left">334</td>
<td valign="top" align="left">75.6</td>
<td valign="top" align="left">496</td>
<td valign="top" align="left">69.4</td>
<td valign="top" align="left">1,811</td>
<td valign="top" align="left">85.9</td>
<td valign="top" align="left">3,322</td>
<td valign="top" align="left">84.5</td>
</tr>
<tr>
<td valign="top" align="left">Good</td>
<td valign="top" align="left">392</td>
<td valign="top" align="left">5.4</td>
<td valign="top" align="left">38</td>
<td valign="top" align="left">8.6</td>
<td valign="top" align="left">29</td>
<td valign="top" align="left">4.1</td>
<td valign="top" align="left">149</td>
<td valign="top" align="left">7.1</td>
<td valign="top" align="left">176</td>
<td valign="top" align="left">4.5</td>
</tr>
<tr>
<th valign="top" colspan="15" align="left">Health status</th>
</tr>
<tr>
<td valign="top" align="left">Poor</td>
<td valign="top" align="left">586</td>
<td valign="top" align="left">8.1</td>
<td valign="top" align="left">34</td>
<td valign="top" align="left">7.7</td>
<td valign="top" align="left">106</td>
<td valign="top" align="left">14.8</td>
<td valign="top" rowspan="3" align="left">25.2</td>
<td valign="top" rowspan="3" align="left">
<bold>&lt;0.001</bold>
</td>
<td valign="top" align="left">104</td>
<td valign="top" align="left">4.9</td>
<td valign="top" align="left">342</td>
<td valign="top" align="left">8.7</td>
<td valign="top" rowspan="3" align="left">54.9</td>
<td valign="top" rowspan="3" align="left">
<bold>&lt;0.001</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">Fair</td>
<td valign="top" align="left">5,422</td>
<td valign="top" align="left">75.3</td>
<td valign="top" align="left">297</td>
<td valign="top" align="left">67.2</td>
<td valign="top" align="left">499</td>
<td valign="top" align="left">69.8</td>
<td valign="top" align="left">1,588</td>
<td valign="top" align="left">73.7</td>
<td valign="top" align="left">3,038</td>
<td valign="top" align="left">77.2</td>
</tr>
<tr>
<td valign="top" align="left">Good</td>
<td valign="top" align="left">1,191</td>
<td valign="top" align="left">16.5</td>
<td valign="top" align="left">111</td>
<td valign="top" align="left">25.1</td>
<td valign="top" align="left">110</td>
<td valign="top" align="left">15.4</td>
<td valign="top" align="left">417</td>
<td valign="top" align="left">19.8</td>
<td valign="top" align="left">553</td>
<td valign="top" align="left">14.1</td>
</tr>
</tbody>
</table>
<table frame="hsides">
<thead>
<tr>
<th valign="top" align="left"/>
<th valign="top" align="left">M</th>
<th valign="top" align="left">SD</th>
<th valign="top" align="left">M</th>
<th valign="top" align="left">SD</th>
<th valign="top" align="left">M</th>
<th valign="top" align="left">SD</th>
<th valign="top" align="left">Z</th>
<th valign="top" align="left">P</th>
<th valign="top" align="left">M</th>
<th valign="top" align="left">SD</th>
<th valign="top" align="left">M</th>
<th valign="top" align="left">SD</th>
<th valign="top" align="left">Z</th>
<th valign="top" align="left">P</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">Age (years)</td>
<td valign="top" align="left">35.1</td>
<td valign="top" align="left">8.3</td>
<td valign="top" align="left">32.3</td>
<td valign="top" align="left">8.3</td>
<td valign="top" align="left">33.7</td>
<td valign="top" align="left">8.4</td>
<td valign="top" align="left">-2.8</td>
<td valign="top" align="left">
<bold>0.004</bold>
</td>
<td valign="top" align="left">35.3</td>
<td valign="top" align="left">8.1</td>
<td valign="top" align="left">35.6</td>
<td valign="top" align="left">8.2</td>
<td valign="top" align="left">-1.6</td>
<td valign="top" align="left">0.109</td>
</tr>
<tr>
<td valign="top" align="left">Working years</td>
<td valign="top" align="left">13.0</td>
<td valign="top" align="left">9.1</td>
<td valign="top" align="left">9.6</td>
<td valign="top" align="left">9.0</td>
<td valign="top" align="left">11.0</td>
<td valign="top" align="left">8.7</td>
<td valign="top" align="left">-3.6</td>
<td valign="top" align="left">
<bold>&lt;0.001</bold>
</td>
<td valign="top" align="left">13.4</td>
<td valign="top" align="left">9.1</td>
<td valign="top" align="left">13.6</td>
<td valign="top" align="left">9.0</td>
<td valign="top" align="left">-0.9</td>
<td valign="top" align="left">0.322</td>
</tr>
<tr>
<td valign="top" align="left">GAD-7 total</td>
<td valign="top" align="left">3.6</td>
<td valign="top" align="left">4.4</td>
<td valign="top" align="left">2.3</td>
<td valign="top" align="left">3.6</td>
<td valign="top" align="left">5.1</td>
<td valign="top" align="left">5.1</td>
<td valign="top" align="left">-8.2</td>
<td valign="top" align="left">
<bold>&lt;0.001</bold>
</td>
<td valign="top" align="left">1.9</td>
<td valign="top" align="left">3.1</td>
<td valign="top" align="left">4.3</td>
<td valign="top" align="left">4.6</td>
<td valign="top" align="left">-8.2</td>
<td valign="top" align="left">
<bold>&lt;0.001</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">ISI-7 total</td>
<td valign="top" align="left">7.1</td>
<td valign="top" align="left">5.5</td>
<td valign="top" align="left">5.5</td>
<td valign="top" align="left">5.2</td>
<td valign="top" align="left">8.9</td>
<td valign="top" align="left">6.5</td>
<td valign="top" align="left">-8.2</td>
<td valign="top" align="left">
<bold>&lt;0.001</bold>
</td>
<td valign="top" align="left">5.2</td>
<td valign="top" align="left">4.7</td>
<td valign="top" align="left">7.9</td>
<td valign="top" align="left">5.5</td>
<td valign="top" align="left">-8.2</td>
<td valign="top" align="left">
<bold>&lt;0.001</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">PHQ-9 total</td>
<td valign="top" align="left">6.3</td>
<td valign="top" align="left">5.5</td>
<td valign="top" align="left">4.7</td>
<td valign="top" align="left">4.9</td>
<td valign="top" align="left">8.3</td>
<td valign="top" align="left">6.3</td>
<td valign="top" align="left">-8.2</td>
<td valign="top" align="left">
<bold>&lt;0.001</bold>
</td>
<td valign="top" align="left">4.3</td>
<td valign="top" align="left">4.3</td>
<td valign="top" align="left">7.2</td>
<td valign="top" align="left">5.6</td>
<td valign="top" align="left">-8.2</td>
<td valign="top" align="left">
<bold>&lt;0.001</bold>
</td>
</tr>
</tbody>
</table>
<table frame="hsides">
<thead>
<tr>
<th valign="top" align="left"/>
<th valign="top" align="left">No-fear</th>
<th valign="top" align="left">Fear</th>
<th valign="top" align="left">
<italic>&#x3c7;</italic>
<sup>2</sup>
</th>
<th valign="top" align="left">P</th>
<th valign="top" align="left">&#xa0;</th>
<th valign="top" align="left">&#xa0;</th>
<th valign="top" align="left">&#xa0;</th>
<th valign="top" align="left">&#xa0;</th>
<th valign="top" align="left">&#xa0;</th>
<th valign="top" align="left">&#xa0;</th>
<th valign="top" align="left">&#xa0;</th>
<th valign="top" align="left">&#xa0;</th>
<th valign="top" align="left">&#xa0;</th>
<th valign="top" align="left">&#xa0;</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">Male</td>
<td valign="top" align="left">442</td>
<td valign="top" align="left">715</td>
<td valign="top" rowspan="2" align="left">4.6</td>
<td valign="top" rowspan="2" align="left">0.03</td>
<td valign="top" align="left"/>
<td valign="top" align="left"/>
<td valign="top" align="left"/>
<td valign="top" align="left"/>
<td valign="top" align="left"/>
<td valign="top" align="left"/>
<td valign="top" align="left"/>
<td valign="top" align="left"/>
<td valign="top" align="left"/>
<td valign="top" align="left"/>
</tr>
<tr>
<td valign="top" align="left">Female</td>
<td valign="top" align="left">2,109</td>
<td valign="top" align="left">3,933</td>
<td valign="top" align="left"/>
<td valign="top" align="left"/>
<td valign="top" align="left"/>
<td valign="top" align="left"/>
<td valign="top" align="left"/>
<td valign="top" align="left"/>
<td valign="top" align="left"/>
<td valign="top" align="left"/>
<td valign="top" align="left"/>
<td valign="top" align="left"/>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>Bolded values: &lt;0.05; M: mean; SD: standard deviation; GAD-7: the 7-item General Anxiety Disorder; ISI-7: the 7-item Insomnia Severity Index; PHQ-9: the 9-item Patient Health Questionnaire.</p>
</fn>
<fn>
<p>P-values are not adjusted for multiple comparisons.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3_2">
<label>3.2</label>
<title>Gender differences in the prevalence of COVID-19 fear in mental health professionals</title>
<p>The overall prevalence of COVID-19 fear (defined as a FCV-19S total score &#x2265; 16) among MHPs was 63.5% (n = 4,648; 95% CI: 62.3%-64.6%) Specifically, 61.7% (n=715, 95% CI: 58.9%-64.4%) of male participants and 65.0% (n=3,933, 95% CI: 63.7%-66.2%) of female participants reported fear of COVID-19. The gender difference of prevalence of COVID-19 fear reached a significance level (&#x3c7;<sup>2</sup> = 4.6; P = 0.03).</p>
</sec>
<sec id="s3_3">
<label>3.3</label>
<title>Independent correlates of COVID-19 fear among male and female MHPs</title>
<p>
<xref ref-type="table" rid="T1">
<bold>Table&#xa0;1</bold>
</xref> shows the demographic characteristics of MHPs with and those without COVID-19 fear by gender.</p>
<p>For males, compared with those without significant COVID-19 fear, MHPs with fear were more likely to be older (P = 0.004), working longer years (P &lt; 0.001), be married (P = 0.002) and reporting poorer economic (P &lt; 0.001) and health status (P &lt; 0.001). Additionally, they exhibited significantly higher mean scores on the PHQ-9 (P &lt; 0.001), GAD-7 (P &lt; 0.001) and ISI-7 (P &lt; 0.001) scales. In contrast, for females, compared with those without COVID-19 fear, MHPs with fear were more likely to be married (P &lt; 0.001), quarantined previously (P &lt; 0.001), and reporting worse economic (P &lt; 0.001) and health conditions (P &lt; 0.001). Similar to males, females with fear scored significantly higher on the PHQ-9 (P &lt; 0.001), GAD-7 (P &lt; 0.001), and ISI-7 (P &lt; 0.001) scales.</p>
<p>Binary logistic regression analysis revealed that, among male MHPs, being married was significantly associated with higher risk of COVID-19 fear (OR: 1.39; 95%CI: 1.02-1.90; P=0.037). Furthermore, having worse economic status (poor vs. good: OR: 1.91; 95%CI: 1.23-2.98; P=0.004) and more severe insomnia (OR:1.04; 95%CI: 1.01-1.07; P=0.018) and depression (OR:1.09; 95%CI: 1.05-1.12; P&lt;0.001) were significantly associated with more severe COVID-19 fear (<xref ref-type="table" rid="T2">
<bold>Table&#xa0;2</bold>
</xref>). Similarly, among female MHPs, having married status (OR: 1.21; 95% CI: 1.06-1.37; P = 0.004), worse economic status (poor vs. good: OR: 1.39; 95% CI: 1.11-1.73; P=0.004) and more severe insomnia (OR: 1.05; 95%CI: 1.03-1.06; P&lt;0.001) and depression (OR: 1.09; 95%CI: 1.08-1.11; P&lt;0.001) were significantly associated with more severe COVID-19 fear. In female but not in male MHPs, quarantine experience (OR: 1.17; 95%CI: 1.04-1.30; P=0.006) was significantly associated with a higher level of COVID-19 fear (<xref ref-type="table" rid="T2">
<bold>Table&#xa0;2</bold>
</xref>). GAD-7 was excluded in the multivariate analyses due to the multicollinearity between PHQ-9 and GAD-7 assessments.</p>
<table-wrap id="T2" position="float">
<label>Table&#xa0;2</label>
<caption>
<p>Independent correlates of COVID-19 fear among male and female mental health professionals.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" rowspan="3" align="center">Variables</th>
<th valign="middle" colspan="6" align="center">Fear of COVID-19(Y/N)</th>
</tr>
<tr>
<th valign="middle" colspan="3" align="center">Male</th>
<th valign="middle" colspan="3" align="center">Female</th>
</tr>
<tr>
<th valign="middle" align="center">P</th>
<th valign="middle" align="center">OR</th>
<th valign="middle" align="center">95%CI</th>
<th valign="middle" align="center">P</th>
<th valign="middle" align="center">OR</th>
<th valign="middle" align="center">95%CI</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="left">Married</td>
<td valign="middle" align="center">
<bold>0.037</bold>
</td>
<td valign="middle" align="center">1.39</td>
<td valign="middle" align="center">1.02-1.90</td>
<td valign="middle" align="center">
<bold>0.004</bold>
</td>
<td valign="middle" align="center">1.21</td>
<td valign="middle" align="center">1.06-1.37</td>
</tr>
<tr>
<td valign="middle" align="left">Age</td>
<td valign="middle" align="center">0.400</td>
<td valign="middle" align="center">1.02</td>
<td valign="middle" align="center">0.98-1.05</td>
<td valign="middle" align="center">&#x2013;</td>
<td valign="middle" align="center">&#x2013;</td>
<td valign="middle" align="center">&#x2013;</td>
</tr>
<tr>
<th valign="middle" align="left">Economic status</th>
<th valign="middle" align="center">&#x2013;</th>
<th valign="middle" align="center">&#x2013;</th>
<th valign="middle" align="center">&#x2013;</th>
<th valign="middle" align="center">&#x2013;</th>
<th valign="middle" align="center">&#x2013;</th>
<th valign="middle" align="center">&#x2013;</th>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;Poor</td>
<td valign="middle" align="center">
<bold>0.004</bold>
</td>
<td valign="middle" align="center">1.91</td>
<td valign="middle" align="center">1.23-2.98</td>
<td valign="middle" align="center">
<bold>0.004</bold>
</td>
<td valign="middle" align="center">1.39</td>
<td valign="middle" align="center">1.11-1.73</td>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;Fair</td>
<td valign="middle" align="center">&gt;0.900</td>
<td valign="middle" align="center">0.99</td>
<td valign="middle" align="center">0.75-1.30</td>
<td valign="middle" align="center">0.700</td>
<td valign="middle" align="center">0.97</td>
<td valign="middle" align="center">0.85-1.11</td>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;Good</td>
<td valign="middle" align="center">&#x2013;</td>
<td valign="middle" align="center">1.00</td>
<td valign="middle" align="center">&#x2013;</td>
<td valign="middle" align="center">&#x2013;</td>
<td valign="middle" align="center">1.00</td>
<td valign="middle" align="center">&#x2013;</td>
</tr>
<tr>
<th valign="middle" colspan="7" align="left">Health status</th>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;Poor</td>
<td valign="middle" align="center">0.600</td>
<td valign="middle" align="center">0.89</td>
<td valign="middle" align="center">0.60-1.32</td>
<td valign="middle" align="center">0.062</td>
<td valign="middle" align="center">0.82</td>
<td valign="middle" align="center">0.67-1.01</td>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;Fair</td>
<td valign="middle" align="center">0.700</td>
<td valign="middle" align="center">0.95</td>
<td valign="middle" align="center">0.75-1.21</td>
<td valign="middle" align="center">0.400</td>
<td valign="middle" align="center">0.95</td>
<td valign="middle" align="center">0.84-1.08</td>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;Good</td>
<td valign="middle" align="center">&#x2013;</td>
<td valign="middle" align="center">1.00</td>
<td valign="middle" align="center">&#x2013;</td>
<td valign="middle" align="center">&#x2013;</td>
<td valign="middle" align="center">1.00</td>
<td valign="middle" align="center">&#x2013;</td>
</tr>
<tr>
<td valign="middle" align="left">Working years</td>
<td valign="middle" align="center">0.600</td>
<td valign="middle" align="center">1.02</td>
<td valign="middle" align="center">0.96-1.02</td>
<td valign="middle" align="center">&#x2013;</td>
<td valign="middle" align="center">&#x2013;</td>
<td valign="middle" align="center">&#x2013;</td>
</tr>
<tr>
<td valign="middle" align="left">At least 1-week quarantine during the COVID-19</td>
<td valign="middle" align="center">&#x2013;</td>
<td valign="middle" align="center">&#x2013;</td>
<td valign="middle" align="center">&#x2013;</td>
<td valign="middle" align="center">
<bold>0.006</bold>
</td>
<td valign="middle" align="center">1.17</td>
<td valign="middle" align="center">1.04-1.30</td>
</tr>
<tr>
<td valign="middle" align="left">ISI-7 total</td>
<td valign="middle" align="center">
<bold>0.018</bold>
</td>
<td valign="middle" align="center">1.04</td>
<td valign="middle" align="center">1.01-1.07</td>
<td valign="middle" align="center">
<bold>&lt;0.001</bold>
</td>
<td valign="middle" align="center">1.05</td>
<td valign="middle" align="center">1.03-1.06</td>
</tr>
<tr>
<td valign="middle" align="left">PHQ-9 total</td>
<td valign="middle" align="center">
<bold>&lt;0.001</bold>
</td>
<td valign="middle" align="center">1.09</td>
<td valign="middle" align="center">1.05-1.12</td>
<td valign="middle" align="center">
<bold>&lt;0.001</bold>
</td>
<td valign="middle" align="center">1.09</td>
<td valign="middle" align="center">1.08-1.11</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>Bolded value: &lt;0.05; CI, confidential interval; OR, odds ratio.</p>
</fn>
<fn>
<p>P-values are unadjusted for multiple comparisons.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3_4">
<label>3.4</label>
<title>Network structure of COVID-19 fear</title>
<p>
<xref ref-type="fig" rid="f1">
<bold>Figures&#xa0;1</bold>
</xref> and <xref ref-type="fig" rid="f2">
<bold>2</bold>
</xref> illustrate the network structure and centrality indices of COVID-19 fear in male and female MHPs, respectively. In the male model, FOC6 (Sleep difficulties caused by worried about COVID-19 pandemic) had the highest EI value, whereas in the female network, FOC7 (Palpitation when thinking about COVID-19 pandemic) exhibited the highest centrality, indicating that these symptoms were the most influential in the respective models. Predictability is encoded in the outer ring&#x2019;s filled-to-blank area ratio, where the colored proportion reflects the magnitude of the EI value in <xref ref-type="fig" rid="f1">
<bold>Figure&#xa0;1</bold>
</xref>. The mean predictability of the seven nodes was 0.45 for males and 0.74 for female, indicating that an average of 45% and 74% of the variance in each node could be accounted for by its neighboring nodes in the respective models (<xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Table S1</bold>
</xref>).</p>
<fig id="f1" position="float">
<label>Figure&#xa0;1</label>
<caption>
<p>Network structure of COVID-19 fear among female and male mental health professionals.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpsyt-16-1631050-g001.tif">
<alt-text content-type="machine-generated">Network diagrams for male and female fear of COVID-19, showing nodes FOC1 to FOC7 with varying connection strengths. Legend describes each node, such as FOC1: Afraid of COVID-19, FOC7: Palpitation linked to thinking about COVID-19.</alt-text>
</graphic>
</fig>
<fig id="f2" position="float">
<label>Figure&#xa0;2</label>
<caption>
<p>The fear of COVID-19 centrality changes among female and male mental health professionals.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpsyt-16-1631050-g002.tif">
<alt-text content-type="machine-generated">Line graph showing EI Value (Z-Score) on the Y-axis against Node on the X-axis, comparing male and female data. Female data is represented with red squares and male data with cyan squares. Both lines fluctuate across nodes FOC1 to FOC7, with node FOC4 showing a steep decline and node FOC5 showing a sharp increase.</alt-text>
</graphic>
</fig>
</sec>
<sec id="s3_5">
<label>3.5</label>
<title>Network comparison tests by gender</title>
<p>
<xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3</bold>
</xref> presents the comparison of the network models between male and female MHPs. Significant differences were found in network global strength (P &lt; 0.001) and the distribution of edge weights (P = 0.031). Specifically, the female network was sparser than the male network.</p>
<fig id="f3" position="float">
<label>Figure&#xa0;3</label>
<caption>
<p>Network comparison.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpsyt-16-1631050-g003.tif">
<alt-text content-type="machine-generated">Histogram comparison shows two charts. The left chart displays the maximum of difference with a significant p-value of 0.031. The right chart shows the difference in global strength with a p-value less than 0.001. Both charts indicate frequency distributions, with notable differences marked by red triangles. Title: &#x201c;Network comparison between male and female."</alt-text>
</graphic>
</fig>
</sec>
<sec id="s3_6">
<label>3.6</label>
<title>Network stability and accuracy</title>
<p>In <xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Figure S1</bold>
</xref>, the left panel displays the bootstrapped 95% CIs for edge weights in the female network, indicating a reliable network with narrower intervals, thus signifying greater stability. Conversely, the intervals for the male network are wider, suggesting a less stable network than the female network, though still within acceptable limits. As shown in <xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Figure S2</bold>
</xref>, the CS-coefficients for both female and male EIs were 0.75, indicating that the EI structure of both networks remains unchanged even after removing 75% of the sample, thus confirming their stability. In addition, bootstrapped difference tests for centrality indices revealed that most boxes were grey in the male network, indicating that the confidence interval for the centrality indices in the bootstrap process contains zero, so the value might not be reliable enough. In contrast, most boxes were black in the female network, indicating that the female network was more stable (<xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Figure S3</bold>
</xref>). Regarding edge weight differences (as shown in <xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Figure S4</bold>
</xref>), in the male panel, more than half of the edges did not differ significantly from each other.</p>
</sec>
</sec>
<sec id="s4" sec-type="discussion">
<label>4</label>
<title>Discussion</title>
<p>To the best of our knowledge, this was the first study that compared COVID-19 fear between male and female MHPs with PISC using network analysis. The prevalence of COVID-19 fear in this study was 63.5% (95% CI: 62.3%-64.6%). Due to a lack of studies comparing the prevalence of COVID-19 fear among HPs in China, a direct comparison with similar studies was not possible. Previous research found that MHPs with COVID-19 fear were usually at an increased risk of burnout and secondary traumatic stress (<xref ref-type="bibr" rid="B58">58</xref>). Our study adds to this literature by examining the gender differences in COVID-19 fear among MHPs with PISC. The results revealed that female MHPs with PISC (65.0%, 95% CI: 63.7%-66.2%) exhibited a higher prevalence of COVID-19 fear compared to their male counterparts (61.7%, 95% CI: 58.9%-64.4%). The gender discrepancy is partly consistent with previous findings from Saudi Arabia and Belgium that female HPs reported higher levels of COVID-19-related anxiety than their male counterparts (<xref ref-type="bibr" rid="B59">59</xref>, <xref ref-type="bibr" rid="B60">60</xref>). Possible reasons for the gender difference might relate to females perceiving the COVID-19 as posing a higher risk to themselves and a greater threat to public health than males (<xref ref-type="bibr" rid="B61">61</xref>). Additionally, the hypothalamic-pituitary-adrenal axis in females might be more sensitive, potentially increasing their vulnerability to fear responses (<xref ref-type="bibr" rid="B62">62</xref>).</p>
<p>We found that having married status, insomnia, worse economic status, and depression were significantly associated with higher levels of COVID-19 fear in both genders, which aligns with previous studies on factors associated with the perceived COVID-19 fear among Brazilian college students, and the economic and societal influence of the COVID-19 lockdown on the general Japanese population (<xref ref-type="bibr" rid="B63">63</xref>, <xref ref-type="bibr" rid="B64">64</xref>). While being married generally enhanced mental health and coping abilities, married MHPs during the pandemic often faced domestic caregiving responsibilities while also managing substantial clinical workloads, thereby exacerbating their perceived vulnerability and pandemic-related fear (<xref ref-type="bibr" rid="B65">65</xref>). The association between insomnia and COVID-19 fear among MHPs might be mediated through several factors. Clinically, the need to manage challenging behaviors among patients, including those exhibiting acute psychiatric disturbances, could lead to prolonged occupational stress (<xref ref-type="bibr" rid="B66">66</xref>). This heightened stress response could result in increased serum cortisol levels and reduced melatonin synthesis, which might in turn disrupt sleep patterns, initiation, and maintenance, thereby exacerbating insomnia (<xref ref-type="bibr" rid="B67">67</xref>). Further, participants with depression were more susceptible to COVID-19 fear, as depressive symptoms might deplete emotional resources, impairing their ability to provide high-quality patient care (<xref ref-type="bibr" rid="B68">68</xref>). Additionally, female MHPs with past quarantine experience exhibited higher levels of COVID-19 fear, aligning with findings from dental professionals who reported that quarantine measures were associated with increased pandemic-related fear (<xref ref-type="bibr" rid="B69">69</xref>). This heightened fear among female MHPs might be partially attributed to the separate quarantine locations in hotels or hospitals, that were remote from their established social networks. Consequently, they might perceive significantly less social support from colleague and families that is a critical stress-coping mechanism for females, compared to males (<xref ref-type="bibr" rid="B70">70</xref>, <xref ref-type="bibr" rid="B71">71</xref>), thus amplifying their fear. Furthermore, quarantine restrictions might limit their ability to share domestic responsibilities with family members and exacerbate their psychological burden. In China, females usually had disproportionately more family and childcare duties (<xref ref-type="bibr" rid="B72">72</xref>) and their inability to fulfill these roles during quarantine could likely intensify their distress. Moreover, we found a significant association between lower economic status and heightened fear of COVID-19. A possible explanation might be related to economic vulnerability being a known predictor of poorer mental health outcomes (<xref ref-type="bibr" rid="B73">73</xref>). Specifically, MHPs in lower economic status might experience increased anxiety due to uncertainties about the future, which could amplify their fear of COVID-19.</p>
<p>Regarding the network comparison of COVID-19 fear between both genders, the overall structures were similar, but the global strengths differed significantly. Specifically, the female network model exhibited greater sparsity, which might be attributed to gender-specific variations in emotion regulation strategies. For instance, females tended to employ a broader range of emotion regulation strategies, including cognitive reappraisal, problem-solving, acceptance and distraction, compared to males (<xref ref-type="bibr" rid="B74">74</xref>). Such enhanced regulatory capacity might result in a more dispersed fear response among females, rather than a concentrated focus on specific fear-related items. Compared to their male counterparts, females tended to maintain broader, non-kin-centered social networks characterized by emotional communication and self-disclosure, which could enhance their access to social support (<xref ref-type="bibr" rid="B75">75</xref>). Such support could mitigate the association between specific aspects of fear, thereby weakening the overall connectivity within the female network model (<xref ref-type="bibr" rid="B76">76</xref>). Consequently, the global strength of internal connections in the female network model might be reduced.</p>
<p>In the male network, the most central symptom was &#x201c;Sleep difficulties caused by worried about COVID-19&#x201d; (FOC6), indicating its pivotal role within the network. This finding is consistent with previous research on COVID-19-related fear among fire service recruits (<xref ref-type="bibr" rid="B37">37</xref>). Sleep difficulties, defined as challenges in falling or staying asleep (<xref ref-type="bibr" rid="B77">77</xref>), represents a physical manifestation of fear and is associated with somatization symptoms. Such symptoms could significantly impair work performance and overall well-being (<xref ref-type="bibr" rid="B78">78</xref>). A nationwide study in China found that approximately 36.2% (95% CI: 35.0%&#x2013;37.0%) of MHPs experienced insomnia, which was likely exacerbated by heightened work pressure and increased risks of infection during the pandemic (<xref ref-type="bibr" rid="B79">79</xref>). Many MHPs had direct care responsibilities for COVID-19 patients despite having limited experience in infectious disease management, which likely intensified their fear and sleep disturbances that had persisted into the post-pandemic era. Male MHPs were disproportionately assigned to leadership positions and high-risk clinical duties during the pandemic, which often led to extended work hours and heightened work pressure (<xref ref-type="bibr" rid="B80">80</xref>). Such occupational stressors likely contributed to higher rates of work burnout among male compared to female MHPs, which might have disrupted their sleep patterns (<xref ref-type="bibr" rid="B81">81</xref>). Additionally, stigma often prevented males from seeking mental health care (<xref ref-type="bibr" rid="B82">82</xref>), which could lead to unaddressed psychological distress manifesting as somatic symptoms.</p>
<p>In the female network model, &#x201c;Palpitations when thinking about COVID-19&#x201d; (FOC7) emerged as the most central symptom, which is consistent with a previous network analysis of COVID-19 fear among Brazilian adults (<xref ref-type="bibr" rid="B83">83</xref>). Palpitations, defined as the perception of an abnormal heartbeat (<xref ref-type="bibr" rid="B84">84</xref>), are strongly associated with intense psychological distress such as fear (<xref ref-type="bibr" rid="B85">85</xref>). For instance, a study on female participants found that fear-induced activation of the sympathetic nervous system could elevate heart pump function and induce vasoconstriction, potentially triggering palpitations (<xref ref-type="bibr" rid="B86">86</xref>). Additionally, palpitations were among the most common symptoms reported in persons with long COVID (<xref ref-type="bibr" rid="B87">87</xref>). In China, the abrupt termination of China&#x2019;s Zero-COVID Policy and the subsequent surge in COVID-19 cases led to a sharp rise in hospital visits, overwhelming the healthcare systems and disrupting daily life (<xref ref-type="bibr" rid="B88">88</xref>). In this context, the ongoing uncertainty and fear of infection had likely aggravated psychological distress, which in turn contributed to persistent symptoms such as palpitations (FOC7). Further, MHPs of whom the majority were female nurses, often faced increased workload and night shifts, as reported in a study on occupational burnout among psychiatric medical staff (<xref ref-type="bibr" rid="B89">89</xref>). Night shifts could adversely affect the physical and mental health of MHPs, with palpitations emerging as a predominant concern, thus contributing to this being the most central symptom impacting COVID-19 fear, especially among female MHPs. In addition, research indicated greater emotional self-awareness in females compared with males, including the perception of internal bodily states (e.g., cardiac, respiratory, or gastrointestinal sensations) could be positively correlated with emotional processing capacity (<xref ref-type="bibr" rid="B90">90</xref>&#x2013;<xref ref-type="bibr" rid="B92">92</xref>). This increased sensitivity to physical-emotional integration might explain why female MHPs disproportionately reported palpitations during fear states, as they were more attuned to psychosomatic manifestations of distress (<xref ref-type="bibr" rid="B93">93</xref>).</p>
<p>The strengths of this study included the large sample size, the unique sample of MHPs and use of the novel statistical approach, network analysis, that could examine the gender-specific COVID-19 fear at a symptom level. Additionally, we explored the correlates across both genders, thereby providing valuable insights into gender differences in fear responses. However, several limitations should be noted. First, as a cross-sectional study, the network structure could not establish either the temporal or causal relationships between symptoms; therefore, longitudinal studies are needed to capture dynamic symptom changes over time. Second, the assessment of COVID-19 fear was based on self-report, which might introduce recall bias. Third, the data collected on quarantine exposure did not distinguish between different quarantine settings (e.g., home vs. centralized facilities), which might influence mental health outcomes. Future studies should capture such details to understand context-dependent effects. Third, the use of an online survey might result in selection bias, thus limiting the generalizability of the findings. Fourth, due to the exploratory nature of the study (<xref ref-type="bibr" rid="B94">94</xref>, <xref ref-type="bibr" rid="B95">95</xref>), the significance level was unadjusted for multiple comparisons, although this might potentially increase the risk of false positive results.</p>
<p>In conclusion, this network analysis found that COVID-19 fear was more common among female MHPs compared with male MHPs. Having married status, insomnia, worse economic status and depression were significantly associated with higher levels of COVID-19 fear in both genders. According to the network analysis, &#x201c;Sleep difficulties caused by worried about COVID-19&#x201d; (FOC6) was the most central symptom in the male network model while &#x201c;Palpitation when thinking about COVID-19&#x201d; (FOC7) was the most central symptom in the female model. Relevant interventions targeting such symptoms should be implemented in the post-pandemic era to alleviate COVID-19 fear effectively and improve the mental health of MHPs.</p>
</sec>
</body>
<back>
<sec id="s5" sec-type="data-availability">
<title>Data availability statement</title>
<p>The datasets presented in this article are not readily available because the Research Ethics Committee of Beijing Anding Hospital that approved the study prohibits the authors from making publicly available the research dataset of clinical studies. Requests to access the dataset should be addressed to the corresponding author.</p>
</sec>
<sec id="s6" sec-type="ethics-statement">
<title>Ethics statement</title>
<p>The studies involving humans were approved by the Ethics Committee of Beijing Anding Hospital. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.</p>
</sec>
<sec id="s7" sec-type="author-contributions">
<title>Author contributions</title>
<p>S-YR: Writing &#x2013; review &amp; editing, Writing &#x2013; original draft. M-RZ: Writing &#x2013; original draft. F-RA: Writing &#x2013; review &amp; editing. YF: Writing &#x2013; review &amp; editing. ZS: Writing &#x2013; review &amp; editing. TC: Writing &#x2013; review &amp; editing. GU: Writing &#x2013; review &amp; editing. CN: Writing &#x2013; review &amp; editing. Y-TX: Writing &#x2013; review &amp; editing. GW: Writing &#x2013; review &amp; editing.</p>
</sec>
<sec id="s8" sec-type="funding-information">
<title>Funding</title>
<p>The author(s) declare financial support was received for the research and/or publication of this article. The study was supported by Beijing High Level Public Health Technology Talent Construction Project (Discipline Backbone-01-028), the Beijing Municipal Science &amp; Technology Commission (No. Z181100001518005), the Capital&#x2019;s Funds for Health Improvement and Research (CFH 2024-2-1174) and the University of Macau (MYRG-GRG2023-00141-FHS; CPG2025-00021-FHS).</p>
</sec>
<ack>
<title>Acknowledgments</title>
<p>The authors are grateful to all participants and clinicians involved in this study.</p>
</ack>
<sec id="s9" sec-type="COI-statement">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec id="s12" sec-type="correction-note">
<title>Correction note</title>
<p>A correction has been made to this article. Details can be found at: <ext-link xlink:href="https://doi.org/10.3389/fpsyt.2025.1716267" ext-link-type="uri">10.3389/fpsyt.2025.1716267</ext-link>.</p>
</sec>
<sec id="s13" sec-type="ai-statement">
<title>Generative AI statement</title>
<p>The author(s) declare that no Generative AI was used in the creation of this manuscript.</p>
<p>Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.</p>
</sec>
<sec id="s14" sec-type="disclaimer">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
<sec id="s15" sec-type="supplementary-material">
<title>Supplementary material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/fpsyt.2025.1631050/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fpsyt.2025.1631050/full#supplementary-material</ext-link>.</p>
<supplementary-material xlink:href="SupplementaryFile1.docx" id="SM1" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document"/>
</sec>
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