Factors Associated with Suicidal Ideation Among Chinese Healthcare Workers Exposed to Workplace Violence: A Cross-Sectional Survey

Factors Associated with Suicidal Ideation Among Chinese Healthcare Workers Exposed to Workplace Violence: A Cross-Sectional Survey | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Factors Associated with Suicidal Ideation Among Chinese Healthcare Workers Exposed to Workplace Violence: A Cross-Sectional Survey Min Wu, Zejun Li, Xiaoyu Zhang, Qianjin Wang, Xin Wang, Huixue Xu, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6621801/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Workplace violence (WPV) against healthcare workers (HCWs) is a common global issue. However, research on the factors influencing suicidal ideation (SI) among HCWs who experience WPV is limited. This study aims to investigate the risk factors for SI among HCWs exposed to WPV. Methods: HCWs were recruited nationwide using snowball sampling. The Workplace Violence Scale (WVS) was used to assess WPV exposure, and a single-item question measured SI. Additionally, sociodemographic characteristics, workload, job satisfaction, burnout, depression, anxiety, stress, insomnia, alcohol abuse/dependence history, and psychiatric disorders were evaluated. Data analysis included descriptive statistics, chi-square tests, Wilcoxon rank-sum tests, and stepwise logistic regression. Results: Among 5086 participants, 53.0% of HCWs experienced some form of WPV. Of these, 44.8% faced verbal insults, 21.2% were threatened, 17.7% suffered physical attacks, 9.2% experienced verbal harassment, and 4.2% encountered sexual assault. The prevalence of SI was significantly higher among HCWs who experienced WPV (OR = 1.69, 95% CI [1.45–1.97]). In HCWs exposed to WPV, SI was significantly associated with high perceived stress, depression, anxiety, burnout, insomnia, history of mental illness, and professional role. Conclusion: WPV is common among HCWs in China, and those affected are more likely to experience SI, as well as emotional problems such as anxiety and depression. These emotional issues can impact mental health and may exacerbate SI. Preventing WPV and addressing its consequences are crucial. workplace violence burnout stress mental health occupational health suicidal ideation Figures Figure 1 Highlights 1. The incidence of workplace violence (WPV) in Chinese healthcare workers (HCWs) is high. 2. HCWs who experienced WPV were more likely to report suicidal ideation (SI). 3. History of mental illness, depression, anxiety, stress, insomnia, burnout, and professional role were independent risk factors for SI among HCWs exposed to WPV. 4. Improving WPV can reduce SI among HCWs and improve their psychiatric mental health. Factors Associated with Suicidal Ideation Among Chinese Healthcare Workers Exposed to Workplace Violence: A Cross-Sectional Survey Introduction Workplace violence (WPV) refers to incidents in which employees are subjected to abuse, threats, or physical assault in connection with their work, including during their commute. Such incidents pose explicit or implicit risks to the safety, well-being, or health of the individuals involved[1]. According to the World Health Organization, healthcare workers (HCWs) were 16 times more likely to experience WPV compared to professionals in other fields[2–4]. In China, WPV is also a significant issue for HCWs. HCWs face multiple pressures such as high-intensity work, fierce competition for promotion, tense doctor-patient relationship and insufficient social support for a long time, which leads to the frequent occurrence of WPV events[5, 6]. A meta-analysis of 47 observational studies revealed that the overall prevalence of WPV against HCWs was 62.4%, with verbal abuse being the most frequent (61.2%), followed by psychological violence (50.8%), threats (39.5%), physical violence (13.7%), and sexual harassment (6.3%)[7]. WPV in healthcare not only results in physical injuries[8] but also leads to serious psychological consequences, including anxiety and depression[4, 9]. These consequences negatively impact job performance and extend into personal lives and family relationships[9, 10], potentially leading to suicidal ideation (SI) or behaviors[11]. According to established models of suicide risk, contributing factors can be broadly categorized into internal and external domains. Internal factors mainly include mental health status and personality traits, whereas external factors mainly encompass cumulative life stressors or occupational stressors such as WPV, heavy workload, and lack of social support[12]. Mental health conditions such as depression, anxiety, and burnout have been identified as key predictors of SI among HCWs. WPV, in particular, has emerged as a critical external factor, as affected HCWs often exhibit elevated levels of depression, anxiety, and post-traumatic stress disorder (PTSD), all of which significantly increase suicide risk. Accumulating evidence from multiple regions confirms that WPV is a significant predictor of suicide risk among HCWs[13]. Given the high suicide risk among HCWs exposed to WPV, exploring the factors influencing SI in this population is of significant importance for the early prevention and intervention of SI. To our knowledge, although there have been some studies that have explored the association between violence and SI, there are no studies that have analyzed the factors influencing SI specifically among HCWs who have been exposed to violence[14–16]. To address this gap, we conducted a web-based cross-sectional study to (1) assess the prevalence of WPV and SI among HCWs, and (2) explore the factors influencing SI in HCWs experiencing WPV. Methods Study design and participants This study employed a cross-sectional survey design, utilizing an online questionnaire administered between November 2023 to June 2024. It is part of a comprehensive online investigation focused on the mental health of medical students and professionals in China[15, 17–19]. Participants were recruited on a nationwide scale through using the snowball sampling method. The inclusion criteria specified that participants must be: (1) Chinese nationals aged between 18 and 60 years; (2) currently employed as doctors or nurses within any healthcare institution in China, regardless of department, with a minimum of one year of clinical experience; and (3) capable to of comprehending the questionnaire and willing to participate voluntarily. The exclusion criteria included: (1) undergraduate medical students and healthcare workers employed overseas; (2) individuals who had resigned or retired; (3) those engaged in non-clinical roles, such as research; and (4) invalid questionnaire responses. Participation was both anonymous and voluntary, with All participants providing written informed consent and retaining the right to withdraw from the study at anytime. The study received ethical approval from the ethics committee of the Second Xiangya Hospital of Central South University (JY20200326). Sample size The calculation of sample size was conducted utilizing PASS version 15.0.5. A sample size of 2353 was determined to yield a two-sided 95% confidence interval (CI) with a width of 0.02, assuming a sample proportion of 0.4. This proportion was derived from a prior study, which indicated that 40% of Chinese medical trainees had experienced WPV[20]. Data collection and quality control The data were collected using the Chinese online survey platform Wenjuanxing ( www.wjx.cn ). A detailed description of the data collection process can be found in previous literature[19]. To ensure the validity of the responses, a set of criteria was employed to identify and exclude invalid submissions: (1) incorrect answers to a control question ("When is Chinese National Day?") or inconsistent responses to repeated questions; (2) uniformity of answers across all questions; and (3) logical inconsistencies, such as contradictions between related questions or responses that contravened common sense. Furthermore, each IP address and WeChat account was limited to a single submission to prevent duplicate entries from the same participant. Sociodemographic and work-related characteristics The sociodemographic variables evaluated in this study encompassed age, gender, marital status, educational attainment, and a history of mental health disorders. The investigation of work-related factors included the participants' professional roles (either as doctors or nurses), professional titles, workload, work-life balance, and job satisfaction. Workload was assessed using the Role Overload Items scale,[21] which comprises five items rated on a five-point Likert scale (1 = strongly disagree, 5 = strongly agree). The comulative score ranges from 5 to 25, with elevated scores indicating a higher perceived workload. Participants were divided into two groups based on the median score: individuals scoring above the median were designated as the high workload group. Work-life balance was measured using with a single five-point Likert item from the Well-Being Index (WBI): “My work schedule leaves me enough time for my personal/family life.” Participants who expressed disagreement or strongly disagreement with this statement were categorized as experiencing "poor" work-life balance. Job satisfaction was assessed using a five-point Likert scale, with participants indicating dissatisfaction or strong dissatisfaction being classified as "dissatisfied." This approach of dichotomizing collapsing responses from a multi-category Likert scale into two categories is widely recognized for the presentation of such data[22]. Workplace violence The Workplace Violence Scale (WVS)[23, 24] was employed to evaluate healthcare workers (HCWs') experiences of WPV. The scale consists of five dimensions, each represented by a single item, resulting in a total of five items: physical assault (PA), verbal abuse (VA, or emotional abuse), threats, verbal sexual harassment (VSH), and sexual assault (SA). Each item is rated on a four-point scale that reflecting the frequency of the violence experienced. For instance, one item inquires: "In the past 12 months, have you experienced emotional abuse at work, such as verbal insults, humiliation, or yelling? (0 = never, 1 = once, 2 = 2–3 times, 3 = ≥ 4 times)." The overall scale score is determined by summing the scores for each item, yielding a range from 0 to 15, where higher scores denote a greater frequency of WPV. In this study, a total score of ≥ 1 was considered indicative of participants having experienced at least one form of WPV in the past year. Suicidal ideation A single-item question from the US National Comorbidity Survey[25] was employed to evaluate suicidal ideation (SI) over the preceding year: "In the past 12 months, have you seriously considered attempting suicide?" Participants were instructed to respond with either "yes" or "no." This question has been extensively utilized in various studies to assess SI among both medical professionals[26–28] and the general population[29, 30]. Mental distress Mental distress was defined to include burnout, depression, anxiety, perceived stress, insomnia and alcohol abuse or dependence. The Chinese version of the Patient Health Questionnaire-9 (PHQ-9)[31], the Generalised Anxiety Disorder scale (GAD-7)[32], and the Alcohol Use Disorders Identification Test Version C (AUDIT-C)[19] were utilized to assess depression, anxiety, and alcohol abuse or dependence, respectively. A detailed description of these instruments can be found in previous literature[19]. The Oldenburg Burnout Inventory (OLBI)[33, 34] was utilized to measure occupational burnout. This instrument comprises of 16 items that assess two primary dimensions: exhaustion and disengagement. Each item is rated on a 4-point Likert scale, with total scores rangeing from 16 to 64, where higher scores denote more severe burnout. The Chinese version of the OLBI has demonstrated robust reliability and validity within nurse populations[35]. In this study, participants were considered to have burnout if their scores on both the exhaustion and disengagement dimensions exceeded the respective median scores for each dimension. The Perceived Stress Scale (PSS-4)[36] was used to measure participants' subjective stress perception over the preceding month. This scale is widely used in research and has demonstrated acceptable reliability and validity[37, 38]. It is scored on a 5-point scale from 0 (never) to 4 (very often),with total scores ranging from 0 to 16, where higher scores indicate increased levels of perceived stress. Based on existing literature, participants with a PSS-4 total score of ≥ 6 were categorized as experiencing high-stress group[39]. The Insomnia Severity Index (ISI)[40] was employed to evaluate the severity of insomnia symptoms as perceived by participants over the preceding two weeks. This instrument comprises seven items, each rated on a 5-point Likert scale ranging from 0 to 4, resulting in a total score spectrum from 0 to 28, where elevated scores denote severe insomnia. The Chinese version of the ISI has been validated, demonstrating robust psychometric properties[41]. Within the context of this study, a total ISI score of 15 or greater was deemded indicative of clinical insomnia[40]. Statistical analysis The normality of the variables was evaluated through quantile-quantile (Q-Q) plots and the Kolmogorov-Smirnov test. Continuous variables that did not follow a normal distribution were reported as medians accompanied by interquartile ranges (IQR: 1st quartile, 3rd quartile), while categorical data were described using frequencies and percentages. To compare differences between groups, Pearson’s chi-square test and the Wilcoxon rank-sum test were utilized as appropriate. Subsequently, stepwise logistic regression analysis (using the "both" selection method) was conducted to identify predictors of SI among HCWs experiencing WPV. The logistic regression model incorporated variables that demonstrated a significance level of p < 0.20 in the intergroup difference analysis. Multi-collinearity among the independent variables was examined using the variance inflation factor (VIF), with VIF values exceeding 5 indicating the presence of multi-collinearity. Additionally, Receiver Operating Characteristic (ROC) curves were plotted, and the Area Under the Curve (AUC) was calculated to assess the model's ability to discriminate between participants experiencing WPV with and without SI. AUC values ranging from 0.7 to 0.8 were deemed acceptable[19]. All statistical analyses were conducted using R software (version 4.3.2). All tests were two-tailed, with the significance level set at 0.05. Results Participants’ characteristics and the prevalence of mental distress and WPV Of the 5,385 questionnaires returned, 5,086 were deemed valid and subsequently included in the final analysis, resulting in an efficiency rate of 94.4%. The sociodemographic, occupational, and mental heatlh characteristics of the participants are comprehensively presented in Table 1 . Among respondents, 1,483 (29.2%) were male and 3,603 (70.8%) were female, with a median age of 31 years (IQR, 26–38). The majority of participants were married (2,903, 57.1%). A total of 858 (16.9%) HCWs possessed a master's degree or higher, and 306 (6.0%) reported a history of mental disorders. Regarding professional roles, 3,731 (73.4%) were physicians and 1,355 (26.6%) were nurses, with 2,261 (44.5%) holding an intermediate or senior title. Approximately half of the participants (2,310, 45.4%) reported experiencing a high workload. Over half (2,660, 52.3%) of the HCWs reported a lack of work-life balance, and 1,360 (26.7%) expressed job dissatisfaction. Table 1 Participant characteristics and factors associated with workplace violence among healthcare workers: results of inter-group differences tests Characteristic Overall* Without WPV exposure * With WPV exposure* P value** Total 5,086 (100%) 2,391 (47.0%) 2,695 (53.0%) Age 31 (26, 38) 32 (27, 39) 31 (26, 38) 0.001 Sex 0.093 Female 3,603 (70.8%) 1,721 (72.0%) 1,882 (69.8%) Male 1,483 (29.2%) 670 (28.0%) 813 (30.2%) Marital status < 0.001 Single 2,043 (40.2%) 886 (37.1%) 1,157 (42.9%) Married 2,903 (57.1%) 1,445 (60.4%) 1,458 (54.1%) Divorced or widowed 140 (2.8%) 60 (2.5%) 80 (3.0%) Education level 0.2 Associate degree or vocational diploma 641 (12.6%) 322 (13.5%) 319 (11.8%) Bachelor's degree 3,587 (70.5%) 1,666 (69.7%) 1,921 (71.3%) Master's or doctoral degree 858 (16.9%) 403 (16.9%) 455 (16.9%) History of mental illness 306 (6.0%) 107 (4.5%) 199 (7.4%) < 0.001 Professional role < 0.001 Physician 3,731 (73.4%) 1,696 (70.9%) 2,035 (75.5%) Nurse 1,355 (26.6%) 695 (29.1%) 660 (24.5%) Professional title 0.4 Junior level 2,825 (55.5%) 1,313 (54.9%) 1,512 (56.1%) Intermediate or senior level 2,261 (44.5%) 1,078 (45.1%) 1,183 (43.9%) High workload 2,310 (45.4%) 839 (35.1%) 1,471 (54.6%) < 0.001 Poor work-life balance 2,660 (52.3%) 1,049 (43.9%) 1,611 (59.8%) < 0.001 Job dissatisfaction 1,360 (26.7%) 437 (18.3%) 923 (34.2%) < 0.001 High perceived stress 3,842 (75.5%) 1,654 (69.2%) 2,188 (81.2%) < 0.001 Depression symptoms 1,529 (30.1%) 533 (22.3%) 996 (37.0%) < 0.001 Anxiety symptoms 901 (17.7%) 302 (12.6%) 599 (22.2%) < 0.001 Burnout 1,840 (36.2%) 626 (26.2%) 1,214 (45.0%) < 0.001 Clinical insomnia 788 (15.5%) 258 (10.8%) 530 (19.7%) < 0.001 Alcohol abuse/dependence 904 (17.8%) 368 (15.4%) 536 (19.9%) < 0.001 Suicidal ideation 1,153 (22.7%) 363 (15.2%) 790 (29.3%) < 0.001 Workplace violence experience 2,695 (53.0%) - 2,695 (100%) - Verbal abuse 2,278 (44.8%) - 2,278 (84.5%) - Threats 1,076 (21.2%) - 1,076 (39.9%) - Physical assault 900 (17.7%) - 900 (33.4%) - Verbal sexual harassment 467 (9.2%) - 467 (17.3%) - Sexual assault 212 (4.2%) - 212 (7.9%) - * Median (IQR); n (%). ** Wilcoxon rank sum test; Pearson’s Chi-squared test; Fisher’s exact test. WPV = workplace violence. The study identified the prevalence of several mental health conditions among Chinese HCWs, including burnout (1,840, 36.2%), depressive symptoms (1,529, 30.1%), anxiety symptoms (901, 17.7%), clinical insomnia (788, 15.5%), high perceived stress (3,842, 75.5%), and alcohol abuse/dependence (904, 17.8%). In relation to WPV, 2,695 HCWs, representing 53.0% of the sample, reported encountering some form of violence. Specifically, 2,278 (44.8%) experienced verbal abuse, 1,076 (21.2%) faced threats, 900 (17.7%) were subjected to physical assault, 467 (9.2%) encountered verbal sexual harassment, and 212 (4.2%) experienced sexual assault (Table 1 ). Prevalence of SI among HCWs exposed to vs. non-exposed to WPV Among total 5,086 HCWs, 1153 (22.7%) reported experiencing SI within the past 12 months. The prevalence of SI was significantly higher among HCWs who had experienced WPV (790 out of 2,695, 29.3%) compared to those who had not (363 out of 2,391, 15.2%) (p < 0.001). This disparity remained statistically significant after adjusting for all other characteristics listed in Table 1 ,with an odds ratio (OR) of 1.69, and a 95% confidence interval (CI) of 1.45–1.97. Comparison of characteristics of between HCWs exposed to vs. non-exposed to WPV Compared to their counterparts who had not experienced WPV, those who had were more likely to be younger, unmarried, physicians, and to have a history of mental disorders (all p ≤ 0.001). Additionally, they were more likely to report a high workload, poor work-life balance, and job dissatisfaction (all p < 0.001). Furthermore, HCWs who were exposed to WPV exhibited had significantly higher prevalence rates of burnout, clinical insomnia, perceived stress, depressive symptoms, anxiety symptoms, and alcohol abuse/dependence (all p 0.05) (Table 1 ). The related factors of SI among HCWs exposed to WPV The analysis of intergroup differences among HCWs who experienced WPV indicated that SI was significantly associated with factors such as age, marital status, history of mental disorders, professional title, high workload, poor work-life balance, job dissatisfaction, perceived stress, depressive symptoms, anxiety symptoms, burnout, and clinical insomnia (all p < 0.001), in addition to professional role (p = 0.016), as detailed in Table 2 . Subsequently, a stepwise logistic regression was conducted (Fig. 1 ). The results from both the Hosmer-Lemeshow goodness-of-fit test (χ² = 4.49, p = 0.72) and the Omnibus test (χ² = 437.0, p < 0.001) demonstrated that the model was well-fitted. In HCWs who had encountered WPV, SI exhibited a significantly association with elevated perceived stress (OR: 1.83, 95% CI: 1.35–2.52, p < 0.001), symptoms of depression (OR: 2.06, 95% CI: 1.64–2.59, p < 0.001), symptoms of anxiety (OR: 1.61, 95% CI: 1.26–2.05, p < 0.001), burnout (OR: 1.79, 95% CI: 1.46–2.19, p < 0.001), clinical insomnia (OR: 1.35, 95% CI: 1.07–1.71, p = 0.011), a history of mental illness (OR: 2.39, 95% CI: 1.73–3.30, p < 0.001), and being a nurse as opposed to a. Physician (OR: 1.38, 95% CI: 1.12–1.70, p = 0.003). Furthermore, the ROC curve depicted in Figure S1 illustrates the efficacy of this regression model in differentiating between participants who had experienced WPV with and without SI, yielding an acceptable AUC of 0.74 (95% CI: 0.72–0.76, p < 0.001). Table 2 Participant characteristics and factors associated with suicidal ideation among healthcare workers exposed to workplace violence: results of inter-group differences tests Characteristic Overall N = 2,695* Without SI N = 1,905 * With SI N = 790* P value** Age 31 (26, 38) 32 (26, 39) 30 (26, 35) < 0.001 Sex 0.3 Female 1,882 (69.8%) 1,319 (69.2%) 563 (71.3%) Male 813 (30.2%) 586 (30.8%) 227 (28.7%) Marital status < 0.001 Single 1,157 (42.9%) 767 (40.3%) 390 (49.4%) Married 1,458 (54.1%) 1,077 (56.5%) 381 (48.2%) Divorced or widowed 80 (3.0%) 61 (3.2%) 19 (2.4%) Education level 0.5 Associate degree or vocational diploma 319 (11.8%) 218 (11.4%) 101 (12.8%) Bachelor's degree 1,921 (71.3%) 1,359 (71.3%) 562 (71.1%) Master's or doctoral degree 455 (16.9%) 328 (17.2%) 127 (16.1%) History of mental illness 199 (7.4%) 86 (4.5%) 113 (14.3%) < 0.001 Professional role 0.016 Physician 2,035 (75.5%) 1,463 (76.8%) 572 (72.4%) Nurse 660 (24.5%) 442 (23.2%) 218 (27.6%) Professional title < 0.001 Junior level 1,512 (56.1%) 1,019 (53.5%) 493 (62.4%) Intermediate or senior level 1,183 (43.9%) 886 (46.5%) 297 (37.6%) High workload 1,471 (54.6%) 958 (50.3%) 513 (64.9%) < 0.001 Poor work-life balance 1,611 (59.8%) 1,081 (56.7%) 530 (67.1%) < 0.001 Job dissatisfaction 923 (34.2%) 546 (28.7%) 377 (47.7%) < 0.001 High perceived stress 2,188 (81.2%) 1,456 (76.4%) 732 (92.7%) < 0.001 Depression symptoms 996 (37.0%) 510 (26.8%) 486 (61.5%) < 0.001 Anxiety symptoms 599 (22.2%) 272 (14.3%) 327 (41.4%) < 0.001 Burnout 1,214 (45.0%) 695 (36.5%) 519 (65.7%) < 0.001 Clinical insomnia 530 (19.7%) 262 (13.8%) 268 (33.9%) < 0.001 Alcohol abuse/dependence 536 (19.9%) 361 (19.0%) 175 (22.2%) 0.058 *Median (IQR); n (%). **Wilcoxon rank sum test; Pearson’s Chi-squared test; Fisher’s exact test. SI = suicidal ideation. Discussion This study investigated the factors influencing SI among HCWs exposed to WPV. Our findings revealed that: 1) 53.0% of HCWs encountered WPV in their clinical practice; 2) HCWs who experienced WPV were more likely to report SI compared to those who had not been exposed to WPV; and 3) a history of mental illness, depressive symptoms, anxiety symptoms, perceived stress, clinical insomnia, burnout, and professional role were identified as independent risk factors for SI among HCWs exposed to WPV. HCWs are at high risk of experiencing WPV in their clinical work, and the overall incidence of WPV suffered by HCWs in this study was 53.0%, with Verbal abuse being the most common form of violence[1, 42–44], which is consistent with previous studies[43–45]. There may be many reasons for the higher incidence of WPV. first, the tension of healthcare resources[42, 44, 46], especially the concentration of patients in large hospitals, which leads to an increased workload for doctors, long waiting time, and dissatisfaction of patients; second, the lack of communication between doctors and patients[47, 48], and the lack of patients' understanding, which makes them prone to misunderstandings; and third, the insufficient penalties for violent behavior in the existing law, which fails to provide an effective deterrent[49]. Therefore, we propose some recommendations to help reduce the occurrence and risk of WPV. First, HCWs should strengthen communication with patients to reduce the possibility of misunderstanding and conflict. Second, the relevant departments should also further improve the relevant laws and regulations and increase the penalties for violence against doctors. SI was significantly higher among HCWs who experienced WPV than those who did not, and experienced higher levels of psychological stress[50]. A study in China found a statistically significant correlation between WPV and SI among 3426 HCWs[11]. Environmental influences become stressors when they are perceived by individuals as threatening and beyond their ability to cope[51]. According to the stress theory of suicide[52], stressful events usually precede psychopathological symptoms and suicidal behavior. It can lead to a range of mental health problems such as anxiety, depression, burnout, and insomnia, which can increase the risk of SI.[53, 54]. This study strongly encourages reducing WPV and keeping HCWs safe. This can be accomplished through deterrent government laws, developing communication strategies between HCWs and patients, increasing the number of HCWs to face these situations, and increasing public awareness[50, 55, 56]. This study found that perceived stress, burnout, and insomnia were identified as independent risk factors for SI among HCWs reporting WPV. The results of previous studies also suggest that burnout, and individual perceived stress is a significant correlate of SI among HCWs [26, 57, 58]. Burnout includes emotional exhaustion, depersonalization and low personal fulfillment[59], may be a predictor of hopelessness, which in turn is a significant indicator of suicide risk[60]. A longitudinal study of U.S. medical students[61] found that improving burnout helped reduce the risk of SI. This suggests that screening for and intervening in burnout can reduce the risk of SI. When individuals perceive environmental influences as threatening and beyond their ability to cope, these influences become stressors[51]. According to the stress theory of suicide[52], stressful events usually precede psychopathological symptoms and suicidal behavior. When individuals are in a vulnerable state, the stress experienced is more likely to translate into SI[62]. Stresses continue to accumulate and these may eventually internalize into depressive symptoms and further induce SI[63]. Previous studies have found[64–66] that individuals with sleep problems may be at higher risk for SI[67], which is consistent with the univariate analysis of this study's results. Underlying neurobiological factors may explain this relationship to some extent. Insomnia has been found to desensitize the serotonin IA receptor system in rats[68], and a number of studies have linked 5-hydroxytryptamine dysregulation to past suicide attempts, future suicide predictions, and suicide deaths[69]. Thus, insomnia may play an important role in the development of suicidal behavior and be a potential target for interventions aimed at suicide prevention[70]. This study and a large number of previous studies have shown that anxiety, depressive symptoms, and history of mental illness were associated with an increased risk of SI among HCWs[58, 71–75]. A large body of evidence suggests that depressive symptoms are one of the major risk factors predicting suicide-related behaviors in various populations[26, 71, 76]. Individuals with anxiety symptoms may exhibit cognitive dissonance and a sense of hopelessness when dealing with internal and external stimuli, which may further trigger depressive mood and prompt individuals to adopt inappropriate coping strategies such as suicide. Therefore, healthcare organizations should strengthen the identification of depression and anxiety symptoms in HCWs, conduct regular psychological assessments, and identify high-risk groups in a timely manner. In addition, among HCWs who experienced WPV, professional role was an independent risk factor for SI, and SI was higher among physicians than among nurses. This is different from the results of some previous studies. For example, a study in the United States found a significantly higher incidence of suicide among nurses than among physicians[77]. Further analysis is needed in the future by adding more nurses to the number of studies. Limitations Despite some important findings of this study, there are still some limitations. There are several limitations of this study. First, cross-sectional studies reveal the state of the study population at a particular moment in time, or the relationship between different variables at a particular moment in time, and do not explain the causal relationship between variables. Second, all data were based on self-report questionnaires, which may introduce response biases such as skipped questions, incomplete and non-disclosure of information, and uncertainty about the timing of the questionnaires; therefore, more discriminating measurement tools are needed to study these issues more objectively. Third, we collected data on whether physicians had experienced WPV in the past 12 months, so there may be recall bias in the results, which could lead to an underestimation of the magnitude of WPV. Conclusion In summary, WPV is not uncommon among HCWs in China, and its impact is far-reaching enough to warrant adequate attention. HCWs exposed to WPV are at higher risk of SI. In HCWs exposed to WPV, SI is influenced by multiple factors, including anxiety, depression, burnout, insomnia, history of mental illness and professional role. These negative emotions not only directly affect mental health, but may also further exacerbate SI. The implementation of early detection and interventions at the individual physician, healthcare system, and external regulatory agency levels is critical to improving the mental health of HCWs and preventing suicide. Declarations Acknowledgments We show the greatest gratitude to all the participants and volunteers who helped deliver the online questionnaires. Funding Statement This study was supported by National Natural Science Foundation of China (Grant No. 82371501 to T.L.) and The Regional Innovation and Development Joint Fund of the National Natural Science Foundation (Grant No. U22A20302 to T.L.). These sources had no further role in this study design, in the data collection and analysis, in the report's writing, and in the decision to submit the paper for publication. CRediT authorship contribution statement Min Wu and Zejun Li have contributed equally to this work and share their first authorship. Xiaoyu Zhang, Qianjin Wang, Xin Wang, Huixue Xu, Yueheng Liu, Manyun Li, Wenjing Yuan, and Hanrui Peng were responsible for data collection. Data analysis and interpretation were performed by Zejun Li. The initial draft of the manuscript was prepared by Min Wu and Zejun Li and critically revised by Tieqiao Liu, Qijian Deng,, and Qiuxia Wu. All co-authors revised and agreed to publish the final version of the manuscript. Ethics approval and consent to participate The study was approved by the ethics committee of the Second Xiangya Hospital of Central South University (JY20200326). The purpose of the study, provision, protection of personal information, and withdrawal of consent were explained to participants before participating. Written informed consent was obtained from study participants, including consent to publish the findings as a paper. All methods were carried out following the Declaration of Helsinki ethical guidelines. Disclosure of interest The authors declare that they have no conflict of interest. Consent for publication Not applicable. Data availability The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. References Liu J, Gan Y, Jiang H, Li L, Dwyer R, Lu K, Yan S, Sampson O, Xu H, Wang C et al : Prevalence of workplace violence against healthcare workers: a systematic review and meta-analysis . 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Liu RT, Steele SJ, Hamilton JL, Do QBP, Furbish K, Burke TA, Martinez AP, Gerlus N: Sleep and suicide: A systematic review and meta-analysis of longitudinal studies . Clin Psychol Rev 2020, 81 :101895. Sarchiapone M, Mandelli L, Carli V, Iosue M, Wasserman C, Hadlaczky G, Hoven CW, Apter A, Balazs J, Bobes J et al : Hours of sleep in adolescents and its association with anxiety, emotional concerns, and suicidal ideation . Sleep Med 2014, 15 (2):248-254. Becker SP, Dvorsky MR, Holdaway AS, Luebbe AM: Sleep problems and suicidal behaviors in college students . J Psychiatr Res 2018, 99 :122-128. Wei LC: Insomnia, Suicidal Thoughts, and Mental Health Among Health-Care Workers During COVID-19 . Disaster Med Public Health Prep 2023, 17 :e546. Roman V, Walstra I, Luiten PG, Meerlo P: Too little sleep gradually desensitizes the serotonin 1A receptor system . Sleep 2005, 28 (12):1505-1510. Bernert RA, Joiner TE: Sleep disturbances and suicide risk: A review of the literature . Neuropsychiatr Dis Treat 2007, 3 (6):735-743. McCall WV, Black CG: The link between suicide and insomnia: theoretical mechanisms . Curr Psychiatry Rep 2013, 15 (9):389. Saeed F, Ghalehnovi E, Saeidi M, Ali Beigi N, Vahedi M, Shalbafan M, Kamalzadeh L, Nazeri Astaneh A, Jalali Nadoushan AH, Shoib S: Factors associated with suicidal ideation among medical residents in Tehran during the COVID-19 pandemic: A multicentric cross-sectional survey . PLoS One 2024, 19 (3):e0300394. Kolves K, De Leo D: Suicide in medical doctors and nurses: an analysis of the Queensland Suicide Register . J Nerv Ment Dis 2013, 201 (11):987-990. Cheung T, Lee PH, Yip PS: Suicidality among Hong Kong nurses: prevalence and correlates . J Adv Nurs 2016, 72 (4):836-848. Gold KJ, Sen A, Schwenk TL: Details on suicide among US physicians: data from the National Violent Death Reporting System . Gen Hosp Psychiatry 2013, 35 (1):45-49. 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Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6621801","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":467033632,"identity":"69337ec8-88db-401d-8ffd-30d08aabd327","order_by":0,"name":"Min Wu","email":"","orcid":"","institution":"National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, the Second Xiangya Hospital of Central South 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15:08:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6621801/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6621801/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84304384,"identity":"3c40f614-b394-4b95-9804-9817c1929866","added_by":"auto","created_at":"2025-06-10 11:23:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1906659,"visible":true,"origin":"","legend":"\u003cp\u003eFactors associated with suicidal ideation in healthcare workers exposed to workplace violence.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6621801/v1/f14d72363808e03988f44a53.png"},{"id":104401851,"identity":"8c93bcb0-210a-4f3a-b596-9b4694b1a9ac","added_by":"auto","created_at":"2026-03-11 12:13:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6335846,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6621801/v1/f0ebd9dc-b96e-40d6-8f7a-6772ca31a0f8.pdf"},{"id":84306824,"identity":"a7ed3f3e-fcb0-4430-8ec9-5b55e40d34dd","added_by":"auto","created_at":"2025-06-10 11:31:08","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3127840,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S1. The ROC curve of the regression model (AUC=0.74).\u003c/p\u003e","description":"","filename":"FigureS1.tif","url":"https://assets-eu.researchsquare.com/files/rs-6621801/v1/6a1aa22123155c9367e778d2.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Factors Associated with Suicidal Ideation Among Chinese Healthcare Workers Exposed to Workplace Violence: A Cross-Sectional Survey","fulltext":[{"header":"Highlights","content":"\u003cp\u003e1. The incidence of workplace violence (WPV) in Chinese healthcare workers (HCWs) is high.\u003c/p\u003e\u003cp\u003e2. HCWs who experienced WPV were more likely to report suicidal ideation (SI).\u003c/p\u003e\u003cp\u003e3. History of mental illness, depression, anxiety, stress, insomnia, burnout, and professional role were independent risk factors for SI among HCWs exposed to WPV.\u003c/p\u003e\u003cp\u003e4. Improving WPV can reduce SI among HCWs and improve their psychiatric mental health.\u003c/p\u003e\u003cp\u003eFactors Associated with Suicidal Ideation Among Chinese Healthcare Workers Exposed to Workplace Violence: A Cross-Sectional Survey\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eWorkplace violence (WPV) refers to incidents in which employees are subjected to abuse, threats, or physical assault in connection with their work, including during their commute. Such incidents pose explicit or implicit risks to the safety, well-being, or health of the individuals involved[1]. According to the World Health Organization, healthcare workers (HCWs) were 16 times more likely to experience WPV compared to professionals in other fields[2\u0026ndash;4]. In China, WPV is also a significant issue for HCWs. HCWs face multiple pressures such as high-intensity work, fierce competition for promotion, tense doctor-patient relationship and insufficient social support for a long time, which leads to the frequent occurrence of WPV events[5, 6]. A meta-analysis of 47 observational studies revealed that the overall prevalence of WPV against HCWs was 62.4%, with verbal abuse being the most frequent (61.2%), followed by psychological violence (50.8%), threats (39.5%), physical violence (13.7%), and sexual harassment (6.3%)[7]. WPV in healthcare not only results in physical injuries[8] but also leads to serious psychological consequences, including anxiety and depression[4, 9]. These consequences negatively impact job performance and extend into personal lives and family relationships[9, 10], potentially leading to suicidal ideation (SI) or behaviors[11].\u003c/p\u003e \u003cp\u003eAccording to established models of suicide risk, contributing factors can be broadly categorized into internal and external domains. Internal factors mainly include mental health status and personality traits, whereas external factors mainly encompass cumulative life stressors or occupational stressors such as WPV, heavy workload, and lack of social support[12]. Mental health conditions such as depression, anxiety, and burnout have been identified as key predictors of SI among HCWs. WPV, in particular, has emerged as a critical external factor, as affected HCWs often exhibit elevated levels of depression, anxiety, and post-traumatic stress disorder (PTSD), all of which significantly increase suicide risk. Accumulating evidence from multiple regions confirms that WPV is a significant predictor of suicide risk among HCWs[13]. Given the high suicide risk among HCWs exposed to WPV, exploring the factors influencing SI in this population is of significant importance for the early prevention and intervention of SI.\u003c/p\u003e \u003cp\u003eTo our knowledge, although there have been some studies that have explored the association between violence and SI, there are no studies that have analyzed the factors influencing SI specifically among HCWs who have been exposed to violence[14\u0026ndash;16]. To address this gap, we conducted a web-based cross-sectional study to (1) assess the prevalence of WPV and SI among HCWs, and (2) explore the factors influencing SI in HCWs experiencing WPV.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eThis study employed a cross-sectional survey design, utilizing an online questionnaire administered between November 2023 to June 2024. It is part of a comprehensive online investigation focused on the mental health of medical students and professionals in China[15, 17\u0026ndash;19]. Participants were recruited on a nationwide scale through using the snowball sampling method. The inclusion criteria specified that participants must be: (1) Chinese nationals aged between 18 and 60 years; (2) currently employed as doctors or nurses within any healthcare institution in China, regardless of department, with a minimum of one year of clinical experience; and (3) capable to of comprehending the questionnaire and willing to participate voluntarily. The exclusion criteria included: (1) undergraduate medical students and healthcare workers employed overseas; (2) individuals who had resigned or retired; (3) those engaged in non-clinical roles, such as research; and (4) invalid questionnaire responses. Participation was both anonymous and voluntary, with All participants providing written informed consent and retaining the right to withdraw from the study at anytime. The study received ethical approval from the ethics committee of the Second Xiangya Hospital of Central South University (JY20200326).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSample size\u003c/h3\u003e\n\u003cp\u003eThe calculation of sample size was conducted utilizing PASS version 15.0.5. A sample size of 2353 was determined to yield a two-sided 95% confidence interval (CI) with a width of 0.02, assuming a sample proportion of 0.4. This proportion was derived from a prior study, which indicated that 40% of Chinese medical trainees had experienced WPV[20].\u003c/p\u003e\n\u003ch3\u003eData collection and quality control\u003c/h3\u003e\n\u003cp\u003eThe data were collected using the Chinese online survey platform Wenjuanxing (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.wjx.cn\" target=\"_blank\"\u003ewww.wjx.cn\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.wjx.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). A detailed description of the data collection process can be found in previous literature[19]. To ensure the validity of the responses, a set of criteria was employed to identify and exclude invalid submissions: (1) incorrect answers to a control question (\"When is Chinese National Day?\") or inconsistent responses to repeated questions; (2) uniformity of answers across all questions; and (3) logical inconsistencies, such as contradictions between related questions or responses that contravened common sense. Furthermore, each IP address and WeChat account was limited to a single submission to prevent duplicate entries from the same participant.\u003c/p\u003e\n\u003ch3\u003eSociodemographic and work-related characteristics\u003c/h3\u003e\n\u003cp\u003eThe sociodemographic variables evaluated in this study encompassed age, gender, marital status, educational attainment, and a history of mental health disorders. The investigation of work-related factors included the participants' professional roles (either as doctors or nurses), professional titles, workload, work-life balance, and job satisfaction. Workload was assessed using the Role Overload Items scale,[21] which comprises five items rated on a five-point Likert scale (1\u0026thinsp;=\u0026thinsp;strongly disagree, 5\u0026thinsp;=\u0026thinsp;strongly agree). The comulative score ranges from 5 to 25, with elevated scores indicating a higher perceived workload. Participants were divided into two groups based on the median score: individuals scoring above the median were designated as the high workload group. Work-life balance was measured using with a single five-point Likert item from the Well-Being Index (WBI): \u0026ldquo;My work schedule leaves me enough time for my personal/family life.\u0026rdquo; Participants who expressed disagreement or strongly disagreement with this statement were categorized as experiencing \"poor\" work-life balance. Job satisfaction was assessed using a five-point Likert scale, with participants indicating dissatisfaction or strong dissatisfaction being classified as \"dissatisfied.\" This approach of dichotomizing collapsing responses from a multi-category Likert scale into two categories is widely recognized for the presentation of such data[22].\u003c/p\u003e\n\u003ch3\u003eWorkplace violence\u003c/h3\u003e\n\u003cp\u003eThe Workplace Violence Scale (WVS)[23, 24] was employed to evaluate healthcare workers (HCWs') experiences of WPV. The scale consists of five dimensions, each represented by a single item, resulting in a total of five items: physical assault (PA), verbal abuse (VA, or emotional abuse), threats, verbal sexual harassment (VSH), and sexual assault (SA). Each item is rated on a four-point scale that reflecting the frequency of the violence experienced. For instance, one item inquires: \"In the past 12 months, have you experienced emotional abuse at work, such as verbal insults, humiliation, or yelling? (0\u0026thinsp;=\u0026thinsp;never, 1\u0026thinsp;=\u0026thinsp;once, 2\u0026thinsp;=\u0026thinsp;2\u0026ndash;3 times, 3\u0026thinsp;=\u0026thinsp;\u0026ge;\u0026thinsp;4 times).\" The overall scale score is determined by summing the scores for each item, yielding a range from 0 to 15, where higher scores denote a greater frequency of WPV. In this study, a total score of \u0026ge;\u0026thinsp;1 was considered indicative of participants having experienced at least one form of WPV in the past year.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSuicidal ideation\u003c/h2\u003e \u003cp\u003eA single-item question from the US National Comorbidity Survey[25] was employed to evaluate suicidal ideation (SI) over the preceding year: \"In the past 12 months, have you seriously considered attempting suicide?\" Participants were instructed to respond with either \"yes\" or \"no.\" This question has been extensively utilized in various studies to assess SI among both medical professionals[26\u0026ndash;28] and the general population[29, 30].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMental distress\u003c/h3\u003e\n\u003cp\u003eMental distress was defined to include burnout, depression, anxiety, perceived stress, insomnia and alcohol abuse or dependence. The Chinese version of the Patient Health Questionnaire-9 (PHQ-9)[31], the Generalised Anxiety Disorder scale (GAD-7)[32], and the Alcohol Use Disorders Identification Test Version C (AUDIT-C)[19] were utilized to assess depression, anxiety, and alcohol abuse or dependence, respectively. A detailed description of these instruments can be found in previous literature[19].\u003c/p\u003e \u003cp\u003eThe Oldenburg Burnout Inventory (OLBI)[33, 34] was utilized to measure occupational burnout. This instrument comprises of 16 items that assess two primary dimensions: exhaustion and disengagement. Each item is rated on a 4-point Likert scale, with total scores rangeing from 16 to 64, where higher scores denote more severe burnout. The Chinese version of the OLBI has demonstrated robust reliability and validity within nurse populations[35]. In this study, participants were considered to have burnout if their scores on both the exhaustion and disengagement dimensions exceeded the respective median scores for each dimension.\u003c/p\u003e \u003cp\u003eThe Perceived Stress Scale (PSS-4)[36] was used to measure participants' subjective stress perception over the preceding month. This scale is widely used in research and has demonstrated acceptable reliability and validity[37, 38]. It is scored on a 5-point scale from 0 (never) to 4 (very often),with total scores ranging from 0 to 16, where higher scores indicate increased levels of perceived stress. Based on existing literature, participants with a PSS-4 total score of \u0026ge;\u0026thinsp;6 were categorized as experiencing high-stress group[39].\u003c/p\u003e \u003cp\u003eThe Insomnia Severity Index (ISI)[40] was employed to evaluate the severity of insomnia symptoms as perceived by participants over the preceding two weeks. This instrument comprises seven items, each rated on a 5-point Likert scale ranging from 0 to 4, resulting in a total score spectrum from 0 to 28, where elevated scores denote severe insomnia. The Chinese version of the ISI has been validated, demonstrating robust psychometric properties[41]. Within the context of this study, a total ISI score of 15 or greater was deemded indicative of clinical insomnia[40].\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe normality of the variables was evaluated through quantile-quantile (Q-Q) plots and the Kolmogorov-Smirnov test. Continuous variables that did not follow a normal distribution were reported as medians accompanied by interquartile ranges (IQR: 1st quartile, 3rd quartile), while categorical data were described using frequencies and percentages. To compare differences between groups, Pearson\u0026rsquo;s chi-square test and the Wilcoxon rank-sum test were utilized as appropriate. Subsequently, stepwise logistic regression analysis (using the \"both\" selection method) was conducted to identify predictors of SI among HCWs experiencing WPV. The logistic regression model incorporated variables that demonstrated a significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.20 in the intergroup difference analysis. Multi-collinearity among the independent variables was examined using the variance inflation factor (VIF), with VIF values exceeding 5 indicating the presence of multi-collinearity. Additionally, Receiver Operating Characteristic (ROC) curves were plotted, and the Area Under the Curve (AUC) was calculated to assess the model's ability to discriminate between participants experiencing WPV with and without SI. AUC values ranging from 0.7 to 0.8 were deemed acceptable[19]. All statistical analyses were conducted using R software (version 4.3.2). All tests were two-tailed, with the significance level set at 0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u0026rsquo; characteristics and the prevalence of mental distress and WPV\u003c/h2\u003e \u003cp\u003eOf the 5,385 questionnaires returned, 5,086 were deemed valid and subsequently included in the final analysis, resulting in an efficiency rate of 94.4%. The sociodemographic, occupational, and mental heatlh characteristics of the participants are comprehensively presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Among respondents, 1,483 (29.2%) were male and 3,603 (70.8%) were female, with a median age of 31 years (IQR, 26\u0026ndash;38). The majority of participants were married (2,903, 57.1%). A total of 858 (16.9%) HCWs possessed a master's degree or higher, and 306 (6.0%) reported a history of mental disorders. Regarding professional roles, 3,731 (73.4%) were physicians and 1,355 (26.6%) were nurses, with 2,261 (44.5%) holding an intermediate or senior title. Approximately half of the participants (2,310, 45.4%) reported experiencing a high workload. Over half (2,660, 52.3%) of the HCWs reported a lack of work-life balance, and 1,360 (26.7%) expressed job dissatisfaction.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParticipant characteristics and factors associated with workplace violence among healthcare workers: results of inter-group differences tests\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWithout WPV exposure\u003cem\u003e*\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWith WPV exposure*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value**\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,086 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,391 (47.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,695 (53.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (26, 38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (27, 39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31 (26, 38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,603 (70.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,721 (72.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,882 (69.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,483 (29.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e670 (28.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e813 (30.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,043 (40.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e886 (37.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,157 (42.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,903 (57.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,445 (60.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,458 (54.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced or widowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e140 (2.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60 (2.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80 (3.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAssociate degree or vocational diploma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e641 (12.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e322 (13.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e319 (11.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBachelor's degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,587 (70.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,666 (69.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,921 (71.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaster's or doctoral degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e858 (16.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e403 (16.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e455 (16.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of mental illness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e306 (6.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107 (4.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e199 (7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfessional role\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysician\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,731 (73.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,696 (70.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,035 (75.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNurse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,355 (26.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e695 (29.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e660 (24.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfessional title\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,825 (55.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,313 (54.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,512 (56.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate or senior level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,261 (44.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,078 (45.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,183 (43.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh workload\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,310 (45.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e839 (35.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,471 (54.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor work-life balance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,660 (52.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,049 (43.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,611 (59.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJob dissatisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,360 (26.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e437 (18.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e923 (34.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh perceived stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,842 (75.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,654 (69.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,188 (81.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression symptoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,529 (30.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e533 (22.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e996 (37.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety symptoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e901 (17.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e302 (12.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e599 (22.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBurnout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,840 (36.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e626 (26.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,214 (45.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical insomnia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e788 (15.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e258 (10.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e530 (19.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol abuse/dependence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e904 (17.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e368 (15.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e536 (19.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuicidal ideation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,153 (22.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e363 (15.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e790 (29.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorkplace violence experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,695 (53.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,695 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVerbal abuse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,278 (44.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,278 (84.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThreats\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,076 (21.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,076 (39.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical assault\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e900 (17.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e900 (33.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVerbal sexual harassment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e467 (9.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e467 (17.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSexual assault\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e212 (4.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e212 (7.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003e*\u003c/em\u003eMedian (IQR); n (%). \u003cem\u003e**\u003c/em\u003eWilcoxon rank sum test; Pearson\u0026rsquo;s Chi-squared test; Fisher\u0026rsquo;s exact test. WPV\u0026thinsp;=\u0026thinsp;workplace violence.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe study identified the prevalence of several mental health conditions among Chinese HCWs, including burnout (1,840, 36.2%), depressive symptoms (1,529, 30.1%), anxiety symptoms (901, 17.7%), clinical insomnia (788, 15.5%), high perceived stress (3,842, 75.5%), and alcohol abuse/dependence (904, 17.8%). In relation to WPV, 2,695 HCWs, representing 53.0% of the sample, reported encountering some form of violence. Specifically, 2,278 (44.8%) experienced verbal abuse, 1,076 (21.2%) faced threats, 900 (17.7%) were subjected to physical assault, 467 (9.2%) encountered verbal sexual harassment, and 212 (4.2%) experienced sexual assault (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePrevalence of SI among HCWs exposed to vs. non-exposed to WPV\u003c/h2\u003e \u003cp\u003eAmong total 5,086 HCWs, 1153 (22.7%) reported experiencing SI within the past 12 months. The prevalence of SI was significantly higher among HCWs who had experienced WPV (790 out of 2,695, 29.3%) compared to those who had not (363 out of 2,391, 15.2%) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This disparity remained statistically significant after adjusting for all other characteristics listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e,with an odds ratio (OR) of 1.69, and a 95% confidence interval (CI) of 1.45\u0026ndash;1.97.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eComparison of characteristics of between HCWs exposed to vs. non-exposed to WPV\u003c/h2\u003e \u003cp\u003eCompared to their counterparts who had not experienced WPV, those who had were more likely to be younger, unmarried, physicians, and to have a history of mental disorders (all p\u0026thinsp;\u0026le;\u0026thinsp;0.001). Additionally, they were more likely to report a high workload, poor work-life balance, and job dissatisfaction (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Furthermore, HCWs who were exposed to WPV exhibited had significantly higher prevalence rates of burnout, clinical insomnia, perceived stress, depressive symptoms, anxiety symptoms, and alcohol abuse/dependence (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Conversely, there were no significant differences were observed between the exposed and non-exposed groups concerning sex, education level, or professional title (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eThe related factors of SI among HCWs exposed to WPV\u003c/h2\u003e \u003cp\u003eThe analysis of intergroup differences among HCWs who experienced WPV indicated that SI was significantly associated with factors such as age, marital status, history of mental disorders, professional title, high workload, poor work-life balance, job dissatisfaction, perceived stress, depressive symptoms, anxiety symptoms, burnout, and clinical insomnia (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), in addition to professional role (p\u0026thinsp;=\u0026thinsp;0.016), as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Subsequently, a stepwise logistic regression was conducted (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The results from both the Hosmer-Lemeshow goodness-of-fit test (χ\u0026sup2; = 4.49, p\u0026thinsp;=\u0026thinsp;0.72) and the Omnibus test (χ\u0026sup2; = 437.0, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) demonstrated that the model was well-fitted. In HCWs who had encountered WPV, SI exhibited a significantly association with elevated perceived stress (OR: 1.83, 95% CI: 1.35\u0026ndash;2.52, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), symptoms of depression (OR: 2.06, 95% CI: 1.64\u0026ndash;2.59, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), symptoms of anxiety (OR: 1.61, 95% CI: 1.26\u0026ndash;2.05, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), burnout (OR: 1.79, 95% CI: 1.46\u0026ndash;2.19, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), clinical insomnia (OR: 1.35, 95% CI: 1.07\u0026ndash;1.71, p\u0026thinsp;=\u0026thinsp;0.011), a history of mental illness (OR: 2.39, 95% CI: 1.73\u0026ndash;3.30, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and being a nurse as opposed to a. Physician (OR: 1.38, 95% CI: 1.12\u0026ndash;1.70, p\u0026thinsp;=\u0026thinsp;0.003). Furthermore, the ROC curve depicted in Figure S1 illustrates the efficacy of this regression model in differentiating between participants who had experienced WPV with and without SI, yielding an acceptable AUC of 0.74 (95% CI: 0.72\u0026ndash;0.76, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParticipant characteristics and factors associated with suicidal ideation among healthcare workers exposed to workplace violence: results of inter-group differences tests\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;2,695*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWithout SI\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1,905\u003cem\u003e*\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWith SI\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;790*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value**\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (26, 38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (26, 39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 (26, 35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,882 (69.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,319 (69.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e563 (71.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e813 (30.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e586 (30.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e227 (28.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,157 (42.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e767 (40.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e390 (49.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,458 (54.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,077 (56.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e381 (48.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced or widowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80 (3.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAssociate degree or vocational diploma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e319 (11.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e218 (11.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101 (12.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBachelor's degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,921 (71.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,359 (71.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e562 (71.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaster's or doctoral degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e455 (16.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e328 (17.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e127 (16.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of mental illness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e199 (7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86 (4.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e113 (14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfessional role\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysician\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,035 (75.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,463 (76.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e572 (72.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNurse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e660 (24.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e442 (23.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e218 (27.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfessional title\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,512 (56.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,019 (53.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e493 (62.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate or senior level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,183 (43.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e886 (46.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e297 (37.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh workload\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,471 (54.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e958 (50.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e513 (64.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor work-life balance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,611 (59.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,081 (56.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e530 (67.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJob dissatisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e923 (34.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e546 (28.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e377 (47.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh perceived stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,188 (81.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,456 (76.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e732 (92.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression symptoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e996 (37.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e510 (26.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e486 (61.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety symptoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e599 (22.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e272 (14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e327 (41.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBurnout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,214 (45.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e695 (36.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e519 (65.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical insomnia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e530 (19.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e262 (13.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e268 (33.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol abuse/dependence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e536 (19.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e361 (19.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e175 (22.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*Median (IQR); n (%). **Wilcoxon rank sum test; Pearson\u0026rsquo;s Chi-squared test; Fisher\u0026rsquo;s exact test. SI\u0026thinsp;=\u0026thinsp;suicidal ideation.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study investigated the factors influencing SI among HCWs exposed to WPV. Our findings revealed that: 1) 53.0% of HCWs encountered WPV in their clinical practice; 2) HCWs who experienced WPV were more likely to report SI compared to those who had not been exposed to WPV; and 3) a history of mental illness, depressive symptoms, anxiety symptoms, perceived stress, clinical insomnia, burnout, and professional role were identified as independent risk factors for SI among HCWs exposed to WPV.\u003c/p\u003e \u003cp\u003eHCWs are at high risk of experiencing WPV in their clinical work, and the overall incidence of WPV suffered by HCWs in this study was 53.0%, with Verbal abuse being the most common form of violence[1, 42\u0026ndash;44], which is consistent with previous studies[43\u0026ndash;45]. There may be many reasons for the higher incidence of WPV. first, the tension of healthcare resources[42, 44, 46], especially the concentration of patients in large hospitals, which leads to an increased workload for doctors, long waiting time, and dissatisfaction of patients; second, the lack of communication between doctors and patients[47, 48], and the lack of patients' understanding, which makes them prone to misunderstandings; and third, the insufficient penalties for violent behavior in the existing law, which fails to provide an effective deterrent[49]. Therefore, we propose some recommendations to help reduce the occurrence and risk of WPV. First, HCWs should strengthen communication with patients to reduce the possibility of misunderstanding and conflict. Second, the relevant departments should also further improve the relevant laws and regulations and increase the penalties for violence against doctors.\u003c/p\u003e \u003cp\u003eSI was significantly higher among HCWs who experienced WPV than those who did not, and experienced higher levels of psychological stress[50]. A study in China found a statistically significant correlation between WPV and SI among 3426 HCWs[11]. Environmental influences become stressors when they are perceived by individuals as threatening and beyond their ability to cope[51]. According to the stress theory of suicide[52], stressful events usually precede psychopathological symptoms and suicidal behavior. It can lead to a range of mental health problems such as anxiety, depression, burnout, and insomnia, which can increase the risk of SI.[53, 54]. This study strongly encourages reducing WPV and keeping HCWs safe. This can be accomplished through deterrent government laws, developing communication strategies between HCWs and patients, increasing the number of HCWs to face these situations, and increasing public awareness[50, 55, 56].\u003c/p\u003e \u003cp\u003eThis study found that perceived stress, burnout, and insomnia were identified as independent risk factors for SI among HCWs reporting WPV. The results of previous studies also suggest that burnout, and individual perceived stress is a significant correlate of SI among HCWs [26, 57, 58]. Burnout includes emotional exhaustion, depersonalization and low personal fulfillment[59], may be a predictor of hopelessness, which in turn is a significant indicator of suicide risk[60]. A longitudinal study of U.S. medical students[61] found that improving burnout helped reduce the risk of SI. This suggests that screening for and intervening in burnout can reduce the risk of SI. When individuals perceive environmental influences as threatening and beyond their ability to cope, these influences become stressors[51]. According to the stress theory of suicide[52], stressful events usually precede psychopathological symptoms and suicidal behavior. When individuals are in a vulnerable state, the stress experienced is more likely to translate into SI[62]. Stresses continue to accumulate and these may eventually internalize into depressive symptoms and further induce SI[63]. Previous studies have found[64\u0026ndash;66] that individuals with sleep problems may be at higher risk for SI[67], which is consistent with the univariate analysis of this study's results. Underlying neurobiological factors may explain this relationship to some extent. Insomnia has been found to desensitize the serotonin IA receptor system in rats[68], and a number of studies have linked 5-hydroxytryptamine dysregulation to past suicide attempts, future suicide predictions, and suicide deaths[69]. Thus, insomnia may play an important role in the development of suicidal behavior and be a potential target for interventions aimed at suicide prevention[70].\u003c/p\u003e \u003cp\u003eThis study and a large number of previous studies have shown that anxiety, depressive symptoms, and history of mental illness were associated with an increased risk of SI among HCWs[58, 71\u0026ndash;75]. A large body of evidence suggests that depressive symptoms are one of the major risk factors predicting suicide-related behaviors in various populations[26, 71, 76]. Individuals with anxiety symptoms may exhibit cognitive dissonance and a sense of hopelessness when dealing with internal and external stimuli, which may further trigger depressive mood and prompt individuals to adopt inappropriate coping strategies such as suicide. Therefore, healthcare organizations should strengthen the identification of depression and anxiety symptoms in HCWs, conduct regular psychological assessments, and identify high-risk groups in a timely manner.\u003c/p\u003e \u003cp\u003eIn addition, among HCWs who experienced WPV, professional role was an independent risk factor for SI, and SI was higher among physicians than among nurses. This is different from the results of some previous studies. For example, a study in the United States found a significantly higher incidence of suicide among nurses than among physicians[77]. Further analysis is needed in the future by adding more nurses to the number of studies.\u003c/p\u003e \u003cp\u003eLimitations\u003c/p\u003e \u003cp\u003eDespite some important findings of this study, there are still some limitations. There are several limitations of this study. First, cross-sectional studies reveal the state of the study population at a particular moment in time, or the relationship between different variables at a particular moment in time, and do not explain the causal relationship between variables. Second, all data were based on self-report questionnaires, which may introduce response biases such as skipped questions, incomplete and non-disclosure of information, and uncertainty about the timing of the questionnaires; therefore, more discriminating measurement tools are needed to study these issues more objectively. Third, we collected data on whether physicians had experienced WPV in the past 12 months, so there may be recall bias in the results, which could lead to an underestimation of the magnitude of WPV.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, WPV is not uncommon among HCWs in China, and its impact is far-reaching enough to warrant adequate attention. HCWs exposed to WPV are at higher risk of SI. In HCWs exposed to WPV, SI is influenced by multiple factors, including anxiety, depression, burnout, insomnia, history of mental illness and professional role. These negative emotions not only directly affect mental health, but may also further exacerbate SI. The implementation of early detection and interventions at the individual physician, healthcare system, and external regulatory agency levels is critical to improving the mental health of HCWs and preventing suicide.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe show the greatest gratitude to all the participants and volunteers who helped deliver the online questionnaires.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by National Natural Science Foundation of China (Grant No. 82371501 to T.L.) and The Regional Innovation and Development Joint Fund of the National Natural Science Foundation (Grant No. U22A20302 to T.L.). These sources had no further role in this study design, in the data collection and analysis, in the report's writing, and in the decision to submit the paper for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMin Wu and Zejun Li have contributed equally to this work and share their first authorship. Xiaoyu Zhang, Qianjin Wang, Xin Wang, Huixue Xu, Yueheng Liu, Manyun Li, Wenjing Yuan, and Hanrui Peng were responsible for data collection. Data analysis and interpretation were performed by Zejun Li. The initial draft of the manuscript was prepared by Min Wu and Zejun Li and critically revised by Tieqiao Liu, Qijian Deng,, and Qiuxia Wu. All co-authors revised and agreed to publish the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the ethics committee of the Second Xiangya Hospital of Central South University (JY20200326). The purpose of the study, provision, protection of personal information, and withdrawal of consent were explained to participants before participating. Written informed consent was obtained from study participants, including consent to publish the findings as a paper. All methods were carried out following the Declaration of Helsinki ethical guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLiu J, Gan Y, Jiang H, Li L, Dwyer R, Lu K, Yan S, Sampson O, Xu H, Wang C\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003ePrevalence of workplace violence against healthcare workers: a systematic review and meta-analysis\u003c/strong\u003e. \u003cem\u003eOccup Environ Med 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\u003cstrong\u003eA cross-sectional study of the psychological status of 33,706 hospital workers at the late stage of the COVID-19 outbreak\u003c/strong\u003e. \u003cem\u003eJ Affect Disord \u003c/em\u003e2022, \u003cstrong\u003e297\u003c/strong\u003e:156-168.\u003c/li\u003e\n\u003cli\u003eAlyahya KI, Alrefaei RM, Almadhyani LF, AlQuwayz SS, AlOmairini MI, Alsayed FA, Alasmari YS: \u003cstrong\u003eThe Prevalence and Correlation of Suicidal Ideation Among Nurses in King Saud University Medical City\u003c/strong\u003e. \u003cem\u003eCureus \u003c/em\u003e2023, \u003cstrong\u003e15\u003c/strong\u003e(9):e44859.\u003c/li\u003e\n\u003cli\u003eDavis MA, Cher BAY, Friese CR, Bynum JPW: \u003cstrong\u003eAssociation of US Nurse and Physician Occupation With Risk of Suicide\u003c/strong\u003e. \u003cem\u003eJAMA Psychiatry \u003c/em\u003e2021, \u003cstrong\u003e78\u003c/strong\u003e(6):1-8.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"workplace violence, burnout, stress, mental health, occupational health, suicidal ideation","lastPublishedDoi":"10.21203/rs.3.rs-6621801/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6621801/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eWorkplace violence (WPV) against healthcare workers (HCWs) is a common global issue. However, research on the factors influencing suicidal ideation (SI) among HCWs who experience WPV is limited. This study aims to investigate the risk factors for SI among HCWs exposed to WPV.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eHCWs were recruited nationwide using snowball sampling. The Workplace Violence Scale (WVS) was used to assess WPV exposure, and a single-item question measured SI. Additionally, sociodemographic characteristics, workload, job satisfaction, burnout, depression, anxiety, stress, insomnia, alcohol abuse/dependence history, and psychiatric disorders were evaluated. Data analysis included descriptive statistics, chi-square tests, Wilcoxon rank-sum tests, and stepwise logistic regression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eAmong 5086 participants, 53.0% of HCWs experienced some form of WPV. Of these, 44.8% faced verbal insults, 21.2% were threatened, 17.7% suffered physical attacks, 9.2% experienced verbal harassment, and 4.2% encountered sexual assault. The prevalence of SI was significantly higher among HCWs who experienced WPV (OR = 1.69, 95% CI [1.45–1.97]). In HCWs exposed to WPV, SI was significantly associated with high perceived stress, depression, anxiety, burnout, insomnia, history of mental illness, and professional role.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eWPV is common among HCWs in China, and those affected are more likely to experience SI, as well as emotional problems such as anxiety and depression. These emotional issues can impact mental health and may exacerbate SI. Preventing WPV and addressing its consequences are crucial.\u003c/p\u003e","manuscriptTitle":"Factors Associated with Suicidal Ideation Among Chinese Healthcare Workers Exposed to Workplace Violence: A Cross-Sectional Survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-10 11:23:03","doi":"10.21203/rs.3.rs-6621801/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dd32843d-6735-4aac-b180-75d530b5b930","owner":[],"postedDate":"June 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-04T07:12:13+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-10 11:23:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6621801","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6621801","identity":"rs-6621801","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}