Psychological Distress and Support Profiles among Healthcare Second Victims: A Latent Profile Analysis

Psychological Distress and Support Profiles among Healthcare Second Victims: A Latent Profile Analysis | 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 Psychological Distress and Support Profiles among Healthcare Second Victims: A Latent Profile Analysis Lulu Nan, Hui Li, Liyan Wang, Xiaohong Zhang, Kun Yang, Weijuan Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7806222/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 Nurses involved in patient safety incidents (PSIs) often become "second victims," suffering significant psychological and professional distress. Rather than being a homogeneous group, these nurses exhibit diverse experiences, suggesting the need for a person-centered approach to better understand their distinct support needs. This study employed Latent Profile Analysis (LPA)—a person-centered method ideal for identifying hidden subgroups—to classify second victim nurses based on their distress and support patterns, with the aim of informing tailored intervention strategies. Methods A stratified cluster sampling technique was employed in May 2024 among clinical nurses from 16 tertiary hospitals and 21 secondary hospitals in Shanxi Province, China, who had experienced PSIs. Participants completed a socio-demographic questionnaire, the Chinese version of the SVEST, and the Simplified Coping Style Questionnaire (SCSQ). Latent Profile Analysis (LPA) was performed on the 24 SVEST items to identify homogeneous subgroups. Multinomial logistic regression was used to examine associations between subgroup membership and demographic, work-related, and coping strategy variables. Results Among 21,519 nurses analyzed, a two-profile model best fit the data. Profile 1 (52.5%, n = 11,295), labeled the ‘high-distress, low-support’ group, reported higher psychological distress (e.g., guilt, self-blame) and lower perceived support. Profile 2 (47.5%, n = 10,224), labeled the ‘low-distress, high-support’ group, reported lower distress and higher support. Multivariate analysis revealed that nurses in the ‘low-distress, high-support’ group were significantly more likely to be older (OR = 1.02, 95%CI: 1.01–1.02), use positive coping strategies (OR = 1.19, 95%CI: 1.18–1.20), and perceive higher levels of leadership support ('always' supported: OR = 8.65, 95%CI: 7.43–10.09). Conversely, nurses with longer work experience (> 10 years vs. <3 years: OR = 0.70, 95%CI: 0.61–0.79) and those using negative coping strategies (OR = 0.89, 95%CI: 0.89–0.90) were more likely to belong to the ‘high-distress, low-support’ group. Conclusion This study is the first to identify distinct profiles of second victim nurses using LPA, revealing significant heterogeneity in their experiences of distress and support. The findings underscore the critical influence of leadership support, coping strategies, age, and work experience. Developing targeted, hierarchical support systems is essential—providing intensive psychological and institutional support for the high-distress group, and reinforcing psychological capital and professional value for the low-distress group. Integrating proactive leadership and systemic support into hospital management is recommended to create a comprehensive support loop. Patient safety incidents Second victims Psychological Distress and Support Latent Profile Analysis Figures Figure 1 Figure 2 Figure 3 1 Introduction Nursing staff, as the principal implementers within healthcare quality assurance frameworks, play a pivotal role in determining patient safety and clinical outcomes. Nurses undertake significant nursing responsibilities and must cope with considerable workloads, including medication administration, infection control, and early detection of clinical deterioration( 1 ). Because nurses are often overworked and have heavy caregiving duties, they face higher risks associated with Patient Safety Incidents (PSIs)( 2 ). According to data from the World Health Organization (WHO), in high-income countries, it is estimated that 1 in every 10 patients experiences an adverse event while receiving hospital care. In low- and middle-income countries, approximately 134 million adverse events occur each year, with about 25% of patients being harmed( 3 , 4 ) . In patient safety incidents (PSIs), while patients are the primary victims, nurses themselves often become "second victims." The term "second victim," first introduced by Wu in 2000( 5 ), refers to healthcare providers who suffer negative consequences after being involved in PSIs that cause harm to patients. On an emotional level, affected nurses frequently report intense psychological distress, including guilt, anxiety, intrusive memories, anger, regret, fear of repeating errors, and sleep disturbances( 6 ). Some may even experience long-term depression or trauma.Professionally, they may also face blame, humiliation from colleagues, and a loss of confidence in their clinical abilities( 7 ). In extreme cases, the accumulated stress can lead to severe psychological crises, including suicidal ideation( 8 – 10 ). To better understand the challenges faced by these ‘second victims’, researchers have developed several tools—among which the SVEST is the most widely used. Burlison and colleagues ( 11 ) developed the Second Victim Experience and Support Tool (SVEST) to assess the impact of PSIs on nurses. This instrument has been widely applied in the United States and the United Kingdom( 12 ). In China, Chen Jiaojiao et al( 13 ) validated the 24-item Chinese SVEST, assessing distress and support across six dimensions, demonstrating excellent internal consistency (Cronbach's α = 0.98) and established reliability and validity in clinical settings.. However, most existing SVEST studies—both domestic and international—have employed variable-centered analytical approaches. These methods assume homogeneous response patterns (i.e., that all participants share the same underlying pattern of relationships between the dimensions) across SVEST dimensions among participants( 14 – 16 ),potentially masking population heterogeneity. Traditional analyses often fail to capture subgroup differences, thereby limiting development of targeted interventions. Latent Profile Analysis (LPA)( 17 ), an individual-centered research method based on a probabilistic model, groups nurses with similar characteristics such that between-group differences are maximized while within-group differences are minimized. This method is particularly useful for uncovering unobserved heterogeneity that traditional methods may overlook. To date, no studies—domestically or internationally—have utilized latent profile analysis to classify subtypes of second victims. This study aims to fill this gap by applying LPA to classify second victim subtypes, thereby informing targeted interventions and fostering a supportive and nonpunitive patient safety culture. 2 Methods 2.1 Participants A stratified cluster sampling technique was employed. The flow of participants through the study was presented in Fig. 1 .As of 2024, a total of 92 medical institutions in our province were enrolled in the National Nursing Quality Data Platform, including 40 tertiary hospitals and 52 secondary hospitals. Following a 1:1.3 ratio, we randomly selected 16 tertiary hospitals and 21 secondary hospitals across Shanxi province. In May 2024, a questionnaire survey was conducted among clinical nurses who had experienced patient safety incidents (PSIs) in these selected hospitals. Electronic questionnaires were distributed via the Shanxi Nursing Quality Control Center (under the Provincial Health Commission). Nursing directors disseminated the survey with instructions emphasizing anonymous responses, the critical importance of findings for developing nurse support strategies, and the need for answers based on authentic personal experiences. The questionnaire employed a required-response format to ensure data authenticity and objectivity. Inclusion criteria were: ( 1 ) Registered nurses holding valid practicing certificates; ( 2 ) Direct involvement in ≥ 1 PSI (e.g., as primary personnel, first discoverer, or key handler); ( 3 ) Employment as clinical frontline nurses at survey time; ( 4 ) Voluntary participation with written informed consent. Exclusion criteria were: ( 1 ) Trainee or student nurses; ( 2 ) Nurses on long-term leave (e.g., sick/maternity leave) during the study period. The study was approved by the Institutional Review Board (YXLL-2024-052), and it followed the principles set forth in the Declaration of Helsinki( 18 ). 2.2 Instruments 2.2.1 Socio‑demographic questionnaire Socio-demographic and work-related variables including age, gender, marital status, work experience, hospital level, nursing title level, monthly income, educational level, employment status, average monthly night shifts, and perceived supervisor support were collected using a self-designed questionnaire. 2.2.2 Second Victim Experience and Support Tool, SVEST The Chinese version of the Second Victim Experience and Support Tool (SVEST) was used (Cronbach's α = 0.98). This 24-item scale comprises two domains: distress (1–12 items) and support (13–24 items). Items were rated on a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree). The distress domain is positively scored, while the support domain is reverse-scored. The total score ranges from 24 to 120, with higher scores indicating less adequate support after experiencing adverse healthcare events( 13 ). 2.2.3 Simplified Coping Style Questionnaire, SCSQ The Simplified Coping Style Questionnaire (SCSQ), culturally adapted for Chinese populations by Xie Yaning ( 19 )based on Folkman and Lazarus' Ways of Coping Questionnaire (WCQ), was administered. SCSQ was used to measure how nurses cope with stress, both positively and negatively, comprising two subscales:positive coping (12 items) and negative coping (8 items). Items are rated on a 4-point Likert scale (0 = never adopt to 3 = frequently adopt). The SCSQ has been validated in healthcare settings to assess nurses' coping strategies. For example, Zhang et al. utilized this scale to evaluate psychological status and coping patterns among cross-regional support nurses versus local nurses during public health emergencies, demonstrating good internal consistency (Cronbach's α = 0.82)( 20 , 21 ). 2.3 Statistic This study mainly used latent profile analysis for data analysis. Unlike variable-centered approaches, LPA aims to identify distinct patterns of multiple variables that occur consistently across individuals rather than focusing solely on individual variables or their interactions( 22 ). By doing so, LPA classifies individuals within heterogeneous populations into smaller, more homogeneous subgroups( 23 ), revealing hidden information that these subgroups bring to light. First, we used the 24 SVEST items to run a latent profile analysis (LPA) and find distinct groups among nurses. Then, we used AIC based stepwised logistic regression to explore which factors were linked to each group. The above analyses were completed by Mplus 8.2with SPSS 25.0 software. The main evaluation indexes of the latent variable model were AIC, BIC, Entropy, and BLRT. Among them, the smaller the values of AIC, BIC, the better the model fit, Entropy is an index to evaluate the accuracy of category classification, which takes the value of 0 ~ 1. Entropy ≥ 0.8 indicates that the classification accuracy exceeds 90%. BLRT are used to compare the fit difference between k-1 and k-category models, and the p-value of both reaches a significant level indicating that the k-category model is better than the k-1 category model( 24 – 27 ). 3 Results 3.1 Total characteristics As shown in Table 1 , a total of 21961questionnaires were collected. Among these, 442 questionnaires were excluded as the nurses indicated no experience with patient safety incidents, resulting in 21,519 valid responses for analysis. Most of them were female (97.6%) and around 34 years old on average. About half had more than 10 years of experience, worked in secondary or tertiary hospitals, and earned less than ¥6,000/month. Regarding work schedules, 48.4% worked 5–9 night shifts. Leadership support was frequently or always reported by 66.0% of participants. The mean SCSQ score was 57.45 ± 10.36, suggesting moderate coping levels. Table 1 Total characteristics Variables Total[n = 2,1519; n (%)] gender male 512 ( 2.4) female 21007 (97.6) average Age 33.80 (7.38) work experience 10 years 10328 (48.0) marital Status 1 Married 16930 (78.7) 2 Single 4368 (20.3) 3 Others 221 ( 1.0) hospital level secondary Hospital 10974 (51.0) tertiary Hospital 10545 (49.0) degree secondary Vocational 287 ( 1.3) associate 5383 (25.0) bachelor 15797 (73.4) master 52 ( 0.2) nursing title level junior 14168 (65.8) mid-Level 5771 (26.8) senior 1580 ( 7.3) employment Status permanent Employee 6192 (28.8) contract Employee 15327 (71.2) income 8,000 Yuan 139 ( 0.6) leadership support Never 1276 ( 5.9) sometimes 6047 (28.1) offen 9141 (42.5) always 5055 (23.5) average monthly night shifts 0 5483 (25.5) 1–4 3962 (18.4) 5–9 10411 (48.4) 10–14 1494 ( 6.9) >15 169 ( 0.8) SCSQ SCSQ -positive 37.89 (6.93) SCSQ -negative 19.56 (5.78) 3.2 Latent profile analysis (LPA) Latent profile models with two to five classes were evaluated. Although BIC values monotonically decreased with additional classes, this trend is expected in very large samples (n ≈ 21 000) and did not coincide with improvements in classification quality (Entropy = 0.93–0.94). Visual inspection indicated that extra classes merely represented proportional shifts along a single distress–support continuum without generating qualitatively novel patterns. Although adding more classes made the model slightly better on paper, in such a big sample this is expected. But those extra classes didn’t really give us new insights—they just split the same trend into smaller pieces. So we chose the simpler 2-class model because it’s easier to understand and apply in hospitals. Full fit statistics and alternative solutions are presented in Table 2 . Table 2 Indicators for each latent profile of nurse subgroups as second victims Model AIC BIC Entropy Smallest Class (%) Largest Class (%) prob_min prob_max BLRT p 2 1,454,083.93 1,454,666.23 0.93 48 52 0.98 0.98 < 0.01 3 1,380,831.47 1,381,613.19 0.93 26 43 0.96 0.97 < 0.01 4 1,344,338.28 1,345,319.41 0.93 13 33 0.95 0.97 < 0.01 5 1,317,817.66 1,318,998.21 0.94 12 29 0.94 0.97 < 0.01 3.3 Naming of latent profile We found two clear groups as shown in Fig. 2 : one group of nurses who had high distress and felt they lacked support (we called this the ‘high-distress, low-support’ group), and another group who felt less distress and had better support (‘low-distress, high-support’ group). The specific scores and trend charts for each item of the distress and support dimensions in the SVEST scale for the two latent profile groups were detailed in the Fig. 3 . 3.4 Univariate analysis Univariate analysis with SVEST as the dependent variable (Table 3 ) revealed significant distributional differences between latent profiles for the following factors: age, work experience, hospital level, nursing title level, average monthly night shifts, employment status, income level, leadership support, and SCSQ (all p < 0.05). Table 3 Univariate analysis with SVEST Variables Profile 1 [n = 11295; n (%)] Profile 2 [n = 10224; n (%)] OR[95%CI] gender male 274 ( 2.4) 238 ( 2.3) 0.96[0.80–1.14] female 11021 (97.6) 9986 (97.7) average Age 34.03 (7.64) 33.55 (7.08) 1.01[1.01–1.01] work experience 10 years 5452 (48.3) 4876 (47.7) marital Status 1 Married 8929 (79.1) 8001 (78.3) 0.96[0.90–1.03] 0.84[0.64–1.10] 2 Single 2259 (20.0) 2109 (20.6) 3 Others 107 ( 0.9) 114 ( 1.1) hospital level secondary Hospital 5441 (48.2) 5533 (54.1) 1.27[1.20–1.34] tertiary Hospital 5854 (51.8) 4691 (45.9) degree secondary Vocational 161 ( 1.4) 126 ( 1.2) 0.83[0.65–1.05] 0.88[0.69–1.11] 0.85[0.47–1.53] associate 2764 (24.5) 2619 (25.6) bachelor 8343 (73.9) 7454 (72.9) master 27 ( 0.2) 25 ( 0.2) nursing title level junior 7313 (64.7) 6855 (67.0) 1.07[1.01–1.14] 1.25[1.12–1.39] mid-Level 3080 (27.3) 2691 (26.3) senior 902 ( 8.0) 678 ( 6.6) employment Status Permanent employee 3384 (30.0) 2808 (27.5) 0.89[0.83–0.94] contract employee 7911 (70.0) 7416 (72.5) income 8,000 Yuan 89 ( 0.8) 50 ( 0.5) leadership support Never 326 ( 2.9) 950 ( 9.3) 1.41[1.23–1.62] 3.70[3.24–4.22] 9.68[8.40-11.15] sometimes 1971 (17.5) 4076 (39.9) offen 5113 (45.3) 4028 (39.4) always 3885 (34.4) 1170 (11.4) average monthly night shifts 0 3067 (27.2) 2416 (23.6) 0.98[0.90–1.06] 0.81[0.76–0.87] 0.64[0.57–0.72] 0.60[0.44–0.82] 1–4 2196 (19.4) 1766 (17.3) 5–9 5287 (46.8) 5124 (50.1) 10–14 672 ( 5.9) 822 ( 8.0) >15 73 ( 0.6) 96 ( 0.9) SCSQ SCSQ -positive 41.09 (5.67) 34.37 (6.47) 1.19[1.18–1.20] 0.89[0.89–0.90] SCSQ -negative 19.81 (6.36) 19.27 (5.05) Total SVEST Score 3.86 3.19 18.69[16.99–20.57] 3.5 multivariate analysis A multinomial stepwise regression based on AIC was conducted with Factors with significant differences in univariate analysis (p < 0.05) as independent variables and potential categories of SVEST in nurses as dependent variables, and the results are shown in Table 4 . As shown in Table 4 , when comparing 2 prolies of SVEST, nurses with higher age and positive SCSQ tended to be classified into ‘Low-distress with high-support group’ (OR = 1.02, p < 0.001,95%CI [1.01–1.02]; OR = 1.19, p < 0.001,95%CI [ 1.18–1.20]). A significant moderating effect of leadership support was observed between the two profiles (p < 0.01). Higher leadership support frequency correlated with reduced nurse distress and enhanced perceived support, with the strongest effects occurring when support was 'always' provided (OR = 8.65,95%CI [7.43–10.09]).For the same duration of work experience, longer working years ,negative SCSQ were associated with a higher likelihood of belonging to the ‘High-distress with low-support group’ (OR = 0.81, p < 0.01,95%CI [0.73–0.90]; OR = 0.70, p < 0.01,95%CI [0.61–0.79]; OR = 0.89, p < 0.01,95%CI [0.89–0.90]). Table 4 Multivariate analysis with SVEST Variables OR[95%CI] P Value average Age 1.02 [1.01–1.02] < 0.001 work experience <3 years < 0.001 10 years 0.70 [0.61–0.79] leadership support Never < 0.001 < 0.001 < 0.001 sometimes 1.40 [1.21–1.62] offen 3.54 [3.07–4.08] always 8.65 [7.43–10.09] SCSQ SCSQ -positive 1.19 [1.18–1.20] < 0.001 < 0.001 SCSQ -negative 0.89 [0.89–0.90] 4 Discussion 4.1 Summary of the study The purpose of this study was to delineate psychological distress and support subgroups of nurses as the second victims and to explore the factors influencing distress and support. We rationalized the selection of two profiles and named them as ‘high-distress, low-support’ group and ‘low-distress, high-support’ group. 4.2 Characteristics of the Two Profile Groups The results of the study showed that about 52.49% of the nurses were classified as ‘high-distress, low-support’ group profile 1. The SVEST total score was 3.86 at a moderately high level, which is higher than Xu Hongning’s findings( 28 ). According to the SVEST scale items 1 to 12, nurses generally feel self-blame and distress after experiencing PSIs, especially item 4 "Feeling deeply self-blame for patient safety incidents" has the highest score. However, the scores of item 11 "Experiencing these events makes me not want to work in the medical industry" and item 12 "Because of the pressure, I sometimes want to quit my current job" are relatively low, indicating that these nurses still tend to stay in their positions. This result is different from the international research by Finney et al.( 29 ), which may be related to the cultural background and the current employment environment in China: nurses' professional identity is often closely associated with "dedication", and they are more likely to attribute incidents to themselves rather than the system( 30 ). This self-blame actually reflects a higher sense of professional responsibility. Compared with foreign nurses, nurses in China face greater practical employment pressure( 31 ). On the other hand, items 13 to 24 of the SVEST scale indicate that the level of support received by nurses is generally low. Multiple studies have pointed out that insufficient support can easily lead nurses to feel helpless when facing events( 2 , 6 ). Therefore, it is crucial to establish a support system that is in line with the hospital culture in China and improve the systematic guarantee mechanism. 47.51%were assigned to the ‘low-distress, high-support’ group profile 2. The SVEST total score was 3.19, markedly lower than the previous group's. Profile 2 scored the lowest on the distress dimension(items 1–12) for the item "Having experienced a patient safety event made me feel deeply guilty," but scored the highest on items such as "These experiences made me not want to work in the healthcare industry" and "Sometimes I want to quit my current job." Meanwhile, their overall scores on the support dimension (items 13–24) were relatively high, presenting a state of low self-blame, low retention intention, and relatively high perceived support. Multiple studies explain the relationships among these factors. When nurses experience adverse events and receive timely and adequate support from managers, colleagues, and the organization, they can quickly adjust their mindset to cope with the negative impacts and acquire more professional knowledge and skills( 11 , 32 , 33 ). This reinforces their belief that they can achieve greater rewards in a better platform. 4.3 Factors Influencing Profile Membership he use of Latent Profile Analysis (LPA) allows us to incorporate individual characteristics of each nurse, thereby identifying those who are more susceptible to external influences. This approach facilitates the exploration of potential factors contributing to this outcome and enables the provision of personalized guidance and support. Further study found that age, work experience, leadership support and SCSQ were influential factors of nurses as the second victims. 4.3.1 Work Experience Work experience is an exposure factor associated with increased nurse distress.Nurses who have long worked in high-intensity and high-risk clinical environments, are more susceptible to emotional exhaustion when facing safety incidents, Consistenting with the findings of Kuang Zixia( 33 ). Moreover, as nurses typically assume multiple roles—including clinical experts, preceptors, and managers—these incidents not only induced professional guilt but also raised concerns about institutional disciplinary actions and potential impacts on career advancement. Consequently, their post-incident distress was more severe and associated with increased turnover intention( 34 , 35 ). Therefore, their support needs should include psychological counseling, crisis management training, and career development guidance to alleviate distress. 4.3.2 Leadership Support Leadership support reduced the occurrence of distress to a large extent. Multiple studies confirm this finding. Cao et al.( 36 ) reported that nurses facing safety incidents fear blame from families, colleague indifference, and leader criticism, but proper organizational safety support enables positive coping and reduces distress. Similarly, Beata Dziedzic's( 37 ) study of 321 nurses found better support led to lower stress and improved emotional management post-incident. Administrative support was the predominant form of support perceived by nurses( 38 ), particularly highlighting the pivotal role of leadership-level support. 4.3.3 Age Age serves as an indicator of psychological maturity and life experience, and is closely associated with psychological capital. According to Zheng Ren et al.( 39 ), older nurses tend to demonstrate stronger emotional regulation abilities and higher levels of psychological capital. Furthermore, Liang Nana et al.( 40 ) found that positive psychological capital enables nurses to effectively manage challenges and develop solutions by utilizing external resources and support, which in turn helps alleviate occupational stress. Consequently, compared to their younger counterparts, older individuals generally possess a broader range of coping strategies to handle adversity and stress, making them more likely to be categorized into the ‘low-distress, high-support’ group. 4.3.4 SCSQ Strategies After PSIs, the use of different coping strategies markedly affects an individual's distress level. SCSQ strategies are generally divided into positive and negative types. Positive strategies include seeking social support, cognitive reframing, and engaging in problem-solving activities. Wang J et al's study confirmed this ( 41 ) that nurses who frequently employ positive coping exhibit lower perceived stress, especially in psychiatric settings. Conversely, negative strategies such as relying on others to solve problems or using alcohol or tobacco for emotional relief can worsen burnout and intensify psychological burden, thereby amplifying negative emotions( 42 , 43 ). 4.4 Limitations This study has several limitations that should be considered. Firstly, the data was gathered through self-reporting, which may be vulnerable to some degree of bias. Secondly,, the study utilized a cross sectional design that did not permit the establishment of causal relationships between the findings. 5 Conclusion Our study grouped distress and support in the second victim nurses into two subgroups (‘high-distress, low-support’ group and ‘low-distress, high-support’ group), each presenting different characteristics. Such a division provided us with a deeper understanding of distress and support among older the second victim nurses. Our findings suggest that distress and support is influenced by several factors, including: work experience、leadership support、age、SCSQ Strategies. By comparing subgroups, we can identify populations disproportionately affected by distress and lower support and develop targeted interventions accordingly. Medical institutions should establish a stratified and precision-focused safety culture support system: For nurses in the ‘high-distress, low-support’ group, priority should be given to providing psychological counseling, crisis intervention, clear institutional guarantees, and career development training, while strengthening supportive behaviours from leadership to enhance their coping capacity and support received after patient safety incidents. For the ‘low-distress, high-support’ group, efforts should focus on consolidating psychological capital and enhancing their sense of professional value through empowerment mechanisms. Furthermore, it is recommended that support measures be preemptive and structured, integrating leadership support and systemic support into hospital management systems to form a full-process support loop encompassing ‘prevention before incidents, support during incidents, and growth after incidents’. Declarations Data availability statement The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. Ethics statement This study was approved by the Medical Ethical Committee of Shanxi Bethune Hospital (YXLL-2024-052) and informed consent was obtained from the participants. Consent for publication Not applicable. Author contributions All authors contributed to the study conception and design. L.W. , H.L. and X.Z. led the experiments . Material preparation, data collection and analysis were performed by L.N., K.Y. ,W.W and H.L.The first draft of the manuscript was written by L.N., and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Funding This work was supported by Science Fundation of Shanxi Bethune Hospital (grant number: 2023YH05). Acknowledgments Thanks to the Shanxi Nursing Quality Control Center in China for providing platforms for free data collection. Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. References Wu Chao, Zhang Hongli, Hu Mengyi, Li Lu, Yuan Weiyun, Sa Zhen, et al. The mediating role of situational motivation between psychological capital and emotional exhaustion among frontline clinical nurses. J Nurs Adm. 2024;24(10):844–8. 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Latent profile analysis of feminist identity among undergraduate nursing students and its relationship with achievement motivation. Chin Nurs Res. 2025;39(9):1456–62. Xu Hongning, Qi Xiaoyan, Fang Jihong, Lu Linyang, Zhang Fu, Xue Jingjing, et al. Support perceived by nurses as the second victims and the relationship between support and professional quality of life. J Nurs Sci. 2021;36(23):5–8. FINNEY RE, TORBENSON VE, RIGGAN KA, Weaver AL, LONG ME, ALLYSE MA, et al. Second Victim Experiences of Nurses in Obstetrics and Gynecology: SVEST Survey. J Nurs Manag. 2021 May;29(4):642–52. Tian X, Gan X, Ren Y, Li F, Herrera MFJ, Liu F. Adaptation and validation of moral distress thermometer in Chinese nurses. BMC Nurs. 2024 July 4;23:456. Lantz B, Fagefors C. Assessing factors associated with nurses leaving the profession: A secondary analysis of cross-sectional data. Int J Nurs Stud Adv. 2025 June;8:100315. Li Z, Zhang C, Chen J, Du R, Zhang X. The current status of nurses’ psychological experience as second victims during the reconstruction of the course of event after patient safety incident in China: a mixed study. BMC Nurs. 2024 Oct 8;23:722. Kuang Zixia, Chen Ling, Lai Guoxin, Li Xin, Xie Yuhang, Sun Xin. Supporting Status and Influencing Factors of the Second Victim in Intensive Care Unit Nursing Adverse Event. Nurs J Chin PLA. 2021;38(12):13–7. Wei L, Guo Z, Zhang X, Niu Y, Wang X, Ma L, et al. Mental health and job stress of nurses in surgical system: what should we care. BMC Psychiatry. 2023 Nov 23;23(1):871. Chai Jingjing, Zhang Yukun, Jia Ruiying, Wang Yuwei, Wang Meiling, Wang Qiru, et al. The study on the current situation and influencing factors of second victim experience and support among emergency department nurses. Chin J Emerg Crit Care Nurs. 2024;5(8):683–8. Cao Na, Xie Shuo, Lu Meisu, Shang Lili, Zhang Luyao. The trauma recovery path of ICU nurses as second victims of patient safety incidents：a grounded theory study. Chin J Emerg Crit Care Nurs. 2025;6(2):219–25. Dziedzic B, Łodziana K, Marcysiak M, Kryczka T. Occupational stress and social support among nurses. Front Public Health. 2025;13:1621312. Jeong S, Kim S, Chang HE, Jeong SH. How does just culture reduce negative work outcomes through second victim distress and demand for support in clinical nurses? A path analysis. BMC Nurs. 2025 Feb 19;24(1):192. Ren Z, Zhao H, Zhang X, Li X, Shi H, He M, et al. Associations of job satisfaction and burnout with psychological distress among Chinese nurses. Curr Psychol N B NJ. 2022 Nov 14;1–11. Liang Nana, Zhao Juan, Ren Jishun, Zhang Yajun, Tao Hongxia, Zhen Haixia. The mediating effect of positive psychological capital between nurses’ spare time planning and occupational stres. J Nurs Sci. 2025;40(3):82–5, 123. Lu Q, Wang B, Zhang R, Wang J, Sun F, Zou G. Relationship Between Emotional Intelligence, Self-Acceptance, and Positive Coping Styles Among Chinese Psychiatric Nurses in Shandong. Front Psychol. 2022;13:837917. Hetherington D, Wilson NJ, Dixon K, Murphy G. Emergency department Nurses’ narratives of burnout: Changing roles and boundaries. Int Emerg Nurs. 2024 June;74:101439. Ren Q, Wang J, Yuan Z, Jin M, Teng M, He H, et al. Examining the impact of perceived social support on mental workload in clinical nurses: the mediating role of positive coping style. BMC Nurs. 2025 Mar 27;24(1):331. Additional Declarations No competing interests reported. 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. 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10:09:52","extension":"html","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":129272,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7806222/v1/9b4bf2bf5b106ab7bd27b50c.html"},{"id":96302062,"identity":"b88fd8b7-755b-46e9-9b46-994407dc56c6","added_by":"auto","created_at":"2025-11-19 14:39:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":98245,"visible":true,"origin":"","legend":"\u003cp\u003eFlow of participate through the study.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7806222/v1/80fa5ae64673bc5880181962.png"},{"id":96302064,"identity":"606a94d3-c821-4f90-84ef-a7c905295507","added_by":"auto","created_at":"2025-11-19 14:39:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":27822,"visible":true,"origin":"","legend":"\u003cp\u003eLatent profile model of nurse subgroups as second victims\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7806222/v1/9e7b63d6ca4ff92275a6baf4.png"},{"id":96364923,"identity":"5fc9c01f-669b-4c37-b89a-d8638b80511d","added_by":"auto","created_at":"2025-11-20 10:09:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":128385,"visible":true,"origin":"","legend":"\u003cp\u003eLatent profile model of nurse subgroups as second victims for each item\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7806222/v1/bf453a89d77d805775ec7fd7.png"},{"id":104882012,"identity":"8bc05be9-e541-490f-b023-126e0964a2c5","added_by":"auto","created_at":"2026-03-18 09:28:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1258630,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7806222/v1/fdf3a3dc-0fd2-4a60-b679-983c52baf78d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Psychological Distress and Support Profiles among Healthcare Second Victims: A Latent Profile Analysis","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eNursing staff, as the principal implementers within healthcare quality assurance frameworks, play a pivotal role in determining patient safety and clinical outcomes. Nurses undertake significant nursing responsibilities and must cope with considerable workloads, including medication administration, infection control, and early detection of clinical deterioration(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Because nurses are often overworked and have heavy caregiving duties, they face higher risks associated with Patient Safety Incidents (PSIs)(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAccording to data from the World Health Organization (WHO), in high-income countries, it is estimated that 1 in every 10 patients experiences an adverse event while receiving hospital care. In low- and middle-income countries, approximately 134\u0026nbsp;million adverse events occur each year, with about 25% of patients being harmed(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) .\u003c/p\u003e\u003cp\u003eIn patient safety incidents (PSIs), while patients are the primary victims, nurses themselves often become \"second victims.\" The term \"second victim,\" first introduced by Wu in 2000(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), refers to healthcare providers who suffer negative consequences after being involved in PSIs that cause harm to patients. On an emotional level, affected nurses frequently report intense psychological distress, including guilt, anxiety, intrusive memories, anger, regret, fear of repeating errors, and sleep disturbances(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Some may even experience long-term depression or trauma.Professionally, they may also face blame, humiliation from colleagues, and a loss of confidence in their clinical abilities(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). In extreme cases, the accumulated stress can lead to severe psychological crises, including suicidal ideation(\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo better understand the challenges faced by these \u0026lsquo;second victims\u0026rsquo;, researchers have developed several tools\u0026mdash;among which the SVEST is the most widely used. Burlison and colleagues (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) developed the Second Victim Experience and Support Tool (SVEST) to assess the impact of PSIs on nurses. This instrument has been widely applied in the United States and the United Kingdom(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). In China, Chen Jiaojiao et al(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) validated the 24-item Chinese SVEST, assessing distress and support across six dimensions, demonstrating excellent internal consistency (Cronbach's α\u0026thinsp;=\u0026thinsp;0.98) and established reliability and validity in clinical settings..\u003c/p\u003e\u003cp\u003eHowever, most existing SVEST studies\u0026mdash;both domestic and international\u0026mdash;have employed variable-centered analytical approaches. These methods assume homogeneous response patterns (i.e., that all participants share the same underlying pattern of relationships between the dimensions) across SVEST dimensions among participants(\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e),potentially masking population heterogeneity.\u003c/p\u003e\u003cp\u003eTraditional analyses often fail to capture subgroup differences, thereby limiting development of targeted interventions. Latent Profile Analysis (LPA)(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), an individual-centered research method based on a probabilistic model, groups nurses with similar characteristics such that between-group differences are maximized while within-group differences are minimized. This method is particularly useful for uncovering unobserved heterogeneity that traditional methods may overlook. To date, no studies\u0026mdash;domestically or internationally\u0026mdash;have utilized latent profile analysis to classify subtypes of second victims. This study aims to fill this gap by applying LPA to classify second victim subtypes, thereby informing targeted interventions and fostering a supportive and nonpunitive patient safety culture.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Participants\u003c/h2\u003e\u003cp\u003eA stratified cluster sampling technique was employed. The flow of participants through the study was presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.As of 2024, a total of 92 medical institutions in our province were enrolled in the National Nursing Quality Data Platform, including 40 tertiary hospitals and 52 secondary hospitals. Following a 1:1.3 ratio, we randomly selected 16 tertiary hospitals and 21 secondary hospitals across Shanxi province. In May 2024, a questionnaire survey was conducted among clinical nurses who had experienced patient safety incidents (PSIs) in these selected hospitals. Electronic questionnaires were distributed via the Shanxi Nursing Quality Control Center (under the Provincial Health Commission). Nursing directors disseminated the survey with instructions emphasizing anonymous responses, the critical importance of findings for developing nurse support strategies, and the need for answers based on authentic personal experiences. The questionnaire employed a required-response format to ensure data authenticity and objectivity. Inclusion criteria were: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Registered nurses holding valid practicing certificates; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Direct involvement in \u0026ge;\u0026thinsp;1 PSI (e.g., as primary personnel, first discoverer, or key handler); (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Employment as clinical frontline nurses at survey time; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) Voluntary participation with written informed consent. Exclusion criteria were: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Trainee or student nurses; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Nurses on long-term leave (e.g., sick/maternity leave) during the study period. The study was approved by the Institutional Review Board (YXLL-2024-052), and it followed the principles set forth in the Declaration of Helsinki(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Instruments\u003c/h2\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e2.2.1 Socio‑demographic questionnaire\u003c/h2\u003e\u003cp\u003eSocio-demographic and work-related variables including age, gender, marital status, work experience, hospital level, nursing title level, monthly income, educational level, employment status, average monthly night shifts, and perceived supervisor support were collected using a self-designed questionnaire.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.2.2 Second Victim Experience and Support Tool, SVEST\u003c/h2\u003e\u003cp\u003eThe Chinese version of the Second Victim Experience and Support Tool (SVEST) was used (Cronbach's α\u0026thinsp;=\u0026thinsp;0.98). This 24-item scale comprises two domains: distress (1\u0026ndash;12 items) and support (13\u0026ndash;24 items). Items were rated on a 5-point Likert scale (1\u0026thinsp;=\u0026thinsp;strongly disagree to 5\u0026thinsp;=\u0026thinsp;strongly agree). The distress domain is positively scored, while the support domain is reverse-scored. The total score ranges from 24 to 120, with higher scores indicating less adequate support after experiencing adverse healthcare events(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.2.3 Simplified Coping Style Questionnaire, SCSQ\u003c/h2\u003e\u003cp\u003eThe Simplified Coping Style Questionnaire (SCSQ), culturally adapted for Chinese populations by Xie Yaning (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e)based on Folkman and Lazarus' Ways of Coping Questionnaire (WCQ), was administered. SCSQ was used to measure how nurses cope with stress, both positively and negatively, comprising two subscales:positive coping (12 items) and negative coping (8 items). Items are rated on a 4-point Likert scale (0\u0026thinsp;=\u0026thinsp;never adopt to 3\u0026thinsp;=\u0026thinsp;frequently adopt). The SCSQ has been validated in healthcare settings to assess nurses' coping strategies. For example, Zhang et al. utilized this scale to evaluate psychological status and coping patterns among cross-regional support nurses versus local nurses during public health emergencies, demonstrating good internal consistency (Cronbach's α\u0026thinsp;=\u0026thinsp;0.82)(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Statistic\u003c/h2\u003e\u003cp\u003eThis study mainly used latent profile analysis for data analysis. Unlike variable-centered approaches, LPA aims to identify distinct patterns of multiple variables that occur consistently across individuals rather than focusing solely on individual variables or their interactions(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). By doing so, LPA classifies individuals within heterogeneous populations into smaller, more homogeneous subgroups(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), revealing hidden information that these subgroups bring to light. First, we used the 24 SVEST items to run a latent profile analysis (LPA) and find distinct groups among nurses. Then, we used AIC based stepwised logistic regression to explore which factors were linked to each group. The above analyses were completed by Mplus 8.2with SPSS 25.0 software. The main evaluation indexes of the latent variable model were AIC, BIC, Entropy, and BLRT. Among them, the smaller the values of AIC, BIC, the better the model fit, Entropy is an index to evaluate the accuracy of category classification, which takes the value of 0\u0026thinsp;~\u0026thinsp;1. Entropy\u0026thinsp;\u0026ge;\u0026thinsp;0.8 indicates that the classification accuracy exceeds 90%. BLRT are used to compare the fit difference between k-1 and k-category models, and the p-value of both reaches a significant level indicating that the k-category model is better than the k-1 category model(\u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Total characteristics\u003c/h2\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, a total of 21961questionnaires were collected. Among these, 442 questionnaires were excluded as the nurses indicated no experience with patient safety incidents, resulting in 21,519 valid responses for analysis. Most of them were female (97.6%) and around 34 years old on average. About half had more than 10 years of experience, worked in secondary or tertiary hospitals, and earned less than \u0026yen;6,000/month. Regarding work schedules, 48.4% worked 5\u0026ndash;9 night shifts. Leadership support was frequently or always reported by 66.0% of participants. The mean SCSQ score was 57.45\u0026thinsp;\u0026plusmn;\u0026thinsp;10.36, suggesting moderate coping levels.\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\u003eTotal characteristics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal[n\u0026thinsp;=\u0026thinsp;2,1519; n (%)]\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003egender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e512 ( 2.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003efemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21007 (97.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eaverage Age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e33.80 (7.38)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003ework experience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;3 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2718 (12.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u0026ndash;10 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8473 (39.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;10 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10328 (48.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003emarital Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 Married\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16930 (78.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 Single\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4368 (20.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 Others\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e221 ( 1.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ehospital level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003esecondary Hospital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10974 (51.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003etertiary Hospital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10545 (49.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003edegree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003esecondary Vocational\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e287 ( 1.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eassociate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5383 (25.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ebachelor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15797 (73.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emaster\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e52 ( 0.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003enursing title level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ejunior\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14168 (65.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emid-Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5771 (26.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003esenior\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1580 ( 7.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eemployment Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003epermanent Employee\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6192 (28.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003econtract Employee\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15327 (71.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eincome\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;6,000Yuan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19320 (89.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6,000\u0026ndash;8,000 Yuan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2060 ( 9.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;8,000 Yuan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e139 ( 0.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eleadership support\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1276 ( 5.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003esometimes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6047 (28.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eoffen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9141 (42.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ealways\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5055 (23.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eaverage monthly night shifts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5483 (25.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u0026ndash;4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3962 (18.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u0026ndash;9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10411 (48.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10\u0026ndash;14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1494 ( 6.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e169 ( 0.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSCSQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSCSQ -positive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e37.89 (6.93)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSCSQ -negative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19.56 (5.78)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Latent profile analysis (LPA)\u003c/h2\u003e\u003cp\u003eLatent profile models with two to five classes were evaluated. Although BIC values monotonically decreased with additional classes, this trend is expected in very large samples (n\u0026thinsp;\u0026asymp;\u0026thinsp;21 000) and did not coincide with improvements in classification quality (Entropy\u0026thinsp;=\u0026thinsp;0.93\u0026ndash;0.94). Visual inspection indicated that extra classes merely represented proportional shifts along a single distress\u0026ndash;support continuum without generating qualitatively novel patterns. Although adding more classes made the model slightly better on paper, in such a big sample this is expected. But those extra classes didn\u0026rsquo;t really give us new insights\u0026mdash;they just split the same trend into smaller pieces. So we chose the simpler 2-class model because it\u0026rsquo;s easier to understand and apply in hospitals. Full fit statistics and alternative solutions are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\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\u003eIndicators for each latent profile of nurse subgroups as second victims\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAIC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBIC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEntropy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSmallest Class (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLargest Class (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eprob_min\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eprob_max\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eBLRT p\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,454,083.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,454,666.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,380,831.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,381,613.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,344,338.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,345,319.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,317,817.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,318,998.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Naming of latent profile\u003c/h2\u003e\u003cp\u003eWe found two clear groups as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e: one group of nurses who had high distress and felt they lacked support (we called this the \u0026lsquo;high-distress, low-support\u0026rsquo; group), and another group who felt less distress and had better support (\u0026lsquo;low-distress, high-support\u0026rsquo; group).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe specific scores and trend charts for each item of the distress and support dimensions in the SVEST scale for the two latent profile groups were detailed in the Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Univariate analysis\u003c/h2\u003e\u003cp\u003eUnivariate analysis with SVEST as the dependent variable (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) revealed significant distributional differences between latent profiles for the following factors: age, work experience, hospital level, nursing title level, average monthly night shifts, employment status, income level, leadership support, and SCSQ (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariate analysis with SVEST\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eProfile 1\u003c/p\u003e\u003cp\u003e[n\u0026thinsp;=\u0026thinsp;11295; n (%)]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eProfile 2\u003c/p\u003e\u003cp\u003e[n\u0026thinsp;=\u0026thinsp;10224; n (%)]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eOR[95%CI]\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003egender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e274 ( 2.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e238 ( 2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.96[0.80\u0026ndash;1.14]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003efemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11021 (97.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9986 (97.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eaverage Age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e34.03 (7.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e33.55 (7.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.01[1.01\u0026ndash;1.01]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003ework experience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;3 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1538 (13.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1180 (11.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.79[0.73\u0026ndash;0.86]\u003c/p\u003e\u003cp\u003e0.86[0.79\u0026ndash;0.93]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u0026ndash;10 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4305 (38.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4168 (40.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;10 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5452 (48.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4876 (47.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003emarital Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 Married\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8929 (79.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8001 (78.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.96[0.90\u0026ndash;1.03]\u003c/p\u003e\u003cp\u003e0.84[0.64\u0026ndash;1.10]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 Single\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2259 (20.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2109 (20.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 Others\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e107 ( 0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e114 ( 1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ehospital level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003esecondary Hospital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5441 (48.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5533 (54.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e1.27[1.20\u0026ndash;1.34]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003etertiary Hospital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5854 (51.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4691 (45.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003edegree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003esecondary Vocational\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e161 ( 1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e126 ( 1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e0.83[0.65\u0026ndash;1.05]\u003c/p\u003e\u003cp\u003e0.88[0.69\u0026ndash;1.11]\u003c/p\u003e\u003cp\u003e0.85[0.47\u0026ndash;1.53]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eassociate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2764 (24.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2619 (25.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ebachelor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8343 (73.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7454 (72.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emaster\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27 ( 0.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25 ( 0.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003enursing title level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ejunior\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7313 (64.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6855 (67.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e1.07[1.01\u0026ndash;1.14]\u003c/p\u003e\u003cp\u003e1.25[1.12\u0026ndash;1.39]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emid-Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3080 (27.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2691 (26.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003esenior\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e902 ( 8.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e678 ( 6.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eemployment Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePermanent\u003c/p\u003e\u003cp\u003eemployee\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3384 (30.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2808 (27.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.89[0.83\u0026ndash;0.94]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003econtract employee\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7911 (70.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7416 (72.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eincome\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;6,000Yuan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9968 (88.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9352 (91.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e1.41[1.29\u0026ndash;1.55]\u003c/p\u003e\u003cp\u003e1.67[1.18\u0026ndash;2.36]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6,000\u0026ndash;8,000 Yuan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1238 (11.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e822 ( 8.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;8,000 Yuan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e89 ( 0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e50 ( 0.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eleadership support\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e326 ( 2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e950 ( 9.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e1.41[1.23\u0026ndash;1.62]\u003c/p\u003e\u003cp\u003e3.70[3.24\u0026ndash;4.22]\u003c/p\u003e\u003cp\u003e9.68[8.40-11.15]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003esometimes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1971 (17.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4076 (39.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eoffen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5113 (45.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4028 (39.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ealways\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3885 (34.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1170 (11.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eaverage monthly night shifts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3067 (27.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2416 (23.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e0.98[0.90\u0026ndash;1.06]\u003c/p\u003e\u003cp\u003e0.81[0.76\u0026ndash;0.87]\u003c/p\u003e\u003cp\u003e0.64[0.57\u0026ndash;0.72]\u003c/p\u003e\u003cp\u003e0.60[0.44\u0026ndash;0.82]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u0026ndash;4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2196 (19.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1766 (17.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u0026ndash;9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5287 (46.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5124 (50.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10\u0026ndash;14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e672 ( 5.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e822 ( 8.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e73 ( 0.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e96 ( 0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSCSQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSCSQ -positive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e41.09 (5.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e34.37 (6.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e1.19[1.18\u0026ndash;1.20]\u003c/p\u003e\u003cp\u003e0.89[0.89\u0026ndash;0.90]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSCSQ -negative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19.81 (6.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19.27 (5.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal SVEST Score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e18.69[16.99\u0026ndash;20.57]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.5 multivariate analysis\u003c/h2\u003e\u003cp\u003eA multinomial stepwise regression based on AIC was conducted with Factors with significant differences in univariate analysis (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) as independent variables and potential categories of SVEST in nurses as dependent variables, and the results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, when comparing 2 prolies of SVEST, nurses with higher age and positive SCSQ tended to be classified into \u0026lsquo;Low-distress with high-support group\u0026rsquo; (OR\u0026thinsp;=\u0026thinsp;1.02, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001,95%CI [1.01\u0026ndash;1.02]; OR\u0026thinsp;=\u0026thinsp;1.19, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001,95%CI [ 1.18\u0026ndash;1.20]). A significant moderating effect of leadership support was observed between the two profiles (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Higher leadership support frequency correlated with reduced nurse distress and enhanced perceived support, with the strongest effects occurring when support was 'always' provided (OR\u0026thinsp;=\u0026thinsp;8.65,95%CI [7.43\u0026ndash;10.09]).For the same duration of work experience, longer working years ,negative SCSQ were associated with a higher likelihood of belonging to the \u0026lsquo;High-distress with low-support group\u0026rsquo; (OR\u0026thinsp;=\u0026thinsp;0.81, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01,95%CI [0.73\u0026ndash;0.90]; OR\u0026thinsp;=\u0026thinsp;0.70, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01,95%CI [0.61\u0026ndash;0.79]; OR\u0026thinsp;=\u0026thinsp;0.89, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01,95%CI [0.89\u0026ndash;0.90]).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariate analysis with SVEST\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOR[95%CI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP Value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eaverage Age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.02 [1.01\u0026ndash;1.02]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003ework experience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;3 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u0026ndash;10 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.81 [0.73\u0026ndash;0.90]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;10 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.70 [0.61\u0026ndash;0.79]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eleadership support\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003esometimes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.40 [1.21\u0026ndash;1.62]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eoffen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.54 [3.07\u0026ndash;4.08]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ealways\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.65 [7.43\u0026ndash;10.09]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSCSQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSCSQ -positive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.19 [1.18\u0026ndash;1.20]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSCSQ -negative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.89 [0.89\u0026ndash;0.90]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Summary of the study\u003c/h2\u003e\u003cp\u003eThe purpose of this study was to delineate psychological distress and support subgroups of nurses as the second victims and to explore the factors influencing distress and support. We rationalized the selection of two profiles and named them as \u0026lsquo;high-distress, low-support\u0026rsquo; group and \u0026lsquo;low-distress, high-support\u0026rsquo; group.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Characteristics of the Two Profile Groups\u003c/h2\u003e\u003cp\u003eThe results of the study showed that about 52.49% of the nurses were classified as \u0026lsquo;high-distress, low-support\u0026rsquo; group profile 1. The SVEST total score was 3.86 at a moderately high level, which is higher than Xu Hongning\u0026rsquo;s findings(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). According to the SVEST scale items 1 to 12, nurses generally feel self-blame and distress after experiencing PSIs, especially item 4 \"Feeling deeply self-blame for patient safety incidents\" has the highest score. However, the scores of item 11 \"Experiencing these events makes me not want to work in the medical industry\" and item 12 \"Because of the pressure, I sometimes want to quit my current job\" are relatively low, indicating that these nurses still tend to stay in their positions. This result is different from the international research by Finney et al.(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e), which may be related to the cultural background and the current employment environment in China: nurses' professional identity is often closely associated with \"dedication\", and they are more likely to attribute incidents to themselves rather than the system(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). This self-blame actually reflects a higher sense of professional responsibility. Compared with foreign nurses, nurses in China face greater practical employment pressure(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOn the other hand, items 13 to 24 of the SVEST scale indicate that the level of support received by nurses is generally low. Multiple studies have pointed out that insufficient support can easily lead nurses to feel helpless when facing events(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Therefore, it is crucial to establish a support system that is in line with the hospital culture in China and improve the systematic guarantee mechanism.\u003c/p\u003e\u003cp\u003e47.51%were assigned to the \u0026lsquo;low-distress, high-support\u0026rsquo; group profile 2. The SVEST total score was 3.19, markedly lower than the previous group's. Profile 2 scored the lowest on the distress dimension(items 1\u0026ndash;12) for the item \"Having experienced a patient safety event made me feel deeply guilty,\" but scored the highest on items such as \"These experiences made me not want to work in the healthcare industry\" and \"Sometimes I want to quit my current job.\" Meanwhile, their overall scores on the support dimension (items 13\u0026ndash;24) were relatively high, presenting a state of low self-blame, low retention intention, and relatively high perceived support. Multiple studies explain the relationships among these factors. When nurses experience adverse events and receive timely and adequate support from managers, colleagues, and the organization, they can quickly adjust their mindset to cope with the negative impacts and acquire more professional knowledge and skills(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). This reinforces their belief that they can achieve greater rewards in a better platform.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Factors Influencing Profile Membership\u003c/h2\u003e\u003cp\u003ehe use of Latent Profile Analysis (LPA) allows us to incorporate individual characteristics of each nurse, thereby identifying those who are more susceptible to external influences. This approach facilitates the exploration of potential factors contributing to this outcome and enables the provision of personalized guidance and support. Further study found that age, work experience, leadership support and SCSQ were influential factors of nurses as the second victims.\u003c/p\u003e\u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\u003ch2\u003e4.3.1 Work Experience\u003c/h2\u003e\u003cp\u003eWork experience is an exposure factor associated with increased nurse distress.Nurses who have long worked in high-intensity and high-risk clinical environments, are more susceptible to emotional exhaustion when facing safety incidents, Consistenting with the findings of Kuang Zixia(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Moreover, as nurses typically assume multiple roles\u0026mdash;including clinical experts, preceptors, and managers\u0026mdash;these incidents not only induced professional guilt but also raised concerns about institutional disciplinary actions and potential impacts on career advancement. Consequently, their post-incident distress was more severe and associated with increased turnover intention(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Therefore, their support needs should include psychological counseling, crisis management training, and career development guidance to alleviate distress.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\u003ch2\u003e4.3.2 Leadership Support\u003c/h2\u003e\u003cp\u003eLeadership support reduced the occurrence of distress to a large extent. Multiple studies confirm this finding. Cao et al.(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e) reported that nurses facing safety incidents fear blame from families, colleague indifference, and leader criticism, but proper organizational safety support enables positive coping and reduces distress. Similarly, Beata Dziedzic's(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e) study of 321 nurses found better support led to lower stress and improved emotional management post-incident. Administrative support was the predominant form of support perceived by nurses(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e), particularly highlighting the pivotal role of leadership-level support.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section3\"\u003e\u003ch2\u003e4.3.3 Age\u003c/h2\u003e\u003cp\u003eAge serves as an indicator of psychological maturity and life experience, and is closely associated with psychological capital. According to Zheng Ren et al.(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e), older nurses tend to demonstrate stronger emotional regulation abilities and higher levels of psychological capital. Furthermore, Liang Nana et al.(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e) found that positive psychological capital enables nurses to effectively manage challenges and develop solutions by utilizing external resources and support, which in turn helps alleviate occupational stress. Consequently, compared to their younger counterparts, older individuals generally possess a broader range of coping strategies to handle adversity and stress, making them more likely to be categorized into the \u0026lsquo;low-distress, high-support\u0026rsquo; group.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section3\"\u003e\u003ch2\u003e4.3.4 SCSQ Strategies\u003c/h2\u003e\u003cp\u003eAfter PSIs, the use of different coping strategies markedly affects an individual's distress level. SCSQ strategies are generally divided into positive and negative types. Positive strategies include seeking social support, cognitive reframing, and engaging in problem-solving activities. Wang J et al's study confirmed this (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e) that nurses who frequently employ positive coping exhibit lower perceived stress, especially in psychiatric settings. Conversely, negative strategies such as relying on others to solve problems or using alcohol or tobacco for emotional relief can worsen burnout and intensify psychological burden, thereby amplifying negative emotions(\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Limitations\u003c/h2\u003e\u003cp\u003eThis study has several limitations that should be considered. Firstly, the data was gathered through self-reporting, which may be vulnerable to some degree of bias. Secondly,, the study utilized a cross sectional design that did not permit the establishment of causal relationships between the findings.\u003c/p\u003e\u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eOur study grouped distress and support in the second victim nurses into two subgroups (\u0026lsquo;high-distress, low-support\u0026rsquo; group and \u0026lsquo;low-distress, high-support\u0026rsquo; group), each presenting different characteristics. Such a division provided us with a deeper understanding of distress and support among older the second victim nurses. Our findings suggest that distress and support is influenced by several factors, including: work experience、leadership support、age、SCSQ Strategies. By comparing subgroups, we can identify populations disproportionately affected by distress and lower support and develop targeted interventions accordingly. Medical institutions should establish a stratified and precision-focused safety culture support system: For nurses in the \u0026lsquo;high-distress, low-support\u0026rsquo; group, priority should be given to providing psychological counseling, crisis intervention, clear institutional guarantees, and career development training, while strengthening supportive behaviours from leadership to enhance their coping capacity and support received after patient safety incidents. For the \u0026lsquo;low-distress, high-support\u0026rsquo; group, efforts should focus on consolidating psychological capital and enhancing their sense of professional value through empowerment mechanisms. Furthermore, it is recommended that support measures be preemptive and structured, integrating leadership support and systemic support into hospital management systems to form a full-process support loop encompassing \u0026lsquo;prevention before incidents, support during incidents, and growth after incidents\u0026rsquo;.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Medical Ethical Committee of Shanxi Bethune Hospital (YXLL-2024-052) and informed consent was obtained from the participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. L.W. , H.L. and X.Z. led the experiments . Material preparation, data collection and analysis were performed by \u0026nbsp;L.N., K.Y. ,W.W and H.L.The first draft of the manuscript was written by L.N., and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Science Fundation of Shanxi Bethune Hospital (grant number: 2023YH05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThanks to the Shanxi Nursing Quality Control Center in China for providing platforms for free data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWu Chao, Zhang Hongli, Hu Mengyi, Li Lu, Yuan Weiyun, Sa Zhen, et al. 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BMC Nurs. 2025 Mar 27;24(1):331. \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":"Patient safety incidents, Second victims, Psychological Distress and Support, Latent Profile Analysis","lastPublishedDoi":"10.21203/rs.3.rs-7806222/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7806222/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eNurses involved in patient safety incidents (PSIs) often become \"second victims,\" suffering significant psychological and professional distress. Rather than being a homogeneous group, these nurses exhibit diverse experiences, suggesting the need for a person-centered approach to better understand their distinct support needs. This study employed Latent Profile Analysis (LPA)\u0026mdash;a person-centered method ideal for identifying hidden subgroups\u0026mdash;to classify second victim nurses based on their distress and support patterns, with the aim of informing tailored intervention strategies.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA stratified cluster sampling technique was employed in May 2024 among clinical nurses from 16 tertiary hospitals and 21 secondary hospitals in Shanxi Province, China, who had experienced PSIs. Participants completed a socio-demographic questionnaire, the Chinese version of the SVEST, and the Simplified Coping Style Questionnaire (SCSQ). Latent Profile Analysis (LPA) was performed on the 24 SVEST items to identify homogeneous subgroups. Multinomial logistic regression was used to examine associations between subgroup membership and demographic, work-related, and coping strategy variables.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAmong 21,519 nurses analyzed, a two-profile model best fit the data. Profile 1 (52.5%, n\u0026thinsp;=\u0026thinsp;11,295), labeled the \u0026lsquo;high-distress, low-support\u0026rsquo; group, reported higher psychological distress (e.g., guilt, self-blame) and lower perceived support. Profile 2 (47.5%, n\u0026thinsp;=\u0026thinsp;10,224), labeled the \u0026lsquo;low-distress, high-support\u0026rsquo; group, reported lower distress and higher support. Multivariate analysis revealed that nurses in the \u0026lsquo;low-distress, high-support\u0026rsquo; group were significantly more likely to be older (OR\u0026thinsp;=\u0026thinsp;1.02, 95%CI: 1.01\u0026ndash;1.02), use positive coping strategies (OR\u0026thinsp;=\u0026thinsp;1.19, 95%CI: 1.18\u0026ndash;1.20), and perceive higher levels of leadership support ('always' supported: OR\u0026thinsp;=\u0026thinsp;8.65, 95%CI: 7.43\u0026ndash;10.09). Conversely, nurses with longer work experience (\u0026gt;\u0026thinsp;10 years vs. \u0026lt;3 years: OR\u0026thinsp;=\u0026thinsp;0.70, 95%CI: 0.61\u0026ndash;0.79) and those using negative coping strategies (OR\u0026thinsp;=\u0026thinsp;0.89, 95%CI: 0.89\u0026ndash;0.90) were more likely to belong to the \u0026lsquo;high-distress, low-support\u0026rsquo; group.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study is the first to identify distinct profiles of second victim nurses using LPA, revealing significant heterogeneity in their experiences of distress and support. The findings underscore the critical influence of leadership support, coping strategies, age, and work experience. Developing targeted, hierarchical support systems is essential\u0026mdash;providing intensive psychological and institutional support for the high-distress group, and reinforcing psychological capital and professional value for the low-distress group. Integrating proactive leadership and systemic support into hospital management is recommended to create a comprehensive support loop.\u003c/p\u003e","manuscriptTitle":"Psychological Distress and Support Profiles among Healthcare Second Victims: A Latent Profile Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-19 14:39:34","doi":"10.21203/rs.3.rs-7806222/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":"43b25ba6-3dc1-46bc-bbd8-11b86cba2c15","owner":[],"postedDate":"November 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-18T09:26:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-19 14:39:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7806222","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7806222","identity":"rs-7806222","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}