Study on the Types and Influencing Factors of Compassion Fatigue Among Psychiatric Nurses Based on Latent Profile Analysis

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The ultimate goal is to establish a foundation for developing targeted intervention strategies, thereby alleviating the occupational psychological burden on nurses and enhancing their mental health. Methods A convenience sampling approach was employed to recruit 297 clinical psychiatric nurses. Data were collected using the General Information Questionnaire, the Chinese Version of the Empathy Fatigue Scale, the Nurse Stress Scale, the Social Support Scale, and the Simplified Coping Style Questionnaire. Latent profile analysis was conducted using the three dimensions of the Empathy Fatigue Scale as observed variables, and Latent Profile Analysis was utilized to examine the factors influencing empathy fatigue among psychiatric nurses. Results Psychiatric nurses can be categorized into three latent profiles concerning empathy fatigue: the mild fatigue-traumatic stress type (18.5%),Moderate Fatigue - Low Burnout Group(63.9%), and the severe fatigue type (17.5%). Multivariate logistic regression analysis identified several risk factors for empathy fatigue, including advanced age, increased frequency of night shifts, elevated scores on the Nurse Stress Scale, and higher scores on negative coping strategies. Conversely, protective factors comprised higher scores on the Social Support Scale, enhanced positive coping strategies, and greater psychological resilience. Conclusion Psychiatric nurses demonstrate three distinct latent profiles of empathy fatigue, characterized by significant heterogeneity. Nursing managers can develop targeted interventions by considering these influencing factors to mitigate empathy fatigue within the nursing workforce. Health sciences/Health care Health sciences/Health occupations Biological sciences/Psychology Social science/Psychology Health sciences/Risk factors Nurses Empathy fatigue Latent profile analysis Figures Figure 1 1. Introduction The World Health Organization (WHO) released the 2025 World Mental Health Report. Integrating the latest global data since 2021, the report reveals that over 1 billion people worldwide are living with mental disorders[ 1 ]. This figure underscores the severe current status of global mental health and subjects the psychiatric medical service system to unprecedented pressure. As the core workforce of mental health services, psychiatric nurses exhibit significantly higher incidence rates of anxiety, depression, and somatic symptoms compared to general ward nurses and the general population[ 2 ]. Previous studies have confirmed that the prevalence of workplace violence in psychiatric settings is considerably higher than in other medical environments[ 3 ][ 4 ]. This high-risk characteristic is closely associated with the unique context of psychiatric wards, the nature of patients’ conditions, and the particularities of service delivery models.Furthermore, nurses account for 44% of the mental health workforce[ 5 ]. Beyond possessing solid professional skills, they are required to demonstrate strong emotional regulation and interpersonal interaction abilities. In daily practice, psychiatric nurses not only provide specialized care to patients with psychological distress, self-harm, or suicidal tendencies but also frequently navigate challenging situations such as clinical aggression and interpersonal conflicts[ 6 ][ 7 ]. Two-thirds of psychiatric nurses have reported experiencing multi-level work stressors, including those from patients/caregivers, colleagues, and organizational management[ 8 ]. The cumulative effect of these acute (e.g., violent attacks) and chronic (e.g., staffing shortages) stressors not only impairs nurses’ physical and mental health as well as the quality of their practice[ 9 ] but also increases the risk of burnout[ 10 ], job dissatisfaction[ 11 ], and other adverse outcomes. Ultimately, this leads to a rising turnover rate[ 12 ], further exacerbating the shortage of mental health service resources. Among the occupational mental health issues affecting psychiatric nurses, compassion fatigue (CF) is relatively common. CF refers to a phenomenon wherein individuals, due to prolonged indirect exposure to others’ traumatic events or distressing experiences, develop persistent empathy stress, which in turn leads to regression in their own empathy ability, accompanied by secondary traumatic stress symptoms and burnout[ 13 ]. Repeated exposure to patients’ traumatic experiences and distressing emotions, coupled with the daily demand for high-intensity emotional labor, exerts a synergistic effect in triggering CF[ 14 ][ 15 ].In recent years, Latent Profile Analysis (LPA)—a person-centered research method—has been widely applied in studies exploring heterogeneous groups in healthcare professionals’ occupational mental health. By classifying individuals based on the response patterns of explicit indicators, LPA facilitates an in-depth exploration of potential categorical differences within a population[ 16 ]. In domestic studies, researchers have successfully used LPA to identify distinct subtypes of CF among nursing interns and develop predictive models[ 17 ]. Among general hospital nurses, subgroups with significantly different CF scores have been distinguished based on moral resilience levels[ 18 ], while among oncology nurses, distinct CF profiles have been identified according to mindfulness self-compassion levels[ 19 ].In international research, a study on Spanish palliative care healthcare professionals, based on overall professional quality of life, clearly delineated two subgroups: "Low Quality" and "High Quality"[ 20 ]. More notably, a study on Japanese public health nurses integrated multi-dimensional empathy characteristics and secondary traumatic stress indicators to identify four unique profiles, including "High Traumatic Stress Type" and "High Empathy-High Personal Distress Type"[ 21 ]. These findings fully highlight the complexity and heterogeneity of CF response patterns among healthcare professionals. However, current latent profile research on compassion fatigue (CF) among psychiatric nurses remains limited. The vast majority of studies adopt a "variable-centered" approach, treating CF as a homogeneous continuous variable and conducting only intergroup comparisons and correlation analyses based on total or mean scores[ 22 ]. A core limitation of this method is that it overlooks individual differences among psychiatric nurses in terms of stressors, coping resources, and cognitive appraisal patterns, failing to uncover potential latent classes of CF within the population. This hinders the accurate identification of truly high-risk subgroups, ultimately precluding the provision of empirical support for developing classification-specific and stratified precision interventions. In contrast, by focusing on the classificatory characteristics of individuals’ overall response patterns, Latent Profile Analysis (LPA) can effectively address this research gap⁠[ 16 ].The ABC-X model[ 23 ] is a classic theoretical framework for stress and crisis. Its core mechanism posits that external stressful events (A), through dynamic interplay with individuals’ cognitive appraisal (B) and available coping resources (C), ultimately determine the formation of their adaptation outcomes (X)[ 24 ][ 25 ]. Based on this model, the present study hypothesizes that individuals’ resources (e.g., social support, psychological resilience) as well as their cognitive appraisal of and coping tendencies toward stressful events (i.e., active or passive coping) may predict the specific impact of such events on individuals—namely, CF.In summary, grounded in the ABC-X model and incorporating LPA, this study takes the latent class structure of CF among psychiatric nurses as its core research question. The specific research pathway is as follows: First, LPA will be employed to identify the latent classes of CF in psychiatric nurses; second, to systematically examine the effects of multidimensional variables—including stressors (e.g., workload), resources (e.g., psychological resilience, organizational support), and cognitive factors (e.g., coping styles)—on latent class membership. By revealing the population heterogeneity of CF among psychiatric nurses and accurately identifying high-risk groups, this study aims to provide a scientific basis for targeted prevention and intervention strategies, safeguard nurses’ physical and mental health, and ultimately enhance the quality of mental health services. 2. Materials and methods 2.1 Study Subjects Research Methods: This study adopted a convenience sampling method. Clinical nurses from 5 psychiatric hospitals in Xuzhou and Huaibei cities were recruited as research participants in October 2025, and data were collected via self-administered questionnaires.Inclusion criteria: (1) Registered and currently employed clinical nurses with at least 1 year of working experience in psychiatric wards; (2) Voluntarily agree to participate in this study; (3)Free from severe physical illnesses or mental disorders, and capable of understanding and completing the questionnaire independently.Exclusion criteria: (1) Nurses on further education, internships, or rotation in psychiatric departments; (2) Individuals unable to complete the survey due to reasons such as leave of absence or resignation.Based on the sample size calculation method for cross-sectional studies, the required sample size was determined to be 5–10 times the number of research variables. With 30 independent variables included and a 20% attrition rate accounted for invalid questionnaires, a minimum of 180 participants was required. Ultimately, a total of 298 participants were enrolled in this study. 2.2 Research Instruments 2.2.1 General Information Gender, age, marital status, educational background, Body Mass Index (BMI), years of work experience, professional title, nature of work, frequency of night shifts per week. 2.2.2 Professional Quality of Life Scale (ProQOL) ProQOL was developed by Dr. Stamm[26] and consists of three dimensions: Compassion Satisfaction, Burnout, and Secondary Traumatic Stress. Among them, Compassion Satisfaction represents a positive trend, while Burnout and Secondary Traumatic Stress represent negative trends. In 2013, Chen Huaying[27]nd colleagues translated the scale into Chinese and tested its reliability and validity. The total Cronbach’s α coefficient of the Chinese version was 0.91, and the Cronbach’s α coefficients for the three dimensions were 0.87, 0.73, and 0.84, respectively. All items of the scale use a 5-point Likert scale, with scores ranging from 1 to 5 (from "Never" to "Always"). A small number of items are reverse-scored. The total score cut-off values for the three dimensions are 27 points, and > 17 points, respectively. 2.2.3 Nurse Stress Scale This scale was compiled and translated by Li Xiaomei et al[28]. It consists of 5 dimensions and 35 items, including Nursing Profession and Work (7 items), Workload and Time Allocation (5 items), Work Environment and Resources (3 items), Patient Care (11 items), and Management and Interpersonal Relationships (9 items). A 4-point rating scale is used for the scale, with scores ranging from 1 to 4 (from "Strongly Disagree" to "Strongly Agree"). The total score ranges from 35 to 140, where 35–70 points indicate mild stress, 71–105 points indicate moderate stress, and 106–140 points indicate severe stress. A higher score indicates greater work stress among nurses. The total Cronbach’s α coefficient of this scale is 0.94, and the test-retest correlation coefficient is 0.79, demonstrating good reliability and validity. 2.2.4 Perceived Social Support Scale (PSSS) The PSSS was developed by Zimet et al. in 1988. Its Chinese version was translated by Jiang Qianjin et al[29]. The scale consists of 12 items, divided into three dimensions: Family Support, Friend Support, and Other Support. A 7-point Likert scale is used for scoring (1 = Strongly Disagree, 7 = Strongly Agree). The total score ranges are categorized as follows: 12–36 points for low support, 37–60 points for moderate support, and 61–84 points for high support. The Cronbach’s α coefficient of the total scale is 0.88, and the test-retest reliability is 0.85. 2.2.5 Resilience Scale The scale refers to the ability to recover from negative experiences and adapt flexibly to the ever-changing external environment. Campbell-Sills & Stein (2007) revised this scale into the Brief Resilience Scale, which consists of 10 items. A 4-point rating scale is used for scoring (1 = Completely Disagree; 2 = Somewhat Disagree; 3 = Somewhat Agree; 4 = Completely Agree), and a higher score indicates a higher level of resilience[30]. 2.2.6 Simplified Coping Style Questionnaire (SCSQ) The SCSQ was developed by Lazarus et al. in 1985, and translated into Chinese by Yaning Xie in 1988[31]. It consists of 20 items, divided into two subscales: Positive Coping and Negative Coping. A 4-point Likert scale is used for scoring (0 = Never Adopt, 4 = Often Adopt). A higher score on a subscale indicates a stronger tendency toward the corresponding coping style. 2.3 Methods of Data Collection and Quality Control Online questionnaires were designed via the Wenjuanxing platform. With the assistance of the hospital nursing department, questionnaire links were distributed to psychiatric nurses who met the inclusion criteria. The homepage of the questionnaire provided a detailed explanation of the study’s purpose, significance, data usage, and privacy protection measures; participants could proceed to fill out the questionnaire only after providing informed consent. To ensure data quality, logical validations were set up, including mandatory responses for all items and a restriction that only one submission could be made per IP address and device. A total of 320 questionnaires were distributed in this survey, and 318 were returned. Among these, 18 questionnaires with incomplete responses or logical inconsistencies were excluded. Finally, 297 valid questionnaires were obtained, resulting in an effective recovery rate of 92.81%. 2.4 Statistical Methods In this study, SPSS Statistics 21.0 was used to conduct descriptive statistics and correlation analysis of the research variables. Mplus was employed to perform LPA for exploring the latent categories of empathy fatigue among psychiatric nurses. First, the scores of each dimension of the Empathy Fatigue Scale were standardized, and a model was established. Starting from 1 category, the number of latent categories was increased sequentially, and the optimal latent category model was selected based on both the indicators of the number of categories and practical significance. For the classified data, chi-square tests (χ² tests) and one-way analysis of variance (ANOVA) were used to compare the differences in empathy fatigue scores among nurses with different empathy fatigue statuses in terms of demographic data and work conditions. Additionally, multivariate logistic regression analysis was adopted to identify the influencing factors of empathy fatigue categories in psychiatric nurses. 2.5 Ethical Approval This study has been approved by the Medical Ethics Committee of Xuzhou Oriental People's Hospital, Jiangsu Province, China (Approval No:202501024003). Prior to data collection, all participants signed a written informed consent form. Researchers fully explained the study purpose, procedures, potential risks, and benefits to participants, with particular emphasis on their right to withdraw from the study at any time without conditions or negative consequences. All data processing was conducted in strict compliance with ethical guidelines, and strict confidentiality was maintained throughout the study. 3. Results 3.1 Selection of the Optimal Model and Naming of Categories A total of 5 models were fitted in this study, and the model fitting results are shown in Table 1 . The Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and adjusted Bayesian Information Criterion (aBIC) gradually decreased as the number of categories increased, indicating an improvement in model fitting. The entropy values of all models were greater than 0.8, which means the classification accuracy exceeded 90%. When the number of categories was 4, the result of the Lo-Mendell-Rubin adjusted likelihood ratio test (LMR) was 0.6740 (> 0.05), suggesting that the 4-category model was not significantly better than the 3-category model. The population probability distribution of the 3-category model was balanced (18%/71%/11%), and the proportion of each category was greater than 5%, with no tiny latent categories. Based on the comprehensive consideration of the above indicators, the 3-category model was determined as the optimal solution. The empathy fatigue of psychiatric nurses could be divided into 3 latent categories, with their membership probabilities being 18.4%, 71.1%, and 10.5% respectively. By analyzing the characteristics of the average item scores across the three dimensions (Compassion Satisfaction, Secondary Traumatic Stress, and Burnout), three empathy fatigue profiles were identified ( Figure 1 ). Profile 1 (Mild Fatigue - Traumatic Stress Group, n = 55, 17.5%): The average item score of the Compassion Satisfaction dimension was > 3.7, the average item score of the Burnout dimension was 1.7. This profile was characterized by Compassion Satisfaction as the dominant factor and a relatively mild degree of empathy fatigue. Profile 2 (Moderate Fatigue - Low Physical Fatigue Group, n = 190, 63.9%): The average item score of the Compassion Satisfaction dimension was 1.7, and the average item score of the Burnout dimension was < 2.7. All three dimensions fell at the medium level among the three groups, but the Burnout level was significantly higher within this group. Profile 3 (Severe Fatigue Group, n = 52, 18.5%): The average item score of the Compassion Satisfaction dimension was 1.7, and the average item score of the Burnout dimension was > 2.7. This profile exhibited the characteristic of severe empathy fatigue driven by the combined effects of multiple dimensions. Table 1. Fitting Results of Latent Class Analysis on Compassion Fatigue Among Psychiatric Nurses. Model AIC BIC aBIC Entropy LMR BLRT Categorical probability 1 1829.498 1851.660 1832.632 — — — 1 2 1623.708 1660.645 1628.931 0.820 0.0000 0.000 0.491/0.508 3 1513.466 1565.178 1520.779 0.818 0.0185 0.0004 0.185/0.639/0.175 4 1462.974 1529.461 1472.377 0.840 0.6740 0.0000 0.114/0.185/0.327/0.377 5 1399.889 1481.151 1411.382 0.810 0.6892 0.0000 0.023/0.572/0.151/0.252 /0.106 Table 2. Univariate Analysis of Latent Classes of Compassion Fatigue in Psychiatric Nurses [n=297, n(%)]. Project Group C1(N=55) C2(N=190) C3(N=52) Statistics P-Value Sex Female 50(90.9%) 169(89.9%) 49(94.2%) 1.328 a 0.515 Male 5(9.1%) 21(11.1%) 3(5.8%) Marital status Unmarried 8(14.50%) 41(21.60%) 8(15.40%) 5.882 a 0.208 Married 45(81.80%) 148(77.90%) 42(80.80%) Divorced 2(3.6%) 1(0.5%) 2(3.8%) Educational level Junior college 5(9.1%) 10(5.3%) 10(19.2%) 19.932 d ＜0.001 Undergraduate 46(83.6%) 179(94.2%) 40(76.9%) Postgraduate 4(7.3%) 1(0.5%) 2(3.8%) Age 33.47±6.65 34.85±7.24 38.12±8.17 5.89 b 0.003 BMI 22.85(2.80) 22.33(2.93) 23.13(2.88) 1.85 b 0.158 Work Experience(Y) 11.87±7.57 12.19±7.84 15.17±9.40 3.09 b 0.047 Professional Title Nurse 3(5.5%) 24(12.6%) 6(11.5%) 11.204 d 0.208 Senior Nurse 10(18.2%) 53(27.9%) 7(13.5%) Charge Nurse 27(49.1%) 81(42.6%) 26(50.0%) Associate Chief Nurse 14(25.5%) 28(14.7%) 11(21.2%) Chief Nurse 1(1.8%) 4(2.1%) 2(3.8%) Nature of Work Day Shift 14(25.5%) 45(23.7%) 18(34.6%) 3.594 d 0.548 Night Shift 41(74.5%) 144(75.8%) 34(65.4%) Night Shifts (Shifts/Week) 0 11(20.0%) 32(16.8%) 10(19.2%) 18.908 a 0.004 ＜2 12(21.8%) 37(19.5%) 8(15.4%) 2 23(41.8%) 97(51.1%) 15(28.8%) ＞2 9(16.4%) 24(12.6%) 19(36.5%) Nurse Stress Scale 83.04±11.26 93.21±15.42 105.58±11.21 34.39 b ＜0.001 PSSS 72.15±10.08 59.71±11.40 51.67±13.18 43.88 b ＜0.001 Negative Coping 10.31±4.23 14.77±5.95 14.96±7.61 12.59 b ＜0.001 Active Coping 31(27，36) 27(23，34) 24(19,35) 15.24 c ＜0.01 Resilience Scale 34(30，40) 29.5(27，30) 29.5(27，30) 37.09 c ＜0.01 Note: a: Chi-square value; b: F-value; c: H-value; d: Fisher's exact test. PSSS: Perceived Social Support Scale; C1: Mild Fatigue - Traumatic Stress Group; C2:Moderate Fatigue - Low Burnout Group; C3: Severe Fatigue Group. Table 3. Assignment of Independent Variables. Independent Variable Assignment Setting Educational Level Junior College= 1 Undergraduate= 2 Postgraduate=3 Age Original Value Input Work Experience(Y) Original Value Input Night Shifts (Shifts/Week) 0=0，＜2=1，2= 2，＞2=3 Nurse Stress Scale Original Value Input PSSS Original Value Input Resilience Scale Original Value Input Active Coping Original Value Input Negative Coping Original Value Input Table 4. Multivariate Logistic Regression for Latent Classes of Compassion Fatigue Among Psychiatric Nurses. Moderate Fatigue - Low Burnout Group Severe Fatigue Group B SE Wald 2 P-value OR Value B SE Wald 2 P-value OR Value Intercept 7.454 3.225 5.343 0.021 1.103 4.267 0.067 0.796 Age 0.066 0.044 2.250 0.134 1.068(0.980,1.164) 0.118 0.059 4.055 0.044 1.126(1.003,1.263) Night Shifts (0) 0.280 0.709 0.156 0.693 1.324(0.330,5.317) -1.851 0.935 3.918 0.048 0.157(0.025, 0.982) Nurse Stress Scale 0.008 0.017 0.259 0.611 1.009(0.976,1.042) 0.102 0.025 16.135 ＜0.001 1.107(1.054,1.164) PSSS -0.072 0.023 10.127 0.001 0.931(0.890,0.973) -0.112 0.028 16.033 ＜0.001 0.894(0.847,0.945) Active Coping -0.135 0.040 11.437 ＜0.001 0.874(0.809,0.945) -0.265 0.053 24.842 ＜0.001 0.767(0.691,0.852) Negative Coping 0.244 0.052 21.942 ＜0.001 1.277(1.153,1.414) 0.275 0.065 17.770 ＜0.001 1.317(1.159,1.497) Resilience Scale -0.211 0.054 15.465 ＜0.001 0.810(0.729,0.900) -0.131 0.065 4.100 0.043 0.877(0.773,0.996) 3.2 Analysis of Differences in Empathy Fatigue Categories Among Psychiatric Nurses The difference analysis showed ( Table 2 ) that there were statistically significant differences in educational background, age, years of work experience, frequency of night shifts, and scores on the Nurse Stress Scale, Perceived Social Support Scale, Resilience Scale, and Simplified Coping Style Questionnaire among the three latent categories (P < 0.05). 3.3 Multivariate Logistic Regression Analysis of Empathy Fatigue Categories Among Psychiatric Nurses Multivariate logistic regression analysis was conducted, with the "Mild Fatigue-Traumatic Stress Subgroup" designated as the reference group. Independent variables were coded according to the specifications outlined in Table 3 . Only variables that demonstrated statistical significance in the preliminary univariate analysis were incorporated into the multivariate logistic regression model. The results indicated that, relative to the mild fatigue subgroup, participants with lower levels of perceived social support, a stronger tendency toward negative coping strategies, and poorer resilience were significantly more likely to be categorized into either the moderate fatigue subgroup or the severe fatigue subgroup. Furthermore, Moreover, unlike the moderate fatigue group, those who are older, work night shifts more frequently, or experience greater nursing stress are also more likely to be classified into the severe fatigue group( Table 4 ). 4. Discussion 4.1 There is heterogeneity in empathy fatigue among psychiatric nurses. Latent Profile Analysis (LPA) confirmed significant heterogeneity in CF among psychiatric nurses, which can be categorized into three distinct latent classes: the Mild Fatigue-Traumatic Stress Type (17.5%), Moderate Fatigue-Low Burnout Type (63.9%), and Severe Fatigue Type (18.5%). This finding verifies the existence of individual differences in CF among psychiatric nurses. Detailed explanations of each class are as follows:The core characteristic of the Mild Fatigue-Traumatic Stress Type is dominance of traumatic stress coupled with low treatment involvement in specific work contexts. Consistent with Figley’s compassion fatigue theory[32], prolonged exposure to patients’ traumatic events (e.g., suicide, self-harm)[33]results in significantly higher secondary traumatic stress (STS) scores in this group compared to the other two types. However, their work primarily consists of routine tasks with stable intensity, leading to only moderate elevation in burnout. Additionally, insufficient treatment involvement prevents them from gaining a sense of value from patients’ recovery, ultimately resulting in a severe lack of compassion satisfaction (CS).The Moderate Fatigue-Low Burnout Type (63.9%) is characterized by significantly lower burnout scores than the other groups, with moderate scores on the remaining dimensions. Psychiatric nurses’ long-term exposure to patients’ traumatic experiences induces high traumatic stress[34], and they struggle to obtain adequate CS from their work[35]. Although their work predominantly involves emotional and cognitive engagement (resulting in low physical fatigue), the sustained depletion from high traumatic stress and the lack of protective effects from low CS interact synergistically, ultimately placing them in a state of moderate fatigue. The Severe Fatigue Type (18.5%) is the most severe. Its formation is shaped by a vicious cycle of multiple interacting stressors, exacerbated by extremely high workload[36], persistent vigilance-related stress[37], lack of professional value[38], and frequent traumatic exposure[39]. These factors are mutually reinforcing: they weaken stress resistance, amplify negative experiences, and ultimately lead to a state of comprehensive exhaustion—marked by extremely high burnout and STS, alongside extremely low CS. 4.2 Analysis of Influencing Factors on Latent Profiles of Compassion Fatigue Among Psychiatric Nurses 4.2.1 Nurse Occupational Stress Work stress level is a significant risk factor influencing the latent class membership of CF among psychiatric nurses. The degree of stress endured by nurses is positively correlated with membership in high-CF classes—specifically, the higher the stress, the greater the likelihood of being classified into the severe fatigue class[40].Occupational stress is defined as a physical and mental stress state experienced by professionals in the workplace, resulting from a mismatch between occupational demands and personal perceptions, as well as discrepancies in work competence. High-responsibility, high-risk, and high-intensity clinical nursing work exposes nurses to substantial occupational stress, whose level far exceeds that of other occupations, with a stress prevalence rate as high as 25%–40%[41]. Chronic occupational stress places nurses in a prolonged state of chronic stress, inflicting severe cumulative harm on their physical and mental health[42]. This can lead to reduced job satisfaction, decreased organizational efficiency, and an elevated risk of nursing errors[43].Nurses’ occupational stress level is positively correlated with turnover rate, accelerating nurse attrition and exacerbating the nursing shortage, which exerts adverse impacts on nursing workforce development. To address this, hospitals may alleviate nurses’ stress by optimizing work schedules, providing psychological support, and offering career development opportunities. Individually, nurses should maintain a work-life balance, master relaxation techniques, expand social support networks, and enhance professional competence. 4.2.2 Social Support Social support systems serve as a protective factor against compassion fatigue (CF) among psychiatric nurses[44]. Their level of social support is positively correlated with membership in low-CF classes[45]—specifically, the more adequate the social support received, the higher the likelihood of being classified into low-fatigue groups. Due to the nature of their work, which requires high-intensity emotional investment and entails significant occupational stress, psychiatric nurses can benefit substantially from enhanced social support (e.g., from hospital management, colleagues, and family). Such support effectively buffers against emotional exhaustion by sharing psychological burdens to reduce the overconsumption of empathic resources, while simultaneously strengthening psychological safety and professional identity to sustain emotional resilience. Collectively, these effects lower the risk of developing high levels of CF.Therefore, constructing a comprehensive social support system encompassing hospital management initiatives, social/familial support, and multi-dimensional network development can enhance the emotional resilience of psychiatric nurses and mitigate the risk of high CF. 4.2.3 Psychological Resilience As an intrinsic psychological resource, psychological resilience exerts a buffering effect on CF among psychiatric nurses. The higher a nurse’s level of psychological resilience, the greater the probability of being classified into low-CF groups. Psychiatric nurses are required to manage patients’ complex emotions and unexpected situations in daily practice, frequently confronting the dual challenges of emotional exhaustion and occupational stress. Nurses with strong psychological resilience possess enhanced emotional regulation capabilities and stress coping efficacy—not only can they rapidly restore psychological balance after high-intensity emotional investment, avoiding the sustained depletion of empathic resources, but they can also flexibly adjust their cognition and coping strategies when facing work setbacks. Instead of falling into a state of negative exhaustion, they transform stressful events into motivation for growth. This intrinsic psychological toughness can directly buffer the onset of CF, enabling them to more easily maintain a low-fatigue state[46]. Therefore, hospitals can implement psychological resilience training and regular psychological assessments to help psychiatric nurses strengthen their intrinsic psychological resilience and combat CF. 4.2.4 Coping Styles Coping style adoption is associated with the latent classes of CF among psychiatric nurses[47][48]. Psychiatric nurses who adopt active coping strategies are more likely to be classified into low-CF groups, and active coping can be regarded as a key protective factor.Psychiatric nursing practice is often accompanied by stressful situations such as patients’ emotional crises and disease exacerbations. Active coping styles enable nurses to proactively mitigate the risk of empathic exhaustion: for instance, when facing patients’ aggressive behaviors, active copers resolve conflicts through professional communication skills rather than engaging in emotional confrontation; when encountering work setbacks, they proactively seek colleague supervision or draw on others’ experiences instead of self-denial. Such problem-solving-oriented coping strategies not only reduce unproductive emotional expenditure and avoid the overdraw of empathic resources but also enhance the sense of professional control through positive actions. Thereby, they lower the likelihood of being categorized into high-CF groups and facilitate the maintenance of a low-fatigue state.Therefore, hospitals can offer targeted training and foster a supportive culture, while nurses can proactively learn and practice active coping strategies—collectively reducing the risk of CF among psychiatric nurses. 4.2.5 Age Consistent with previous research [49], the present study found that older psychiatric nurses were more likely to be classified into the high compassion fatigue group. Age was statistically associated with the latent classes of compassion fatigue among psychiatric nurses. Specifically, with increasing age, physiological functions gradually decline; psychiatric nursing inherently involves sustained exposure to high-intensity scenarios (e.g., patients' sudden emotional outbursts and physical conflicts), and the diminished capacity to recover physical strength and energy may exacerbate emotional exhaustion due to chronic fatigue accumulation [49]. Additionally, older nurses typically have longer service tenure, and prolonged exposure to patients' negative emotions, traumatic events, and occupational stress leads to the depletion of compassion resources following years of continuous overexertion, thereby reducing their resilience in maintaining emotional engagement with patients. Furthermore, older nurses often face multiple role pressures: professionally, they may assume responsibilities such as preceptorship and managing complex cases, while personally, they may shoulder family obligations including supporting elderly relatives and caring for grandchildren. The accumulation of these multiple burdens further depletes their psychological energy. Therefore, hospitals can reduce the risk of older psychiatric nurses being categorized into the high compassion fatigue group by optimizing work schedules, supplementing compassion resources, and mitigating role-related stressors. 4.2.6 Shift Work Pattern Shift work pattern was associated with the latent classes of compassion fatigue among psychiatric nurses. Specifically, psychiatric nurses who did not work night shifts were significantly more likely to be categorized into the low compassion fatigue group compared with those who worked night shifts, suggesting that night shift work may be one of the risk factors for high compassion fatigue. For psychiatric nurses, night shift work poses multiple physical and psychological challenges: psychiatric patients are at a higher risk of condition deterioration during nighttime, requiring nurses to maintain continuous vigilance to respond to emergent situations. Frequent night shifts may disrupt circadian rhythms, thereby triggering a series of adverse physical and psychological reactions such as fatigue, anxiety, accelerated aging, and insomnia[50]; long-term exposure to such conditions may further translate into job burnout and compassion fatigue[51]. In contrast, nurses who do not work night shifts can maintain regular schedules and adequate rest, reducing emotional vulnerability caused by physical exhaustion. Additionally, they are more likely to receive sufficient team support during daytime work, which helps sustain stable emotional states and compassion resilience. Therefore, optimizing night shift scheduling and strengthening nighttime support can alleviate the physical and psychological depletion of psychiatric nurses caused by night shifts, thereby reducing the risk of high compassion fatigue. 5. Limitations and conclusions A core finding of the present study is that compassion fatigue among psychiatric nurses does not present a uniform pattern but rather exhibits significant group heterogeneity. Through latent profile analysis, we identified that multiple factors—including but not limited to age, stress, psychological resources, and coping styles—influence the manifestations of compassion fatigue in psychiatric nurses, and further delineated risk subgroups with distinct characteristics. Based on these findings, at the organizational level, resource support should be provided through precision management (e.g., structural adjustments and institutionalized support); at the individual level, systematic empowerment should be implemented to enhance psychological resilience (i.e., improving psychological flexibility and the ability to master adaptive coping strategies).The conclusions of this study should be interpreted in light of the following limitations: First, the cross-sectional design precludes the inference of definitive causal relationships. Second, the relative concentration of sample sources may limit the generalizability of the findings. Additionally, this study focused primarily on individual-level variables; future research should incorporate more in-depth organizational and environmental factors for comprehensive investigation.For subsequent studies, longitudinal follow-up designs could be adopted to depict the dynamic developmental trajectory of compassion fatigue and clarify its causal mechanisms. Furthermore, based on the risk subgroups and key influencing factors identified in this study, targeted intervention programs could be developed, implemented, and validated. This would provide high-level evidence-based support for establishing a scientific and efficient occupational health protection system for psychiatric nurses. Declarations Acknowledgements This study was supported by the Development and Molecular Mechanisms of a Cellular Model for Diabetes and Tinnitus Comorbidity. Data Availability Statement The article/Supplementary Material contains the original contributions made for the study; further questions should be addressed to the relevant authors. Author Contributions LD: Conceptualization, Writing - Original Draft, Formal analysis, Investigation, Writing - Review & Editing. JD: Formal analysis, Investigation, Resources, Data Curation, Writing - Review & Editing. HYS: Formal analysis, Investigation, Resources, Data Curation, Writing - Review & Editing. XXW: Formal analysis, Investigation, Data Curation, Writing Review & Editing. XCY：Formal analysis, Investigation, Data Curation, Writing Review & Editing. ALZ: Investigation, Resources, Data Curation, Writing - Review& Editing. TL, Conceptualization, Methodology, Writing - Review & Editing, Supervision, Project administration, Funding acquisition. All authors reviewed the manuscript. Funding This study was sponsored by the Development and Molecular Mechanisms of a Cellular Model for Diabetes and Tinnitus Comorbidity [No. S202510313079]. Ethical approval This was a retrospective study approved by the affiliated hospital of Xuzhou Medical University's ethics committee (No.202501024003). Informed consent was waived. Conflict of Interest The authors declare that they have no competing interests. Correspondence and requests for materials should be addressed to Ting Li. References World Health Organization. World mental health today: latest data[M]. World Health Organization, 2025. Jang S J, Son Y J, Lee H. Prevalence, associated factors and adverse outcomes of workplace violence towards nurses in psychiatric settings: A systematic review[J]. International journal of mental health nursing, 2022, 31(3): 450-468. Ferri P, Silvestri M, Artoni C, et al. 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Social Science & Medicine, 2024, 352: 117000. 李燕媚,洪荣梅,梁冬红,等.ABC-X模型在急诊危重症患者家属情绪状态中的应用研究[J].护理实践与研究,2016,13(20):148-149. Prieto L, Thorsen H, Juul K. Development and validation of a quality of life questionnaire for patients with colostomy or ileostomy[J]. Health and quality of life outcomes, 2005, 3(1): 62. STAMM B H. The concise ProQOL manual pocatello[EB / OL].( 2012-03-12) [2020-07-10]. http / / www. proqol. org. 陈华英,王卫红.中文版同情疲劳量表的信度、效度研究[J].中国护理管理,2013,13(04):39-41. 李小妹,刘彦君.护士工作压力源及工作疲溃感的调查研究[J].中华护理杂志,2000,(11):4-8. 姜乾金. 领悟社会支持量表[J]. 中国行为医学科学， 2001， 10(10): 41-43. Campbell‐Sills L, Stein M B. Psychometric analysis and refinement of the connor–davidson resilience scale (CD‐RISC): Validation of a 10‐item measure of resilience[J]. Journal of Traumatic Stress: Official Publication of The International Society for Traumatic Stress Studies, 2007, 20(6): 1019-1028. 解亚宁.简易应对方式量表信度和效度的初步研究[J].中国临床心理学杂志，1998，(02):53-54. Figley C R. Compassion fatigue: Toward a new understanding of the costs of caring[J]. 1995. O’Donnell O, House A, Waterman M. The co-occurrence of aggression and self-harm: systematic literature review[J]. Journal of affective disorders, 2015, 175: 325-350. Pearlman L A, Mac Ian P S. Vicarious traumatization: An empirical study of the effects of trauma work on trauma therapists[J]. Professional psychology: Research and practice, 1995, 26(6): 558. Newell J M, MacNeil G A. Professional burnout, vicarious trauma, secondary traumatic stress, and compassion fatigue[J]. Best practices in mental health, 2010, 6(2): 57-68. 姜宏婷, 钟耕坤, 卢庆华. 精神科护士工作压力源现状及影响因素分析[J]. 齐鲁护理杂志, 2018, 24(17): 82-84. 汤语忌, 栗文娟, 崔倩, 等. 精神科护士体面劳动感知及影响因素研究[J]. 护理学杂志, 2022, 37(17): 69-71. Konstantinou A K, Bonotis K, Sokratous M, et al. Burnout evaluation and potential predictors in a Greek cohort of mental health nurses[J]. Archives of psychiatric nursing, 2018, 32(3): 449-456. Malik S, Gunn S, Robertson N. The impact of patient suicide on doctors and nurses: a critical interpretive meta-synthesis[J]. Archives of suicide research, 2022, 26(3): 1266-1285. 彭燕,李华芳.不同特征精神科护士的共情疲劳及影响因素分析[J].精神医学杂志,2018,31(03):196-199. 贾泽娟, 田素斋. 国内外护士减压方法研究进展[J]. 中国卫生标准管理,2018,9(8): 3-6. Weigl T, Tölle AS, Seppelfrick T. Differential aspects of chronic work-related stress predict depression in registered and geriatric nurses[J]. Pflege,2021,22:1-8. Adriaenssens J, De Gucht V, Maes S. Determinants and prevalence of burnout in emergency nurses: A systematic review of 25 years of research[J]. Int J Nurs Stud, 2015, 52(2): 649-661. Adriaenssens J, De Gucht V, Maes S. Determinants and prevalence of burnout in emergency nurses: A systematic review of 25 years of research[J]. Int J Nurs Stud, 2015, 52(2): 649-661. Velando‐Soriano A, Ortega‐Campos E, Gómez‐Urquiza J L, et al. Impact of social support in preventing burnout syndrome in nurses: A systematic review[J]. Japan Journal of Nursing Science, 2020, 17(1): e12269. 任敏敏,王广梅,张丽,等.335名抗疫一线护理人员心理弹性对共情疲劳的影响[J].山东大学学报(医学版),2021,59(02):88-94. 郭玉江．应对方式与运动心理疲劳的关系：两种社会支持的调节效应［J］．北京体育大学学报，2017，40（12）： 64-71. DOI: 10.19582/j.cnki.11-3785/g8.2017.12.011. 杨芷．癌症患者照顾者的心理健康调查研究——照顾者反应、应对方式及疲劳感［D］．大连：大连医科大学，2015. 曾丽华,陈娴,朱珠,等.江苏省高年资护士共情疲劳的潜在类别分析[J].护理学杂志,2024,39(02):64-68. 韩悦,石婷婷.疲劳感和工作疏离感在轮值夜班护士昼夜节律改变与职业倦怠间的中介 作用 [J].工业卫生与职业病 ,2023,49(1):53-57. 陈珺仪,冯晓玲,古文珍,等.夜班对护士生理和生活状况的影响分析[J].护理实践与研究,2021,18(23):3487-3491 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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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8251260","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":562628158,"identity":"63e2cbe8-d85c-41e5-9b41-91ea24e1cb90","order_by":0,"name":"Li Dou","email":"","orcid":"","institution":"Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Dou","suffix":""},{"id":562628159,"identity":"fd09a3c8-951f-4eaa-b451-7f68d406a779","order_by":1,"name":"Jie Dou","email":"","orcid":"","institution":"Dongfang Hospital Affiliated to Xuzhou Medical 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1","display":"","copyAsset":false,"role":"figure","size":44289,"visible":true,"origin":"","legend":"\u003cp\u003eThree Latent Profile Characteristics of Compassion Fatigue in Psychiatric Nurses\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8251260/v1/8a5ce0db524d52285fe1b729.png"},{"id":100365507,"identity":"c16a0206-9827-4994-8857-4332dc3a16f2","added_by":"auto","created_at":"2026-01-16 07:55:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1069535,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8251260/v1/2a848a4d-8139-4aae-9bba-0360eaf96c80.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Study on the Types and Influencing Factors of Compassion Fatigue Among Psychiatric Nurses Based on Latent Profile Analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe World Health Organization (WHO) released the 2025 World Mental Health Report. Integrating the latest global data since 2021, the report reveals that over 1\u0026nbsp;billion people worldwide are living with mental disorders[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This figure underscores the severe current status of global mental health and subjects the psychiatric medical service system to unprecedented pressure. As the core workforce of mental health services, psychiatric nurses exhibit significantly higher incidence rates of anxiety, depression, and somatic symptoms compared to general ward nurses and the general population[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Previous studies have confirmed that the prevalence of workplace violence in psychiatric settings is considerably higher than in other medical environments[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e][\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This high-risk characteristic is closely associated with the unique context of psychiatric wards, the nature of patients\u0026rsquo; conditions, and the particularities of service delivery models.Furthermore, nurses account for 44% of the mental health workforce[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Beyond possessing solid professional skills, they are required to demonstrate strong emotional regulation and interpersonal interaction abilities. In daily practice, psychiatric nurses not only provide specialized care to patients with psychological distress, self-harm, or suicidal tendencies but also frequently navigate challenging situations such as clinical aggression and interpersonal conflicts[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e][\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Two-thirds of psychiatric nurses have reported experiencing multi-level work stressors, including those from patients/caregivers, colleagues, and organizational management[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The cumulative effect of these acute (e.g., violent attacks) and chronic (e.g., staffing shortages) stressors not only impairs nurses\u0026rsquo; physical and mental health as well as the quality of their practice[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] but also increases the risk of burnout[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], job dissatisfaction[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and other adverse outcomes. Ultimately, this leads to a rising turnover rate[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], further exacerbating the shortage of mental health service resources.\u003c/p\u003e \u003cp\u003eAmong the occupational mental health issues affecting psychiatric nurses, compassion fatigue (CF) is relatively common. CF refers to a phenomenon wherein individuals, due to prolonged indirect exposure to others\u0026rsquo; traumatic events or distressing experiences, develop persistent empathy stress, which in turn leads to regression in their own empathy ability, accompanied by secondary traumatic stress symptoms and burnout[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Repeated exposure to patients\u0026rsquo; traumatic experiences and distressing emotions, coupled with the daily demand for high-intensity emotional labor, exerts a synergistic effect in triggering CF[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e][\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].In recent years, Latent Profile Analysis (LPA)\u0026mdash;a person-centered research method\u0026mdash;has been widely applied in studies exploring heterogeneous groups in healthcare professionals\u0026rsquo; occupational mental health. By classifying individuals based on the response patterns of explicit indicators, LPA facilitates an in-depth exploration of potential categorical differences within a population[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In domestic studies, researchers have successfully used LPA to identify distinct subtypes of CF among nursing interns and develop predictive models[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Among general hospital nurses, subgroups with significantly different CF scores have been distinguished based on moral resilience levels[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], while among oncology nurses, distinct CF profiles have been identified according to mindfulness self-compassion levels[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].In international research, a study on Spanish palliative care healthcare professionals, based on overall professional quality of life, clearly delineated two subgroups: \"Low Quality\" and \"High Quality\"[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. More notably, a study on Japanese public health nurses integrated multi-dimensional empathy characteristics and secondary traumatic stress indicators to identify four unique profiles, including \"High Traumatic Stress Type\" and \"High Empathy-High Personal Distress Type\"[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. These findings fully highlight the complexity and heterogeneity of CF response patterns among healthcare professionals.\u003c/p\u003e \u003cp\u003eHowever, current latent profile research on compassion fatigue (CF) among psychiatric nurses remains limited. The vast majority of studies adopt a \"variable-centered\" approach, treating CF as a homogeneous continuous variable and conducting only intergroup comparisons and correlation analyses based on total or mean scores[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. A core limitation of this method is that it overlooks individual differences among psychiatric nurses in terms of stressors, coping resources, and cognitive appraisal patterns, failing to uncover potential latent classes of CF within the population. This hinders the accurate identification of truly high-risk subgroups, ultimately precluding the provision of empirical support for developing classification-specific and stratified precision interventions. In contrast, by focusing on the classificatory characteristics of individuals\u0026rsquo; overall response patterns, Latent Profile Analysis (LPA) can effectively address this research gap⁠[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].The ABC-X model[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] is a classic theoretical framework for stress and crisis. Its core mechanism posits that external stressful events (A), through dynamic interplay with individuals\u0026rsquo; cognitive appraisal (B) and available coping resources (C), ultimately determine the formation of their adaptation outcomes (X)[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e][\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Based on this model, the present study hypothesizes that individuals\u0026rsquo; resources (e.g., social support, psychological resilience) as well as their cognitive appraisal of and coping tendencies toward stressful events (i.e., active or passive coping) may predict the specific impact of such events on individuals\u0026mdash;namely, CF.In summary, grounded in the ABC-X model and incorporating LPA, this study takes the latent class structure of CF among psychiatric nurses as its core research question. The specific research pathway is as follows: First, LPA will be employed to identify the latent classes of CF in psychiatric nurses; second, to systematically examine the effects of multidimensional variables\u0026mdash;including stressors (e.g., workload), resources (e.g., psychological resilience, organizational support), and cognitive factors (e.g., coping styles)\u0026mdash;on latent class membership. By revealing the population heterogeneity of CF among psychiatric nurses and accurately identifying high-risk groups, this study aims to provide a scientific basis for targeted prevention and intervention strategies, safeguard nurses\u0026rsquo; physical and mental health, and ultimately enhance the quality of mental health services.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Study Subjects\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResearch Methods: This study adopted a convenience sampling method. Clinical nurses from 5 psychiatric hospitals in Xuzhou and Huaibei cities were recruited as research participants in October 2025, and data were collected via self-administered questionnaires.Inclusion criteria: \u0026nbsp;(1) Registered and currently employed clinical nurses with at least 1 year of working experience in psychiatric wards; \u0026nbsp;(2) Voluntarily agree to participate in this study; (3)Free from severe physical illnesses or mental disorders, and capable of understanding and completing the questionnaire independently.Exclusion criteria: \u0026nbsp;(1) Nurses on further education, internships, or rotation in psychiatric departments; \u0026nbsp;(2) Individuals unable to complete the survey due to reasons such as leave of absence or resignation.Based on the sample size calculation method for cross-sectional studies, the required sample size was determined to be 5–10 times the number of research variables. With 30 independent variables included and a 20% attrition rate accounted for invalid questionnaires, a minimum of 180 participants was required. Ultimately, a total of 298 participants were enrolled in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Research Instruments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.1 General Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGender, age, marital status, educational background, Body Mass Index (BMI), years of work experience, professional title, nature of work, frequency of night shifts per week.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.2 Professional Quality of Life Scale (ProQOL)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProQOL was developed by Dr. Stamm[26] and consists of three dimensions: Compassion Satisfaction, Burnout, and Secondary Traumatic Stress. Among them, Compassion Satisfaction represents a positive trend, while Burnout and Secondary Traumatic Stress represent negative trends. In 2013, Chen Huaying[27]nd colleagues translated the scale into Chinese and tested its reliability and validity. The total Cronbach’s α coefficient of the Chinese version was 0.91, and the Cronbach’s α coefficients for the three dimensions were 0.87, 0.73, and 0.84, respectively. All items of the scale use a 5-point Likert scale, with scores ranging from 1 to 5 (from \"Never\" to \"Always\"). A small number of items are reverse-scored. The total score cut-off values for the three dimensions are \u0026lt; 37 points, \u0026gt; 27 points, and \u0026gt; 17 points, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.3 Nurse Stress Scale\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis scale was compiled and translated by Li Xiaomei et al[28]. It consists of 5 dimensions and 35 items, including\u0026nbsp;Nursing Profession and Work\u0026nbsp;(7 items),\u0026nbsp;Workload and Time Allocation\u0026nbsp;(5 items),\u0026nbsp;Work Environment and Resources\u0026nbsp;(3 items),\u0026nbsp;Patient Care\u0026nbsp;(11 items), and\u0026nbsp;Management and Interpersonal Relationships\u0026nbsp;(9 items). A 4-point rating scale is used for the scale, with scores ranging from 1 to 4 (from \"Strongly Disagree\" to \"Strongly Agree\"). The total score ranges from 35 to 140, where 35–70 points indicate mild stress, 71–105 points indicate moderate stress, and 106–140 points indicate severe stress. A higher score indicates greater work stress among nurses. The total Cronbach’s α coefficient of this scale is 0.94, and the test-retest correlation coefficient is 0.79, demonstrating good reliability and validity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.4 Perceived Social Support Scale (PSSS)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe PSSS was developed by Zimet et al. in 1988. Its Chinese version was translated by Jiang Qianjin et al[29]. The scale consists of 12 items, divided into three dimensions: Family Support, Friend Support, and Other Support. A 7-point Likert scale is used for scoring (1 = Strongly Disagree, 7 = Strongly Agree). The total score ranges are categorized as follows: 12–36 points for low support, 37–60 points for moderate support, and 61–84 points for high support. The Cronbach’s α coefficient of the total scale is 0.88, and the test-retest reliability is 0.85.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.5 Resilience Scale\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe scale refers to the\u0026nbsp;ability to recover from negative experiences and adapt flexibly to the ever-changing external environment.\u0026nbsp;Campbell-Sills \u0026amp; Stein (2007) revised this scale into the Brief Resilience Scale, which consists of 10 items. A 4-point rating scale is used for scoring (1 = Completely Disagree; 2 = Somewhat Disagree; 3 = Somewhat Agree; 4 = Completely Agree), and a higher score indicates a higher level of resilience[30].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.6 Simplified Coping Style Questionnaire (SCSQ)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe SCSQ was developed by Lazarus et al. in 1985, and translated into Chinese by Yaning Xie in 1988[31]. It consists of 20 items, divided into two subscales: Positive Coping and Negative Coping. A 4-point Likert scale is used for scoring (0 = Never Adopt, 4 = Often Adopt). A higher score on a subscale indicates a stronger tendency toward the corresponding coping style.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Methods of Data Collection and Quality Control\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOnline questionnaires were designed via the Wenjuanxing platform. With the assistance of the hospital nursing department, questionnaire links were distributed to psychiatric nurses who met the inclusion criteria. The homepage of the questionnaire provided a detailed explanation of the study’s purpose, significance, data usage, and privacy protection measures; participants could proceed to fill out the questionnaire only after providing informed consent. To ensure data quality, logical validations were set up, including mandatory responses for all items and a restriction that only one submission could be made per IP address and device. A total of 320 questionnaires were distributed in this survey, and 318 were returned. Among these, 18 questionnaires with incomplete responses or logical inconsistencies were excluded. Finally, 297 valid questionnaires were obtained, resulting in an effective recovery rate of 92.81%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Statistical Methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, SPSS Statistics 21.0 was used to conduct descriptive statistics and correlation analysis of the research variables. Mplus was employed to perform LPA for exploring the latent categories of empathy fatigue among psychiatric nurses. First, the scores of each dimension of the Empathy Fatigue Scale were standardized, and a model was established. Starting from 1 category, the number of latent categories was increased sequentially, and the optimal latent category model was selected based on both the indicators of the number of categories and practical significance. For the classified data, chi-square tests (χ² tests) and one-way analysis of variance (ANOVA) were used to compare the differences in empathy fatigue scores among nurses with different empathy fatigue statuses in terms of demographic data and work conditions. Additionally, multivariate logistic regression analysis was adopted to identify the influencing factors of empathy fatigue categories in psychiatric nurses.\u003c/p\u003e\n\u003cp\u003e2.5 Ethical Approval\u003c/p\u003e\n\u003cp\u003eThis study has been approved by the Medical Ethics Committee of Xuzhou Oriental People's Hospital, Jiangsu Province, China (Approval No:202501024003). Prior to data collection, all participants signed a written informed consent form. Researchers fully explained the study purpose, procedures, potential risks, and benefits to participants, with particular emphasis on their right to withdraw from the study at any time without conditions or negative consequences. All data processing was conducted in strict compliance with ethical guidelines, and strict confidentiality was maintained throughout the study.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Selection of the Optimal Model and Naming of Categories\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 5 models were fitted in this study, and the model fitting results are shown in \u003cstrong\u003eTable 1\u003c/strong\u003e. The Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and adjusted Bayesian Information Criterion (aBIC) gradually decreased as the number of categories increased, indicating an improvement in model fitting. The entropy values of all models were greater than 0.8, which means the classification accuracy exceeded 90%. When the number of categories was 4, the result of the Lo-Mendell-Rubin adjusted likelihood ratio test (LMR) was 0.6740 (\u0026gt; 0.05), suggesting that the 4-category model was not significantly better than the 3-category model. The population probability distribution of the 3-category model was balanced (18%/71%/11%), and the proportion of each category was greater than 5%, with no tiny latent categories. Based on the comprehensive consideration of the above indicators, the 3-category model was determined as the optimal solution. The empathy fatigue of psychiatric nurses could be divided into 3 latent categories, with their membership probabilities being 18.4%, 71.1%, and 10.5% respectively.\u003c/p\u003e\n\u003cp\u003eBy analyzing the characteristics of the average item scores across the three dimensions (Compassion Satisfaction, Secondary Traumatic Stress, and Burnout), three empathy fatigue profiles were identified (\u003cstrong\u003eFigure 1\u003c/strong\u003e). Profile 1 (Mild Fatigue - Traumatic Stress Group, n = 55, 17.5%): The average item score of the Compassion Satisfaction dimension was \u0026gt; 3.7, the average item score of the Burnout dimension was \u0026lt; 2.7, and the average item score of the Secondary Traumatic Stress dimension was \u0026gt; 1.7. This profile was characterized by Compassion Satisfaction as the dominant factor and a relatively mild degree of empathy fatigue. Profile 2 (Moderate Fatigue - Low Physical Fatigue Group, n = 190, 63.9%): The average item score of the Compassion Satisfaction dimension was \u0026lt; 3.7, the average item score of the Secondary Traumatic Stress dimension was \u0026gt; 1.7, and the average item score of the Burnout dimension was \u0026lt; 2.7. All three dimensions fell at the medium level among the three groups, but the Burnout level was significantly higher within this group. Profile 3 (Severe Fatigue Group, n = 52, 18.5%): The average item score of the Compassion Satisfaction dimension was \u0026lt; 3.7, the average item score of the Secondary Traumatic Stress dimension was \u0026gt; 1.7, and the average item score of the Burnout dimension was \u0026gt; 2.7. This profile exhibited the characteristic of severe empathy fatigue driven by the combined effects of multiple dimensions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eFitting Results of Latent Class Analysis on Compassion Fatigue Among Psychiatric Nurses.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"136%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eBIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eaBIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eEntropy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eLMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eBLRT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 249px;\"\u003e\n \u003cp\u003eCategorical probability\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e1829.498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e1851.660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e1832.632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 249px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e1623.708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e1660.645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e1628.931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 249px;\"\u003e\n \u003cp\u003e0.491/0.508\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e1513.466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e1565.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e1520.779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.0185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.0004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 249px;\"\u003e\n \u003cp\u003e0.185/0.639/0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e1462.974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e1529.461\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e1472.377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.840\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.6740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 249px;\"\u003e\n \u003cp\u003e0.114/0.185/0.327/0.377\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e1399.889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e1481.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e1411.382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.6892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 249px;\"\u003e\n \u003cp\u003e0.023/0.572/0.151/0.252\u003c/p\u003e\n \u003cp\u003e/0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eUnivariate Analysis of Latent Classes of Compassion Fatigue in Psychiatric Nurses [n=297, n(%)].\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"612\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eProject\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eC1(N=55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eC2(N=190)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eC3(N=52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eStatistics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eP-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e50(90.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e169(89.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e49(94.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1.328\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.515\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e5(9.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e21(11.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e3(5.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eUnmarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e8(14.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e41(21.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e8(15.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e5.882\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e45(81.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e148(77.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e42(80.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e2(3.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1(0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e2(3.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eEducational level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eJunior college\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e5(9.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e10(5.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e10(19.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e19.932\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eUndergraduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e46(83.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e179(94.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e40(76.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003ePostgraduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e4(7.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1(0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e2(3.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e33.47\u0026plusmn;6.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e34.85\u0026plusmn;7.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e38.12\u0026plusmn;8.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e5.89\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e22.85(2.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e22.33(2.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e23.13(2.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1.85\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eWork Experience(Y)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e11.87\u0026plusmn;7.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e12.19\u0026plusmn;7.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e15.17\u0026plusmn;9.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e3.09\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eProfessional Title\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eNurse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e3(5.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e24(12.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e6(11.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e11.204\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eSenior Nurse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e10(18.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e53(27.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e7(13.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eCharge Nurse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e27(49.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e81(42.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e26(50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eAssociate Chief Nurse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e14(25.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e28(14.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e11(21.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eChief Nurse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e1(1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e4(2.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e2(3.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eNature of Work\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eDay Shift\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e14(25.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e45(23.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e18(34.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e3.594\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.548\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eNight Shift\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e41(74.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e144(75.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e34(65.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eNight Shifts (Shifts/Week)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e11(20.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e32(16.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e10(19.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e18.908\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e＜2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e12(21.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e37(19.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e8(15.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e23(41.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e97(51.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e15(28.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e＞2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e9(16.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e24(12.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e19(36.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eNurse Stress Scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e83.04\u0026plusmn;11.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e93.21\u0026plusmn;15.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e105.58\u0026plusmn;11.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e34.39\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003ePSSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e72.15\u0026plusmn;10.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e59.71\u0026plusmn;11.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e51.67\u0026plusmn;13.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e43.88\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eNegative Coping\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e10.31\u0026plusmn;4.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e14.77\u0026plusmn;5.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e14.96\u0026plusmn;7.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e12.59\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eActive Coping\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e31(27，36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e27(23，34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e24(19,35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e15.24\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e＜0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eResilience Scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e34(30，40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e29.5(27，30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e29.5(27，30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e37.09\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e＜0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: a: Chi-square value; b: F-value; c: H-value; d: Fisher\u0026apos;s exact test.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePSSS: Perceived Social Support Scale; C1: Mild Fatigue - Traumatic Stress Group; C2:Moderate Fatigue - Low Burnout Group; C3: Severe Fatigue Group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003eAssignment of Independent Variables.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"568\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eIndependent Variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 365px;\"\u003e\n \u003cp\u003eAssignment Setting\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eEducational Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 365px;\"\u003e\n \u003cp\u003eJunior College= 1 \u0026nbsp;Undergraduate= 2 Postgraduate=3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 365px;\"\u003e\n \u003cp\u003eOriginal Value Input\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eWork Experience(Y)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 365px;\"\u003e\n \u003cp\u003eOriginal Value Input\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eNight Shifts (Shifts/Week)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 365px;\"\u003e\n \u003cp\u003e0=0，＜2=1，2= 2，＞2=3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eNurse Stress Scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 365px;\"\u003e\n \u003cp\u003eOriginal Value Input\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003ePSSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 365px;\"\u003e\n \u003cp\u003eOriginal Value Input\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eResilience Scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 365px;\"\u003e\n \u003cp\u003eOriginal Value Input\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eActive Coping\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 365px;\"\u003e\n \u003cp\u003eOriginal Value Input\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eNegative Coping\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 365px;\"\u003e\n \u003cp\u003eOriginal Value Input\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Multivariate Logistic Regression for Latent Classes of Compassion Fatigue Among Psychiatric Nurses.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"776\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 340px;\"\u003e\n \u003cp\u003eModerate Fatigue - Low Burnout Group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" style=\"width: 338px;\"\u003e\n \u003cp\u003eSevere Fatigue Group\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eWald\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eOR Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eWald\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eOR Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e7.454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e3.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e5.343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e1.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e4.267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e2.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e1.068(0.980,1.164)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e4.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.126(1.003,1.263)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003eNight\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eShifts (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e1.324(0.330,5.317)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e-1.851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e3.918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.157(0.025, \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.982)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003eNurse Stress Scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.611\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e1.009(0.976,1.042)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e16.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.107(1.054,1.164)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003ePSSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e10.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.931(0.890,0.973)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e-0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e16.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.894(0.847,0.945)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003eActive\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eCoping\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e11.437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.874(0.809,0.945)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e-0.265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e24.842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.767(0.691,0.852)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003eNegative\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eCoping\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e21.942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e1.277(1.153,1.414)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e17.770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.317(1.159,1.497)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003eResilience\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eScale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e15.465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.810(0.729,0.900)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e-0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e4.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.877(0.773,0.996)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Analysis of Differences in Empathy Fatigue Categories Among Psychiatric Nurses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe difference analysis showed (\u003cstrong\u003eTable 2\u003c/strong\u003e) that there were statistically significant differences in educational background, age, years of work experience, frequency of night shifts, and scores on the Nurse Stress Scale, Perceived Social Support Scale, Resilience Scale, and Simplified Coping Style Questionnaire among the three latent categories (P \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Multivariate Logistic Regression Analysis of Empathy Fatigue Categories Among Psychiatric Nurses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultivariate logistic regression analysis was conducted, with the \u0026quot;Mild Fatigue-Traumatic Stress Subgroup\u0026quot; designated as the reference group. Independent variables were coded according to the specifications outlined in \u003cstrong\u003eTable 3\u003c/strong\u003e. Only variables that demonstrated statistical significance in the preliminary univariate analysis were incorporated into the multivariate logistic regression model. The results indicated that, relative to the mild fatigue subgroup, participants with lower levels of perceived social support, a stronger tendency toward negative coping strategies, and poorer resilience were significantly more likely to be categorized into either the moderate fatigue subgroup or the severe fatigue subgroup. Furthermore, Moreover, unlike the moderate fatigue group, those who are older, work night shifts more frequently, or experience greater nursing stress are also more likely to be classified into the severe fatigue group(\u003cstrong\u003eTable 4\u003c/strong\u003e).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e\u003cstrong\u003e4.1 There is heterogeneity in empathy fatigue among psychiatric nurses.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLatent Profile Analysis (LPA) confirmed significant heterogeneity in CF among psychiatric nurses, which can be categorized into three distinct latent classes: the Mild Fatigue-Traumatic Stress Type (17.5%), Moderate Fatigue-Low Burnout Type (63.9%), and Severe Fatigue Type (18.5%). This finding verifies the existence of individual differences in CF among psychiatric nurses. Detailed explanations of each class are as follows:The core characteristic of the\u0026nbsp;Mild Fatigue-Traumatic Stress Type\u0026nbsp;is dominance of traumatic stress coupled with low treatment involvement in specific work contexts. Consistent with Figley’s compassion fatigue theory[32], prolonged exposure to patients’ traumatic events (e.g., suicide, self-harm)[33]results in significantly higher secondary traumatic stress (STS) scores in this group compared to the other two types. However, their work primarily consists of routine tasks with stable intensity, leading to only moderate elevation in burnout. Additionally, insufficient treatment involvement prevents them from gaining a sense of value from patients’ recovery, ultimately resulting in a severe lack of compassion satisfaction (CS).The\u0026nbsp;Moderate Fatigue-Low Burnout Type\u0026nbsp;(63.9%) is characterized by significantly lower burnout scores than the other groups, with moderate scores on the remaining dimensions. Psychiatric nurses’ long-term exposure to patients’ traumatic experiences induces high traumatic stress[34], and they struggle to obtain adequate CS from their work[35]. Although their work predominantly involves emotional and cognitive engagement (resulting in low physical fatigue), the sustained depletion from high traumatic stress and the lack of protective effects from low CS interact synergistically, ultimately placing them in a state of moderate fatigue.\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;Severe Fatigue Type\u0026nbsp;(18.5%) is the most severe. Its formation is shaped by a vicious cycle of multiple interacting stressors, exacerbated by extremely high workload[36], persistent vigilance-related stress[37], lack of professional value[38], and frequent traumatic exposure[39]. These factors are mutually reinforcing: they weaken stress resistance, amplify negative experiences, and ultimately lead to a state of comprehensive exhaustion—marked by extremely high burnout and STS, alongside extremely low CS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 Analysis of Influencing Factors on Latent Profiles of Compassion Fatigue Among Psychiatric Nurses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2.1 Nurse Occupational Stress\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWork stress level is a significant risk factor influencing the latent class membership of CF among psychiatric nurses. The degree of stress endured by nurses is positively correlated with membership in high-CF classes—specifically, the higher the stress, the greater the likelihood of being classified into the severe fatigue class[40].Occupational stress is defined as a physical and mental stress state experienced by professionals in the workplace, resulting from a mismatch between occupational demands and personal perceptions, as well as discrepancies in work competence. High-responsibility, high-risk, and high-intensity clinical nursing work exposes nurses to substantial occupational stress, whose level far exceeds that of other occupations, with a stress prevalence rate as high as 25%–40%[41]. Chronic occupational stress places nurses in a prolonged state of chronic stress, inflicting severe cumulative harm on their physical and mental health[42]. This can lead to reduced job satisfaction, decreased organizational efficiency, and an elevated risk of nursing errors[43].Nurses’ occupational stress level is positively correlated with turnover rate, accelerating nurse attrition and exacerbating the nursing shortage, which exerts adverse impacts on nursing workforce development. To address this, hospitals may alleviate nurses’ stress by optimizing work schedules, providing psychological support, and offering career development opportunities. Individually, nurses should maintain a work-life balance, master relaxation techniques, expand social support networks, and enhance professional competence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2.2 Social Support\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSocial support systems serve as a protective factor against compassion fatigue (CF) among psychiatric nurses[44]. Their level of social support is positively correlated with membership in low-CF classes[45]—specifically, the more adequate the social support received, the higher the likelihood of being classified into low-fatigue groups.\u003c/p\u003e\n\u003cp\u003eDue to the nature of their work, which requires high-intensity emotional investment and entails significant occupational stress, psychiatric nurses can benefit substantially from enhanced social support (e.g., from hospital management, colleagues, and family). Such support effectively buffers against emotional exhaustion by sharing psychological burdens to reduce the overconsumption of empathic resources, while simultaneously strengthening psychological safety and professional identity to sustain emotional resilience. Collectively, these effects lower the risk of developing high levels of CF.Therefore, constructing a comprehensive social support system encompassing hospital management initiatives, social/familial support, and multi-dimensional network development can enhance the emotional resilience of psychiatric nurses and mitigate the risk of high CF.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2.3 Psychological Resilience\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs an intrinsic psychological resource, psychological resilience exerts a buffering effect on CF among psychiatric nurses. The higher a nurse’s level of psychological resilience, the greater the probability of being classified into low-CF groups.\u003c/p\u003e\n\u003cp\u003ePsychiatric nurses are required to manage patients’ complex emotions and unexpected situations in daily practice, frequently confronting the dual challenges of emotional exhaustion and occupational stress. Nurses with strong psychological resilience possess enhanced emotional regulation capabilities and stress coping efficacy—not only can they rapidly restore psychological balance after high-intensity emotional investment, avoiding the sustained depletion of empathic resources, but they can also flexibly adjust their cognition and coping strategies when facing work setbacks. Instead of falling into a state of negative exhaustion, they transform stressful events into motivation for growth. This intrinsic psychological toughness can directly buffer the onset of CF, enabling them to more easily maintain a low-fatigue state[46].\u003c/p\u003e\n\u003cp\u003eTherefore, hospitals can implement psychological resilience training and regular psychological assessments to help psychiatric nurses strengthen their intrinsic psychological resilience and combat CF.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2.4 Coping Styles\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCoping style adoption is associated with the latent classes of CF among psychiatric nurses[47][48]. Psychiatric nurses who adopt active coping strategies are more likely to be classified into low-CF groups, and active coping can be regarded as a key protective factor.Psychiatric nursing practice is often accompanied by stressful situations such as patients’ emotional crises and disease exacerbations. Active coping styles enable nurses to proactively mitigate the risk of empathic exhaustion: for instance, when facing patients’ aggressive behaviors, active copers resolve conflicts through professional communication skills rather than engaging in emotional confrontation; when encountering work setbacks, they proactively seek colleague supervision or draw on others’ experiences instead of self-denial. Such problem-solving-oriented coping strategies not only reduce unproductive emotional expenditure and avoid the overdraw of empathic resources but also enhance the sense of professional control through positive actions. Thereby, they lower the likelihood of being categorized into high-CF groups and facilitate the maintenance of a low-fatigue state.Therefore, hospitals can offer targeted training and foster a supportive culture, while nurses can proactively learn and practice active coping strategies—collectively reducing the risk of CF among psychiatric nurses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2.5 Age\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsistent with previous research [49], the present study found that older psychiatric nurses were more likely to be classified into the high compassion fatigue group. Age was statistically associated with the latent classes of compassion fatigue among psychiatric nurses. Specifically, with increasing age, physiological functions gradually decline; psychiatric nursing inherently involves sustained exposure to high-intensity scenarios (e.g., patients' sudden emotional outbursts and physical conflicts), and the diminished capacity to recover physical strength and energy may exacerbate emotional exhaustion due to chronic fatigue accumulation [49]. Additionally, older nurses typically have longer service tenure, and prolonged exposure to patients' negative emotions, traumatic events, and occupational stress leads to the depletion of compassion resources following years of continuous overexertion, thereby reducing their resilience in maintaining emotional engagement with patients. Furthermore, older nurses often face multiple role pressures: professionally, they may assume responsibilities such as preceptorship and managing complex cases, while personally, they may shoulder family obligations including supporting elderly relatives and caring for grandchildren. The accumulation of these multiple burdens further depletes their psychological energy. Therefore, hospitals can reduce the risk of older psychiatric nurses being categorized into the high compassion fatigue group by optimizing work schedules, supplementing compassion resources, and mitigating role-related stressors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2.6 Shift Work Pattern\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShift work pattern was associated with the latent classes of compassion fatigue among psychiatric nurses. Specifically, psychiatric nurses who did not work night shifts were significantly more likely to be categorized into the low compassion fatigue group compared with those who worked night shifts, suggesting that night shift work may be one of the risk factors for high compassion fatigue. For psychiatric nurses, night shift work poses multiple physical and psychological challenges: psychiatric patients are at a higher risk of condition deterioration during nighttime, requiring nurses to maintain continuous vigilance to respond to emergent situations. Frequent night shifts may disrupt circadian rhythms, thereby triggering a series of adverse physical and psychological reactions such as fatigue, anxiety, accelerated aging, and insomnia[50]; long-term exposure to such conditions may further translate into job burnout and compassion fatigue[51]. In contrast, nurses who do not work night shifts can maintain regular schedules and adequate rest, reducing emotional vulnerability caused by physical exhaustion. Additionally, they are more likely to receive sufficient team support during daytime work, which helps sustain stable emotional states and compassion resilience. Therefore, optimizing night shift scheduling and strengthening nighttime support can alleviate the physical and psychological depletion of psychiatric nurses caused by night shifts, thereby reducing the risk of high compassion fatigue.\u003c/p\u003e"},{"header":"5. Limitations and conclusions","content":"\u003cp\u003eA core finding of the present study is that compassion fatigue among psychiatric nurses does not present a uniform pattern but rather exhibits significant group heterogeneity. Through latent profile analysis, we identified that multiple factors—including but not limited to age, stress, psychological resources, and coping styles—influence the manifestations of compassion fatigue in psychiatric nurses, and further delineated risk subgroups with distinct characteristics. Based on these findings, at the organizational level, resource support should be provided through precision management (e.g., structural adjustments and institutionalized support); at the individual level, systematic empowerment should be implemented to enhance psychological resilience (i.e., improving psychological flexibility and the ability to master adaptive coping strategies).The conclusions of this study should be interpreted in light of the following limitations: First, the cross-sectional design precludes the inference of definitive causal relationships. Second, the relative concentration of sample sources may limit the generalizability of the findings. Additionally, this study focused primarily on individual-level variables; future research should incorporate more in-depth organizational and environmental factors for comprehensive investigation.For subsequent studies, longitudinal follow-up designs could be adopted to depict the dynamic developmental trajectory of compassion fatigue and clarify its causal mechanisms. Furthermore, based on the risk subgroups and key influencing factors identified in this study, targeted intervention programs could be developed, implemented, and validated. This would provide high-level evidence-based support for establishing a scientific and efficient occupational health protection system for psychiatric nurses.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Development and Molecular Mechanisms of a Cellular Model for Diabetes and Tinnitus Comorbidity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe article/Supplementary Material contains the original contributions made for the study; further questions should be addressed to the relevant authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLD: Conceptualization, Writing - Original Draft, Formal analysis, Investigation, Writing - Review \u0026amp; Editing. JD: Formal analysis, Investigation, Resources, Data Curation, Writing - Review \u0026amp; Editing. HYS: Formal analysis, Investigation, Resources, Data Curation, Writing - Review \u0026amp; Editing. XXW: Formal analysis, Investigation, Data Curation, Writing Review \u0026amp; Editing. XCY：Formal analysis, Investigation, Data Curation, Writing Review \u0026amp; Editing. ALZ: Investigation, Resources, Data Curation, Writing - Review\u0026amp; Editing. TL, Conceptualization, Methodology, Writing - Review \u0026amp; Editing, Supervision, Project administration, Funding acquisition. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was sponsored by the Development and Molecular Mechanisms of a Cellular Model for Diabetes and Tinnitus Comorbidity [No. S202510313079]. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis was a retrospective study approved by the affiliated hospital of Xuzhou Medical University\u0026apos;s ethics committee (No.202501024003). Informed consent was waived.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrespondence\u003c/strong\u003e and requests for materials should be addressed to Ting Li.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization. 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DOI: 10.19582/j.cnki.11-3785/g8.2017.12.011.\u003c/li\u003e\n\u003cli\u003e杨芷．癌症患者照顾者的心理健康调查研究\u0026mdash;\u0026mdash;照顾者反应、应对方式及疲劳感［D］．大连：大连医科大学，2015.\u003c/li\u003e\n\u003cli\u003e曾丽华,陈娴,朱珠,等.江苏省高年资护士共情疲劳的潜在类别分析[J].护理学杂志,2024,39(02):64-68.\u003c/li\u003e\n\u003cli\u003e韩悦,石婷婷.疲劳感和工作疏离感在轮值夜班护士昼夜节律改变与职业倦怠间的中介 作用 [J].工业卫生与职业病 ,2023,49(1):53-57.\u003c/li\u003e\n\u003cli\u003e陈珺仪,冯晓玲,古文珍,等.夜班对护士生理和生活状况的影响分析[J].护理实践与研究,2021,18(23):3487-3491\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":"Nurses, Empathy fatigue, Latent profile analysis","lastPublishedDoi":"10.21203/rs.3.rs-8251260/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8251260/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study seeks to delineate the potential categories of empathy fatigue among nurses and identify their influencing factors through latent profile analysis. The ultimate goal is to establish a foundation for developing targeted intervention strategies, thereby alleviating the occupational psychological burden on nurses and enhancing their mental health.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA convenience sampling approach was employed to recruit 297 clinical psychiatric nurses. Data were collected using the General Information Questionnaire, the Chinese Version of the Empathy Fatigue Scale, the Nurse Stress Scale, the Social Support Scale, and the Simplified Coping Style Questionnaire. Latent profile analysis was conducted using the three dimensions of the Empathy Fatigue Scale as observed variables, and Latent Profile Analysis was utilized to examine the factors influencing empathy fatigue among psychiatric nurses.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePsychiatric nurses can be categorized into three latent profiles concerning empathy fatigue: the mild fatigue-traumatic stress type (18.5%),Moderate Fatigue - Low Burnout Group(63.9%), and the severe fatigue type (17.5%). Multivariate logistic regression analysis identified several risk factors for empathy fatigue, including advanced age, increased frequency of night shifts, elevated scores on the Nurse Stress Scale, and higher scores on negative coping strategies. Conversely, protective factors comprised higher scores on the Social Support Scale, enhanced positive coping strategies, and greater psychological resilience.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003ePsychiatric nurses demonstrate three distinct latent profiles of empathy fatigue, characterized by significant heterogeneity. Nursing managers can develop targeted interventions by considering these influencing factors to mitigate empathy fatigue within the nursing workforce.\u003c/p\u003e","manuscriptTitle":"Study on the Types and Influencing Factors of Compassion Fatigue Among Psychiatric Nurses Based on Latent Profile Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-23 15:25:19","doi":"10.21203/rs.3.rs-8251260/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":"4bad9468-f04f-4f01-89b8-307f0d863b59","owner":[],"postedDate":"December 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":59916231,"name":"Health sciences/Health care"},{"id":59916232,"name":"Health sciences/Health occupations"},{"id":59916233,"name":"Biological sciences/Psychology"},{"id":59916234,"name":"Social science/Psychology"},{"id":59916235,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-01-13T08:39:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-23 15:25:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8251260","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8251260","identity":"rs-8251260","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}