What is behind high turnover intention among hospital nurses during the full liberalization of COVID-19 and post-pandemic era in China: a 2-wave repeated multicenter survey

What is behind high turnover intention among hospital nurses during the full liberalization of COVID-19 and post-pandemic era in China: a 2-wave repeated multicenter survey | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article What is behind high turnover intention among hospital nurses during the full liberalization of COVID-19 and post-pandemic era in China: a 2-wave repeated multicenter survey Julan Xiao, Lili Liu, Yueming Peng, Xia Lv, Chunfeng Xing, Yanling Tao, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5257180/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Feb, 2025 Read the published version in BMC Nursing → Version 1 posted 4 You are reading this latest preprint version Abstract Background The COVID−19 pandemic was a major public health crisis, which has exacerbated the difficulties nurses face, resulting in higher turnover rates and workforce shortages. While many early studies that have examined factors contributing to turnover intention, surprisingly, as yet, no studies have compared the turnover intention of Chinese hospital nurses during the full liberalization of COVID−19 period and post-pandemic era, and it is unclear which potential factors may be associated with turnover intention of nurses at the different periods. This 2-wave repeated survey purposed to explore the prevalence and correlates of turnover intention at different stages of the full liberalization of COVID−19 and post-pandemic era in a large sample of nurses in China. Method Using a repeated cross-sectional survey design, we conducted two online surveys at 25 hospitals in Guandong, China. The 2 surveys were conducted during the full liberalization of COVID−19 period (T1: 27 December 2022 to 7 January 2023, N = 1,766), and post-pandemic era (T2: 11 May to 23 May 2023, N = 2,643). Turnover intention was measured by the six-item Turnover Intention Scale (TIS). A range of turnover intention-related factors was assessed, including sociodemographic characteristics, preceived stress, anxiety, depression, insomnia, job burnout, intolerance of uncertainty, satisfaction with life, and work-related factors. Results The prevalence of turnover intention were 73.33% and 72.34% at T1 and T2, respectively. Dissatisfaction with nursing work ( aOR : 2.160–6.536, Ps < 0.001), lack of interest in nursing ( aOR : 2.513–2.802, Ps < 0.001) and job burnout ( aOR : 1.360–4.096, Ps < 0.01) were associated with an increased risk of turnover intention. And satisfaction with life ( aOR : 0.343–0.683, Ps < 0.05) was associated with a reduced risk of turnover intention. Conclusions Turnover intention were particularly higher both in the full liberalization of COVID−19 period and the post-pandemic era. Multiple factors, especially dissatisfaction with nursing work, lack of interest in nursing, job burnout and satisfaction with life are associated with turnover intention. Early detection of turnover intention among hospital nurses and preventive and promotive interventions should be implemented during the full liberalization of COVID−19 and the post-pandemic era to reduce turnover intention among nurses. Turnover intention Job burnout Risk factors Prevalence Nurses full liberalization of COVID−19 post-pandemic era Figures Figure 1 1 | BACKGROUND The COVID−19 pandemic posed a dramatic effect and significant threat to hospitals nurses of the entire world owing to the urgency and uncertainty of the situation[ 1 ]. As the fight against the epidemic progresses, the National Health Commission of the People’s Republic of China (NHCPR) issued a circular on further optimizing prevention and control measures against COVID−19 and suddenly adopted a policy of full liberalization, called daily nucleic acid testing or temperature screening of patients with COVID−19 infection[ 2 ]. In a very short and fast time, whether they were medical workers or the general population, most people were infected. However, hospital nurses had to cope with the challenges and difficulties such as the huge increase in the number of critically ill COVID−19 patients, and had to work while themselfs were infected. Previous research has reported that nurses were particularly vulnerable to developing physical and psychological issues from excessive and high intensity workloads and long-term usage of personal protective equipment[ 3 , 4 ]. It was proved to associate with increased turnover intention[ 5 ]. Nurses component the largest proportion of hospital health care workers, play a crucial role in the treatment, care, and control of patients’ disease progression during disease outbreaks, disasters, and emergency situations[ 4 , 6 ]. In addition, nurses have the closest proximity to COVID−19 patients worldwide and spent more time caring for patients than other health care professionals[ 2 ]. Nurses were at the forefront of defeating the pandemic and were therefore at risk of developing high levels of stress and physical and psychological depletion[ 2 ]. Moreover, previous evidence has reported that nurses witnessed the high number of death among critically ill COVID−19 patients, relatives and colleagues developing a tremendous psychological burden[ 7 ]. During the COVID−19 pandemic, A meta-analysis showed that the overall prevalence of burnout, depression, anxiety, insomnia and psychological distress among healthcare workers was 37.4%, 31.8%, 34.4%, 27.8% and 46.1%, respectively[ 8 ]. After the sudden full liberalization of COVID−19, nurses experienced acute and exacerbated psychological problem. A multicenter cross-sectional analysis during the full liberalization of COVID−19 period found that frontline nurses experienced turnover intentions (37.66%), depressive symptoms (69.20%), anxiety (62.51%) and insomnia (76.78%) [ 2 ]. More importantly, poor psychological health among nurses is a significant predictor of turnover intention[ 9 ]. With a shortage of nurses, excavating the factors associated with nurses’ turnover intention and reducing nurses’ turnover intention are the essential issues that nursing managers and scholars need to attention. Turnover intention, which refers to employees’ proclivity withdrawal from their current position and consider for new possibilities, is a crucial predictor of turnover, however it does not always result in real turnover[ 10 ]. Nurses tended to have higher turnover intentions compared to other professions, a research illustrated that the proportion of hospital nurses with turnover intention ranged from 20.2 to 56.1%[ 11 ], stating that nurses cohort is erratic and constantly in a state of change. At the moment, sufficient evidence illustrated that turnover intention is the direct premise of turnover behavior[ 12 ], which is a strong predictor of actual turnover[ 13 ]. Turnover intention may greatly hinder the development of nursing career [ 12 ]. Frequent nurse turnover has many negative impact on nursing work quality, patients outcomes, medical organization stability and so on, it may lead to emotional instability and slack behavior of other nurses in the organization, and aggravates the hospital’s potentially high expenditure in new staff recruitment, hiring and training[ 14 ], especially in remote and poverty-stricken rural areas of China, where experienced a shortage of hospital nursing workers[ 15 ]. The loss of experienced nurses, particularly specialty ones, may adversely have an adverse impact on the provision and continuity of nursing services, which may result in higher incidence rates of nursing adverse events and patient mortality[ 16 ]. The State of the World Nursing 2020 declared that the shortage of hospital nurses will reach 5.7 million by 2030 in global, which means that the high turnover rate of nurses will undoubtedly be a great challenge for the chinese healthcare system[ 17 ]. In consequence, a greater insight into turnover intention contribute to decision makers taking preventive measures to reduce eliminate the nurse intention to leave before actual turnover occurs and saving cost for the organization[ 13 ]. China has gone through the last two stages in the process of fighting the COVID−19 pandemic, including a full liberalization of COVID−19 (the National Health Commission of the People’s Republic of China issued a circular on further optimizing prevention and control measures against COVID−19 and suddenly adopted a policy of full liberalization) and post-pandemic era (pandemic prevention and controlwork shifted from a full liberalization state to a normal state). However, to the best of our knowledge, most previous studies have had cross-sectional designs. Little is known about turnover intention and changing trends among hospital nurses since the full liberalization of the pandemic, which may hinder nursing managers and policymakers from formulating preventive and promotive interventions to prevent the adverse effects of turnover intention. During the COVID−19 pandemic, the prevalence of turnover intention among hospital nurses and nursing assistants was already high[ 18 ]. However, there is scarce evidence about the topic during the full liberalization of COVID−19 and post-pandemic era. And a comparative study on the turnover intention of Chinese hospital nurses during the full liberalization of COVID−19 period and post-pandemic era has not been reported. In addition, it is unclear which potential factors may be associated with turnover intention of nurses in the different periods, and this lack of research may hinder timely and effectively interventions of turnover intention. To fill this grap, the current study was conducted a large-sample repeated cross-sectional study of Chinese hospital nurses during the full liberalization of COVID−19 period (T1: 27 December 2022 to 7 January 2023), and post-pandemic era (T2: 11 May to 23 May 2023), aiming to determine the prevalence of turnover intention and identify its significantly associated factors among nurses in different periods of COVID−19 and post-pandemic era. Consequently, our study will make a positive contribution to the literature on nursing mangers and policymakers pay close attention to turnover intention, investigate antecedents, and implement targeted strategies to reduce nurses’ turnover intention. 2 | METHODS 2.1 | Study setting and sample This large-scale repeated cross-sectional survey was carried out in Shenzhen, Guangdong Province, which is one of the highest-income cities in China. Convenience sampling was used for the recruitment strategy, in total, 25 hospitals’ nurses were selected to participate in the current survey, which was conducted from 27 December 2022 to 7 January 2023 (T1) and 11 May to 23 May 2023 (T2). The online questionnaire was shared via “Wenjuan Xing”, a professional questionnaire survey platform that is widely used in China[ 19 ]. We provided a normative notice applicable to these 25 hospitals, exploring the questionnaire survey’s purpose, significance, mode of participation, completion method of this research and deadline. The online survey was first disseminated through an instant messaging system, the WeChat group, to nursing managers at Shenzhen hospitals, who were encouraged to pass it on to other nurses at Shenzhen hospitals. The inclusion criteria for participants were as follows: (1) nurses aged 18 and above; (2) normal language expression and understanding, and the ability to understand the investigation content and cooperate with the research; and (3) the ability to give voluntary informed consent. The exclusion criteria were (1) history of mental illness and (2) serious physical diseases. Screening criteria included a response time of fewer than 200s[ 20 ] to complete the survey and the deletion of incomplete or repeated answers. The effective sample sizes of T1 and T2 were 1,766 and 2,643, respectively. And the effective rates were 96.34% and 94.87%, respectively. 2.2 | Data collection All investigators received unified training on the online survey. Each question was a mandatory item and could only be answered once by the same IP address. In addition, all of the participants were allowed to stopped at any time, and anonymity would be guaranteed. The online questionnaire began with informed consent. Hospital nurses needed to read the informed consent and choose the “agree” option to begin filling out the questionnaire; otherwise, the questionnaire could not be completed. To ensure the feasibility and suitability of the questionnaire, online pilot survey including 45 nurses from Shenzhen was accomplished. Then the nurses who participated were also asked for suggestion on questionnaire modification. Pilot test data were not used for the final statistics. The trained authors of this article distributed the final version of the questionnaires to the hospital nurses for data collection. 2.3 | Measures 2.3.1 | Sociodemographic and work-related characteristics A general information questionnaire (created by the authors from literature reviews and group discussions) included two parts. The first part collected data on nurses’ characteristics included gender, age, educational level, working years, marital status, monthly income per year, employment type, professional technical titles, hierarchy of nurse, job duty, night shifts per month, hospital type, and working unit. The second part included job satisfaction, interest in nursing, and compared with the outbreak period, the attitude toward the current epidemic. 2.3.2 | Perceived Stress The 10-item Perceived Stress Scale (PSS−10) is based on the theory of psychological stress and was developed in 1983 by Cohen et al[ 21 ]. The PSS−10 is used to measure how stressful an individual perceives events in daily life. The PSS−10 is a 10-item self-report scale, and the total score on the PSS−10 ranges from 0–40. Higher scores indicate that the individual’s perceived stress level is high. In the current study, the Cronbach’s alpha for the PSS−10 was 0.72 at T1, and 0.82 at T2. 2.3.3 | Depressive symptoms The 9-item Patient Health Questionnaire (PHQ−9) is a self-report scale assessing patients’ and general populations’ depressive symptoms over the past 2 weeks[ 22 ]. The questionnaire is based on the nine symptoms of depression in the US Diagnostic Standard for Mental Illness (DSM-IV)[ 23 ]. The PHQ−9 is rated on a 4-point Likert scale, and the total score ranges from 0 to 27, with higher scores indicating more obvious the state of depression. In our study, the Cronbach’s alpha for the PHQ−9 both were 0.93 at T1 and T2. 2.3.4 | Anxiety We used the 7-item Generalized Anxiety Disorder (GAD−7) to evaluate the severity of anxiety, which is a self-report scale with good reliability and validity[ 24 ]. The participants were asked to report their symptoms of anxiety over the previous 2 weeks on a 4-point Likert scale from 0 (never) to 3 (almost every day), for a total score ranging from 0 to 21. Higher scores indicated higher degree of anxiety symptoms. Cronbach’s alpha in the present study was 0.97 at T1, and 0.96 at T2. 2.3.5 | Insomnia The 7-item Insomnia Severity Index (ISI) was used to evaluate the severity of insomnia in the past week. The ISI consists of 7 items, each of which is rated on a 5-point scale, for a total score ranging from 0 to 28. Higher scores on the ISI indicates greater insomnia severity during the previous month. Cronbach’s alpha of the scale both were 0.93 at T1 and T2 in this study. 2.3.6 | Intolerance Of Uncertainty The Intolerance of Uncertainty Scale (IUS) is a selfassessment instrument to measure intolerance of uncertainty through 12 items with a 5-point Likert scale[ 25 ]. The IUS calculates a total score, with a higher score illustrate a higher intolerance of uncertainty, resulting in difficulty in tolerating the negative emotion, triggered by a lack of information regarding a specific state[ 26 ]. Cronbach’s alpha of IUS in the present study was 0.91 at T1, and 0.92 at T2. 2.3.7 | Job Burnout The Maslach Burnout Inventory-General Burnout Survey (MBI-GS) was used to measure burnout in HCWs[ 27 ]. This tool encompasses emotional exhaustion, depersonalization, and reduced personal accomplishment. The MBI-GS comprises 15 items scored on a 7-point Likert scale ranging from 0 (never) to 6 (everyday). High scores for this questionnaire indicate the incidence of burnout[ 28 ]. Cronbach’s alpha for the MBI-GS in the current study was 0.87 at T1, and 0.92 at T2. 2.3.8 | Satisfaction with Life The Satisfaction with Life Scale (SWLS) was conducted to measure an individual’s global judgment regarding their life satisfaction. The SWLS contains 5 items in which 3 items reflect present life satisfaction whereas the other 2 items embody past life satisfaction. The scale is rated on a 7-point Likert scale from 1 (terrible dissatisfaction) to 7 (high satisfaction)[ 29 ]. Higher scores indicated higher degree of life satisfaction. Cronbach’s alpha for the SWLS in the current study was 0.93 at T1, and 0.94 at T2. 2.3.9 | Turnover Intention The turnover intention of hospital nurses were evaluated by the Turnover Intention Scale, which included the possibility of leaving the current job, the motivation to find a new job, and the possibility of acquiring another job[ 30 ]. It consists of six items with a 4-point response scale, and with a total score of four points for each item and higher scores suggesting a stronger turnover intention of nurses. Cronbach’s alpha of the scale both were 0.81 at T1 and 0.80 at T2 in this study. 2.4 | Data analysis Data were analyzed with SPSS version 26.0. Descriptive statistics, including frequencies and central tendencies, were calculated to characterize the sample’s demographic profile. The chi-square test was performed to test the diference between the prevalence of turnover intention from T1 to T2. Binary and multiple logistic regression analyses were conducted to determine the potential factors of turnover intention among hospital nurses during the sudden full liberalization of COVID−19 peroid and post-pandemic era. The four mental health symptoms (perceived stress, depression, anxiety, and insomnia) and other independent variables (job burnout, intolerance of uncertainty, satisfaction with life) were divided into four groups using quartiles, indicating the position of the score in the sample. The first quartile was 0–25%, the second was 25%−50%, the third was 50%−75%, and the fourth was 75%−100%. Odds ratios (ORs) and 95% confidence intervals (95% CIs) were obtained from the logistic regression models. P <0.05 was considered statistically significant. 3 | RESULTS 3.1 | Demographic characteristics The descriptive characteristics of the hospital nurses are presented in Table 1 . In the two-wave survey, most of the participants 95.92% were female in T1, and 95.27% in T2. The majority (>75%) were younger than 40 years of age. Contract staffs accounted for a large proportion (> 80%). The number of clinical nurses was approximately four times that of administrative nurse and others, with T1 clinical nurses accounting for 80.92%, and T2 accounting for 77.75%. Detailed participants demographics and mental health at the two time points are reported in Table 1 . 3.2 | Prevalence of turnover intention at T1-T2 As shown in Fig. 1 , the prevalence of turnover intention at T1 and T2 was 73.33% (95% CI: [71.18, 75.37]), and 72.34% (95% CI: [70.64, 74.08]), respectively. The χ² test between the two groups wasn’t signifcant, χ²=7.522, P = 0.057. 3.3 | Correlation between anxiety, depression, perceived stress, insomnia, intolerance of uncertainty, job burnout, satisfaction with life and turnover intention The average turnover intention scores among all the nurses at T1 and T2 were found to be 2.49 (0.63) and 2.47 (0.62) points (Table 2 ), respectively. Higher scores of turnover intention were positively correlated with anxiety, depression, perceived stress, insomia, intolerance of uncertainty, and job burnout ( all P <0.01)in T1 and T2. Significant negative relationships were also found between turnover intention and satisfaction with life both (all P <0.01) in T1 and T2. 3.4 | Logistic regression analysis of factors associated with turnover intention The binary and multiple logistic regression analysis results of the factors for turnover intention are presented in Table 3 . Binary and multiple logistic regression analysis were conducted to reckon the independent odds ratio (Crude OR, cOR) and adjusted odds ratio (Adj OR, aOR) of the risk factors for turnover intention, which were used to refect the OR of each factor individually and jointly when predicting turnover intention[ 31 ]. Most influencing factors could independently predict turnover intention, but there were differences when they were jointly predicted. Ages; years of nursing work experience; 15000 or higher income per year; professional titles; hierarchy of nurse; marital status; night shift per month; working unit; nurses’ attitude towards the current pandemic management; perceived stress; anxiety; insomnia and intolerance of uncertainty could independently predict turnover intention, but the aOR were not signifcant from T1 to T2 when predicting turnover intention jointly ( Ps > 0.05). As we can see, nurses who were average or not satisfied with job; lack of interest in nursing; higher job burnout at T1 and T2 were risk factor for turnover intention when predicting turnover intention jointly ( Ps <0.05). Moreover, higher satisfaction with life at T1 and T2 were protective factor for turnover intention when predicting jointly ( Ps <0.05). Employment type for contract staff was a risk factor for turnover intention when it was predicted separately [T1: cOR = 4.377, 95% CI: (3.394, 5.644), P < 0.001; T2: cOR = 4.245, 95% CI: (3.399, 5.301), P < 0.001]. However, when jointly predicted, this variable remained a risk factor at T2 [aOR = 2.830, 95% CI: (1.952, 4.101), P 0.05). Job duty as administrative nurse and others was a protective factor for turnover intention when it was predicted separately [T1: cOR = 0.460, 95% CI: (0.358, 0.591), P < 0.001; T2: cOR = 0.582, 95% CI: (0.358, 0.591), P < 0.001]. Interestingly, when jointly predicted, this variable became a risk factor at T2 [aOR = 1.861, 95% CI: (1.358, 2.550), P 0.05). Depression was negatively correlated with turnover intention when predicted individually ( Ps < 0.001), while depression remained a risk factor at T2 [aOR = 2.830, 95% CI: (1.952, 4.101), P 0.05). 4 | DISCUSSION In one of the first large-scale, multicenter, 2-wave cross-sectional survey of turnover intention among Chinese hospital nurses during the full liberalization of COVID−19 and post-pandemic era, our findings reveal an insignificant decreased trend in the prevalence of overall turnover intention among nursing staff during the full liberalization of COVID−19 and post-pandemic era periods. The results of this findings indicated that the prevalence of turnover intention were particularly higher, no matter in the full liberalization of COVID−19 period or post-pandemic era. The scores were also higher compared with another research which conducted among hospital nurses at the beginning of the outbreak of the COVID−19[ 32 , 33 ]. The results illustrated that while the post-pandemic era has returned to normal conditions, the long-term negative impact of the pandemic should not be overlooked. Although the post-pandemic era has returned to normal, the prevalence of turnover intention is still higher, which suggests that we need pay attention to turnover intention among nursing staff, continue to monitor and track turnover intention and identify appropriate response and intervention strategies, which were consistent with previous researches. The result demonstrated that mental health problems, such as anxiety, depression, insomnia were positively associated with turnover intention in nursing staff. Several studies have confirmed that the significant and positive associations between depression, anxiety, insomnia and turnover intention[ 34 – 36 ]. And anxiety and depression have been repeatedly identified as predictors of turnover intention in previous literature[ 37 ]. Notably, after adjusted other variables, anxiety and insomnia were not predictors of turnover intention among nurses, and depression symptom was not significantly associated with turnover intention at beginning of the full liberalization of COVID−19 (T1), but turn into significant at post-pandemic era periods (T2). However, Tabur et al. showed that anxiety and depression were not predictors of turnover intention among healthcare professionals[ 38 ]. The result appeared consistent with our study, which can largely be attributed to the discrepancies in the statistical analysis method[ 34 ]. Due to the significant correlation between anxiety and depression scores, statistically significant results could change when anxiety and depression were entered into the same model simultaneously[ 39 ]. On the other hand, the above result may be related to a number of the variables included in the prediction model[ 31 ]. Because there were many variables included in the equation for joint prediction, the effect of depression symptoms was weakened or interacted with other factors[ 31 ]. Similar to the results of previous studies, psychological factors such as job burnout, satisfaction with life were important factors for turnover intention. Our fingdings revealed that nursing staff with higher level job burnout experienced more turnover intention. According to previous studies focusing on the relationship between job burnout and turnover intention, greater job burnout were associated with higher turnover intention[ 40 ]. This may reasonablely explain why the rate of turnover intention were particularly higher in nursing staff at different stages of the full liberalization of COVID−19 and post-pandemic era. Moreover, satisfaction with life can be measured by general wellbeing. In current study, satisfaction with life were negatively associated with turnover intention among nursing staff, and the result was similar to previous studies[ 41 , 42 ], illustrating that improving nurses’ wellbeing can decrease their turnover intentions and turnover rates. According to literature, a favorable sense of wellbeing can great enhance nurses’ psychological resilience and mental health, and it was associated with lower absence and turnover intention[ 43 ]. Therefore, preventive and promotive interventions and positive psychology interventions should be implemented as far as possible to reduce nurses experiencing turnover intention. In this study, nurse’s turnover intention was significantly influenced by their satisfaction with their job, nurses who were dissatisfaction with their nursing work more likely to leave their workplace, which was consistent with the result of previous literature[ 44 ]. A cross-sectional study in Ethiopia showed that among hospital nurses, who were unsatisfied on their job autonomy were 2.55 more likely to intend to quit their jobs than nurses who reported to be satisfied[ 44 ]. Turnover theory attach importance to turnover intention begins with dissatisfaction among workers, and our study supports the theory[ 45 ]. In order to alleviate nurses’ turnover intention, effective intervention measures should be implemented in time to improve their job satisfaction. Another significant predictor of turnover intention in the hospital was interest in nursing work among nurses, that is, nurses who lack of interest in their job, they usually lack clear career goals and planning; to be more serious, they even wonder whether they were competent for the job. Eventually, such nursing staff may experience higher turnover intention. Some prior studies presented that professional nurses in emergency or critical care medicine were tend to higher overwork and stress than those of other departments, which could lead to stronger turnover intention[ 46 ]. However, no such associations were revealed in current study. On account of nurses in the 2-wave repeated multicenter survey were mostly medical, surgical, and outpatient nurses, and low number of nurses in the emergency department and ICU, the results in our study were not similar to those in previous studies. Therefore, working department may not be factors that influence turnover intention among chinese nursing staffs, and further studies is needed to shed light on this issue. Furthermore, our fingdings revealed that nursing staff employed as contract staff compared with permanent staff, administrative nurses and others with clinical nurses, have higher rate of turnover intention at T2. The main reason may be that the proportion of contract nurses (81.99%) at TI and (85.19%) at T2 in present study were higher than that reported by Cao[ 47 ] (65.72%) and Chen[ 48 ] (69.46%), which may lead to a higher turnover intention. Due to the 2-wave repeated multicenter survey were conducted at different special times, the above result were not significant in T1, these also confirmed that the factors related to high turnover intention at different periods were not consistent. To be exact, the chinese nursing employment system has two parallel types of employment for nursing staff: permanent nurses and contract nurses. Although two parallel types of nurses undertake the same work, contract nurses receive lower salaries and fewer benefits than permanent nurses; therefore, contract nurses are more likely to intend to leave their positions[ 49 ]. For the sake of reducing nurse turnover, chinese government priorities should take reasonable policies which was to developed to eliminate the disparities between permanent nurses and contract nurses, improve development opportunities for hospital nursing staffs[ 50 ]. Our study found that administrative nurses had a increased risk for turnover intention than clinical nurses. Even though the professional careers of these administrative nurses were relatively successful, and they were having more work experience, better position and higher salary, the turnover intentions do not show a decreasing tendency. It is possible that they were undertaking more nursing management and quality and safety work. And moreover, the appointment of administrative nurses relatively conservative in China’s hospitals, as most administrative nurses were elected from the nurses with excellent clinical skills and rich nursing knowledge[ 51 ] irrespective of management abilities and without further administration training. Thus, administrative nurses have very few standards upon which could guide their work[ 52 ] and may feel they are not qualified enough for their position, especially in the special period of public emergency. The fingdings of this study may be helpful for guiding hospital administrators to systematically understand nurses’ responses to turnover intention during the pre- and post-pandemic era and further provide preventive and promotive interventions to reduce turnover intention and improve mental health in this special population. This study clarified factors associated with intention to leave the nursing profession during the full liberalization of COVID−19 period and post-pandemic era. There are several limitations in this current study, which involved in the sample size and sampling method. First, The convenience sampling method may not be representative or generalizable to the hospital nurses in China. The impact of the COVID−19 epidemic on nursing staff’s turnover intention may have been over or underestimated. We cannot know the prevalence of turnover intention of hospital nurses who did not participate in our research. Second, considering the limitations of turnover intention, job burnout, intolerance of uncertainty, satisfaction with life and mental health using correlation scale, this 2-wave repeated cross-sectional design limited the explanation of causality. Future longitudinal research is needed to accurate track the changes in turnover intention and mental health among nursing staff. 5 | CONCLUSION In conclusion, the current study first described the prevalence of turnover intention among Chinese hospital nurses during the two key windows of the full liberalization of COVID−19 and post-pandemic era. Although the post-pandemic era has returned to normal, the COVID−19 pandemic has had a long-term and far-reaching negative impact on nursing work, resulting in higher turnover intention and workforce shortages. In the meanwhile, we found that turnover intention among hospital nurses were related to multiple factors included dissatisfaction with nursing work, lack of interest in nursing, job burnout, satisfaction with life, depressive symptom, employment type and job duty. The findings contribute to a better understanding for hospital management of the prevalence of turnover intention among nursing staff since the full liberalization of COVID−19 and provide guidance and targeted practical recommendations of policy-making within nursing human resources departments. Longitudinal studies, qualitative studies even mixed studies are urgent needed to provide further evidence regarding the development of nursing career and mental health among nurses. The Chinese adaptations of the standardized questionnaires used in current study are reliable and valid and can be used to help healthcare managers identify areas of concern within their institution and take effective interventions to prevent nurses from resignation and promote their career success. 6 | IMPLICATIONS FOR NURSING MANAGEMENT Our study suggest continuous detection of turnover intention among hospital nurses, implementing nursing job satisfaction-promoting and nurse-caring measures, cultivating nurses’ interest in nursing work and providing targeted psychological intervention services. Hospital managers should make full use of job duty advantages when allocating work according to administrative nurses’ capabilities to improve their management skills, and guide clinical nurses to devote themselves to nursing work with enthusiasm and initiative. According to the needs of nurses with different degrees, it is essential to provide different opportunities and support to help nurses pursue further education and career development, allocate human resources reasonably to remain active in completing their tasks and reduce job burnout and turnover rates. Meanwhile, selection and training of specialized nurses is also a very important career development direction. The development of specialized nursing enables specialized nurses to strengthen their work in the field, they are good at or like, and gain a sense of professional value and accomplishment. At the same time, we recommend various forms of stress reduction activities for nurses, such as psychological counseling, mindfulness-based stress reduction sandplay and so on, to improve their general wellbeing and emotional intelligence. We appeal for an increase in night shift fees and salaries for nurses, expanding reward and rest time for night nurses to ensure that nurses get enough rest after night shift and improve their professional happiness. In most developed countries, the income level of nurses is often higher than the average social income. Under the premise of ensuring the quality of nursing and patient safety, we promote the appointment and scheduling of nurses, and the head nurse tries to meet the individual needs of nurses under the premise of ensuring normal work. Only through these approaches will we be able to reduce job burnout and turnover intention of nurses, enhance career happiness and optimize the structure of nursing teams in China and, possibly, other countries as well. Declarations HUMAN ETHICS AND CONSENT TO PARTICIPATE DECLARATIONS This study was in line with ethical principles, and the contents of the questionnaire didn’t involve private and sensitive topics such as names. More importantly, the study protocol was approved by the Institutional Ethics Board of Shenzhen People’s Hospital (Approval No. LL-KY-2023107-01). All individuals were given information about introducing the study and notified about their own right to withdraw at any time, and informed consent was sought from all eligible participants, which illustrated that they had understood the study in its entirety. DATA AVAILABILITY STATEMENT The datasets that support the findings of the current study are available from the corresponding author on reasonable request. Authors’ contributions JLX and WXL designed and managed the study, the methodology was developed by LLL and YMP. XL, CFX, YLT, SNZ, AHM, LJL, HYH, YF, WSP, HSX and JR were responsible for the questionnaire survey of frontline nurses in Shenzhen and data acquisition. JLX and LLL analyzed the statistics. JLX , WXL and LLL wrote the first draft of the manuscript, all authors revised the manuscript. All authors listed meet the authorship criteria according to the latest guidelines of the International Committee of Medical Journal Editors, and that all authors read and approved the final manuscript. CONFLICT OF INTEREST The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. AFUNDING STATEMENT This study was supported by the Shenzhen Peoples’ Hospital and Young and Middle-aged Research Fund Project (grant number SYHL2021-N0006), Shenzhen Health Economics Society Research Fund Project (grant number 202336) and Nursing discipline research topic of Chinese Medical Association journal (grant number CMAPH-NRG2022042). ACKNOWLEDGEMENTS We are grateful to all the nurses who took their time to participate in the study, especially those participants who provided our research with essential information about their feelings, supporting us to complete the survey. 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Tables Table 1 Descriptive characteristics of the sampled hospital nurses Variables T1 N (%) T2 N (%) Gender Male 72 (4.08%) 125 (4.73%) Female 1694 (95.92%) 2518 (95.27%) Age, years 18-29 763 (43.20%) 1072 (40.56%) 30-39 579 (32.79%) 1022 (38.67%) ≥40 424 (24.01%) 549 (20.77%) Educational level Technical secondary school 25 (1.42%) 68 (2.57%） Junior college 451 (25.54%) 666 (25.20%) Bachelor 1270 (71.91%) 1895 (71.70%) Master or higher 20 (1.13%) 14 (0.53%) Years of nursing work experience Less than 1 year 75 (4.25%) 113 (4.28%) 1-5 469 (26.56%) 602 (22.78%) 6-10 421 (23.84%) 649 (2.56%) 11-20 453 (25.65%) 850 (32.16%) 21 or above 348 (19.71%) 429 (16.23%) Monthly income level (CHY)* ≤4999 105 (5.95%) 234 (8.85%) 5000-9999 686 (38.84%) 1205 (45.59%) 10000-14999 686 (38.84%) 827 (31.29%) 15000 or higher 289 (16.36%) 377 (14.26%) Employment type Permanent staff 318 (18.01%) 392 (14.83%) Contract staff 1448 (81.99%) 2251 (85.19%) Professional technical titles Primary technical title 1092 (61.83%) 1618 (61.22%) Intermediate technical title 564 (31.94%) 873 (33.03%) Senior technical title 110 (6.23%) 152 (5.75%) Hierarchy of nurse N0* 183 (10.36%) 270 (10.22%) N1-N2* 988 (55.95%) 1365 (51.65%) N3-N4* 542 (30.69%) 911 (34.47%) N4-N6* 53 (3.00%) 97 (3.67%) Job duty Clinical nurse 1429 (80.92%) 2055 (77.75%) Administrative nurse and others 337 (19.08%) 588 (22.25%) Marital status Married 1000 (56.63%) 1604 (60.69%) Single 726 (41.11%) 965 (36.51%) Divorced or widowed 40 (2.27%) 74 (2.80%) Night shift per month None 542 (30.69%) 789 (29.85%) 1-5 514 (29.11%) 942 (35.64%) 6-10 470 (26.61%) 670 (25.35%) 11 or more 240 (13.59%) 242 (9.16%) Hospital type General hospital 1476 (83.58%) 2204 (83.39%) Specialist hospital 290 (16.42%) 439 (16.61%) Working unit Surgery (including gynecology) 578 (32.73%) 761 (28.79%) Medicine (including pediatrics) 420 (23.78%) 573 (21.68%) Emergency 76 (4.30%) 154 (5.83%) Intensive care unit 59 (3.34%) 100 (3.78%) Out-patient 241 (13.65%) 315 (11.92%) Others 392 (22.20%) 740 (28.00%) Job satisfaction Satisfied 856 (48.47%) 1640 (62.05%) Neutral satisfied 618 (34.99%) 821 (31.06%) Not satisfied 292 (16.53%) 182 (6.89%) L ack of interest in nursing Yes 665 (37.66%) 1141 (43.17%) No 1101 (62.34%) 1502 (56.83%) Your attitude towards the current pandemic management Very panic, anxiety 357 (20.22%) 77 (2.91%) Mildly nervous, worried 500 (28.31%) 340 (12.86%) Normal mindset 854 (48.36%) 1968 (74.46%) Feel nothing 55 (3.11%) 258 (9.76%) GAD-7 Q1 662 (37.49%) 1300 (49.19%) Q2 637 (36.07%) 904 (34.20%) Q3 143 (8.10%) 318 (12.03%) Q4 324 (18.35%） 121 (4.58%) PHQ-9 Q1 544 (30.80%) 546 (20.66%) Q2 538 (30.46%) 833 (31.52%) Q3 352 (19.93%) 706 (26.71%) Q4 332 (18.80%) 558 (21.11%) ISI Q1 410 (23.22%) 658 (24.90%) Q2 745 (42.19%) 704 (26.64%) Q3 443 (25.08%) 657 (24.86%) Q4 168 (9.51%) 624 (23.61%) PSS-10 Q1 439 (24.85%) 645 (24.40%) Q2 644 (36.47%) 754 (28.53%) Q3 348 (19.71%) 598 (22.63%) Q4 335 (18.97%） 646 (24.44%) MBI-GS Q1 440 (24.92%) 660 (24.97%) Q2 445 (25.20%) 680 (25.73%) Q3 445 (25.20%) 643 (24.33%) Q4 436 (24.69%) 660 (24.97%) IUS-12 Q1 435 (24.63%) 610 (23.08%) Q2 489 (27.69%) 767 (29.02%) Q3 423 (23.95%) 639 (24.18%) Q4 419 (23.73%) 627 (23.72%) SWLS Q1 419 (23.73%) 577 (21.83%) Q2 655 (37.09%) 894 (33.83%) Q3 269 (15.23%) 661 (25.01%) Q4 423 (23.95%) 511 (19.33%) Note: CHY1 = USD 0.14, based on the exchange rate on 24 September 2023. N0, registered nurse who were unable to care for patients independently; N1, registered nurse and who were able to care for patients independently; N2, nurses who have been qualified for 3 years as N1 nurses; N3, nurses who have obtained intermediate titles and are able to undertake various clinical/teaching tasks and a certain managerial duty; N4, nurses who have acquired deputy senior title or above and are able to undertake the work of quality management, teaching management; N5, nurses who have acquired senior title and are able to complete the work of guidance and review; N6, on the basis of N5 nurses, working hours were longer than N5 head nurses. GAD-7 represents the total score of the Generalized Anxiety Disorder Scale, PHQ-9 represents the total score of the 9-item Patient Health Questionnaire depressive symptoms, ISI represents the total score of the Insomnia Severity Index, PSS represents the total score of the 10-item Perceived Stress Scales, MBI-GS represents the total score of the Maslach Burnout Inventory-General Survey, IUS represents the total score of the Intolerance of Uncertainty Scale, and SWLS represents the total score of the Satisfaction with Life Scale. Q1 represents the first quartile, Q2 represents the second quartile, Q3 represents the third quartile, and Q4 represents the fourth quartile. Table2 correlation matrix between anxiety, depression, perceived stress, insomnia, intolerance of uncertainty, job burnout, satisfaction with life and turnover intention (T1 and T2) T1 M SD 1 2 3 4 5 6 7 8 1. Anxiety 7.22 5.63 1.00 2. Depression 9.04 6.06 0.78** 1.00 3. Perceived Stress 19.53 5.22 0.51** 0.51** 1.00 4.Insomnia 12.42 6.45 0.62** 0.69** 0.41** 1.00 5.Intolerance of Uncertainty 37.36 9.60 0.60** 0.59** 0.47** 0.47** 1.00 6.Job Burnout 2.62 1.21 0.68** 0.68** 0.43** 0.56** 0.55** 1.00 7.Satisfaction with Life 20.26 6.11 -0.36** -0.38** -0.20** -0.32** -0.29** -0.55** 1.00 8.Turnover Intention 2.49 0.63 0.30** 0.30** 0.20** 0.28** 0.25** 0.52** -0.46** 1.00 T2 M SD 1 2 3 4 5 6 7 8 1. Anxiety 5.54 5.01 1.00 2. Depression 6.88 5.73 0.80** 1.00 3. Perceived Stress 17.71 6.21 0.66** 0.66** 1.00 4.Insomnia 10.18 6.44 0.60** 0.72** 0.55** 1.00 5.Intolerance of Uncertainty 35.65 9.79 0.58** 0.56** 0.52** 0.44** 1.00 6.Job Burnout 2.23 1.14 0.68** 0.71** 0.69** 0.59** 0.49** 1.00 7.Satisfaction with Life 20.82 6.39 -0.38** -0.42** -0.48** -0.37** -0.31** -0.50** 1.00 8.Turnover Intention 2.47 0.62 0.29** 0.32** 0.32** 0.30** 0.19** 0.48** -0.37** 1.00 Table3 Binary and multiple logistic regression analysis of the factors associated with turnover intention at T1 and T2 Variables Category T1 T2 Crude OR 95%CI Adj OR 95%CI Crude OR 95%CI Adj OR 95%CI Gender Male Female 0.285 ** (0.130, 0.627) 0.516 (0.208, 1.279) 0.642 (0.410, 1.003) 0.749 (0.438, 1.280) Age, years 18-29 30-39 0.607 *** (0.461, 0.801) 1.317 (0.700, 2.475) 0.768 * (0.624, 0.946) 1.222 (0.805, 1.854) >40 0.166 *** (0.126, 0.219) 0.930 (0.391, 2.210) 0.238 *** (0.190, 0.298) 0.864 (0.472, 1.579) Educational level Technical secondary school Junior college 1.971 (0.861, 4.510) 0.818 (0.278, 2.403) 2.007 * (1.192, 3.378) 1.348 (0.719, 2.528) Bachelor 1.816 (0.808, 4.082) 0.916 (0.319, 2.629) 1.549 (0.941, 2.552) 1.195 (0.644, 2.218) Master or higher 1.556 (0.447, 5.413) 0.961 (0.181, 5.102) 0.619 (0.195, 1.967) 0.680 (0.153, 3.020) Years of nursing work experience Less than 1 year 1-5 1.401 (0.754, 2.604) 1.067 (0.460, 2.474) 1.601 * (1.010, 2.537) 1.362 (0.771, 2.407) 6-10 1.664 (0.884, 3.132) 0.948 (0.358, 2.508) 1.737 * (1.097, 2.750) 1.618 (0.851, 3.075) 11-20 0.577 (0.316, 1.051) 0.475 (0.154, 1.466) 0.966 (0.623, 1.500) 0.975 (0.464, 2.050) 21 or above 0.201 *** (0.110, 0.367) 0.344 (0.095, 1.250) 0.327 *** (0.208, 0.515) 0.741 (0.311, 1.767) Income per year, CNY ≤4999 5000-9999 0.786 (0.458, 1.348) 0.953 (0.465, 1.953) 1.291 (0.881, 1.688) 1.407 (0.933, 2.123) 10000-14999 0.653 (0.382, 1.118) 0.929 (0.437, 1.977) 0.783 (0.562, 1.089) 1.049 (0.672, 1.637) 15000 or higher 0.205 *** (0.118, 0.359) 0.598 (0.260, 1.376) 0.447 *** (0.313, 0.640) 1.274 (0.747, 2.174) Employment type Permanent staff Contract staff 4.377 *** (3.394, 5.644) 1.441 (0.890, 2.333) 4.245 *** (3.399, 5.301) 2.830 *** (1.952, 4.101) Professional titles Primary title Intermediate title 0.335 *** (0.266, 0.423) 0.848 (0.512, 1.406) 0.522 *** (0.434, 0.627) 0.969 (0.671, 1.398) Senior title 0.123 *** (0.081, 0.186) 0.859 (0.390, 1.895) 0.180 *** (0.128, 0.255) 1.054 (0.552, 2.014) Hierarchy of nurse N0 N1-N2 1.457 * (1.006, 2.111) 1.535 (0.890, 2.646) 1.297 (0.960, 1.754) 1.177 (0.786, 1.763) N3-N4 0.535 ** (0.367, 0.779) 2.014 (0.998, 4.061) 0.659 ** (0.486, 0.894) 1.245 (0.743, 2.085) N5-N6 0.238 *** (0.126, 0.452) 2.250 (0.789, 6.421) 0.267 *** (0.165, 0.434) 1.268 (0.576, 2.793) Job duty Clinical nurse Administrative nurse and others 0.460 *** (0.358, 0.591) 1.221 (0.824, 1.810) 0.582 *** (0.497, 0.707) 1.861 *** (1.358, 2.550) Marital status Married Single 2.903 *** (2.279, 3.698) 1.240 (0.800, 1.921) 2.209 *** (1.818, 2.684) 1.351 (0.977, 1.867) Divorced or widowed 0.379 ** (0.200, 0.719) 0.572 (0.250, 1.310) 0.509 ** (0.319, 0.813) 0.708 (0.403, 1.245) Night shift per month None 1-5 2.096 *** (1.615, 2.720) 1.081 (0.744, 1.569) 1.689 *** (1.379, 2.068) 1.058 (0.805, 1.390) 6-10 3.621 *** (2.694, 4.867) 1.155 (0.741, 1.798) 2.750 *** (2.161, 3.499) 1.096 (0.790, 1.520) 11 or more 5.121 *** (3.369, 7.785) 0.905 (0.489, 1.672) 3.216 *** (2.226, 4.648) 1.117 (0.697, 1.788) Hospital type General hospital Specialist hospital 0.675 ** (0.515, 0.885) 0.865 (0.605, 1.237) 1.006 (0.800, 1.265) 0.997 (0.747, 1.332) Working unit Surgery Medicine 1.345 (0.992, 1.847) 0.869 (0.588, 1.286) 1.433 ** (1.098, 1.869) 1.109 (0.810, 1.518) Emergency 0.680 (0.404, 1.143) 0.566 (0.288, 1.113) 0.948 (0.638, 1.409) 0.805 (0.500, 1.296) Intensive care unit 2.770 * (1.166, 6.584) 0.563 (0.190, 1.666) 1.331 (0.794, 2.232) 0.920 (0.489, 1.732) Out-patient 0.490 *** (0.356, 0.677) 0.890 (0.562, 1.409) 0.579 *** (0.436, 0.767) 0.819 (0.561, 1.195) Other 0.632 ** (0.476, 0.840) 1.032 (0.709, 1.503) 0.622 *** (0.497, 0.777) 0.755 (0.575, 0.992) Job satisfaction Satisfied Average 6.160 *** (4.661, 8.142) 2.741 *** (1.950, 3.852) 4.638 *** (3.667, 5.867) 2.160 *** (1.635, 2.852) Not satisfied 21.079 *** (11.370, 39.077) 6.536 *** (3.129, 13.653) 9.548 *** (5.148, 17.709) 3.598 *** (1.783, 7.263) Lack of interest in nursing No Yes 5.835 *** (4.639, 7.340) 2.802 *** (2.100, 3.740) 4.329 *** (3.606, 5.197) 2.513 *** (2.014, 3.136) Your attitude towards the current pandemic management Feel nothing Normal mindset 0.491 *** (0.244, 0.990) 1.179 (0.486, 2.856) 0.527 *** (0.380, 0.732) 1.265 (0.844, 1.896) Mildly nervous, worried 0.660 *** (0.323, 1.347) 1.214 (0.492, 2.994) 0.836 (0.557, 1.255) 1.342 (0.823, 2.188) Very panic, anxiety 0.928 (0.445, 1.932) 0.765 (0.299, 1.959) 0.945 (0.496, 1.801) 0.722 (0.342, 1.527) PSS-10 Q1 Q2 1.508 ** (1.163, 1.954) 1.160 (0.826, 1.629) 2.020 *** (1.620, 2.518) 0.954 (0.720, 1.264) Q3 2.408 *** (1.734, 3.344) 1.509 (0.961, 2.371) 3.540 *** (2.741, 4.571) 0.882 (0.616, 1.263) Q4 2.627 *** (1.873, 3.685) 1.004 (0.599, 1.684) 4.657 *** (3.576, 6.065) 0.740 (0.477, 1.148) GAD-7 Q1 Q2 2.435 *** (1.903, 3.117) 1.130 (0.771, 1.657) 2.718 *** (2.220, 3.329) 1.180 (0.867, 1.605) Q3 2.761 *** (1.766, 4.317) 1.107 (0.561, 2.184) 2.967 *** (2.169, 4.060) 0.653 (0.387, 1.102) Q4 3.362 *** (2.404, 4.700) 0.970 (0.496, 1.899) 2.890 *** (1.782, 4.688) 0.577 (0.276, 1.207) PHQ-9 Q1 Q2 2.588 *** (1.984, 3.376) 1.405 (0.943, 2.093) 2.146 *** (1.716, 2.684) 1.334 * (1.087, 1.803) Q3 3.036 *** (2.217, 4.159) 0.789 (0.472, 1.348) 4.187 *** (3.248, 5.399) 1.754 ** (1.155, 2.663) Q4 3.583 *** (2.566, 5.003) 0.590 (0.300, 1.160) 5.210 *** (3.916, 6.931) 1.540 (0.885, 2.681) ISI Q1 Q2 2.175 *** (1.684, 2.809) 1.142 (0.801, 1.629) 1.912 *** (1.527, 2.394) 1.015 (0.757, 1.361) Q3 3.328 *** (2.440, 4.539) 1.426 (0.888, 2.291) 2.874 *** (2.254, 3.664) 0.960 (0.683, 1.350) Q4 4.260 *** (2.668, 6.800) 1.006 (0.498, 2.030) 3.692 *** (2.849, 4.784) 0.795 (0.527, 1.200) MBI-GS Q1 Q2 2.979 *** (2.258, 3.930) 1.360 ** (1.051, 1.945) 2.412 *** (1.931, 3.013) 1.787 *** (1.357, 2.354) Q3 8.062 *** (5.792, 11.222) 2.885 *** (1.817, 4.581) 5.848 *** (4.507, 7.586) 3.138 *** (2.170, 4.540) Q4 14.855 *** (9.984, 22.104) 4.096 *** (2.095, 8.006) 10.210 *** (7.578, 13.757) 3.768 *** (2.333, 6.088) IUS-12 Q1 Q2 1.829 *** (1.383, 2.417) 0.899 (0.619, 1.304) 1.624 *** (1.296, 2.035) 0.951 (0.716, 1.264) Q3 2.099 *** (1.561, 2.823) 0.772 (0.511, 1.167) 2.245 *** (1.755, 2.873) 1.040 (0.752, 1.440) Q4 2.942 *** (2.146, 4.033) 0.683 (0.415, 1.121) 2.597 *** (2.014, 3.348) 0.877 (0.611, 1.260) SWLS Q1 Q2 0.377 *** (0.256, 0.555) 0.579 * (0.368, 0.912) 0.592 *** (0.441, 0.794) 0.683 * (0.486, 0.960) Q3 0.225 *** (0.147, 0.344) 0.549 * (0.328, 0.921) 0.327 *** (0.244, 0.439) 0.605 ** (0.427, 0.856) Q4 0.090 *** (0.061, 0.133） 0.343 *** (0.213, 0.553) 0.127 *** (0.094, 0.172) 0.395 *** (0.271, 0.576) Note: *** P <0.001, ** P <0.01, * P <0.05. PSS-10 represents the total score of the Perceived Stress Scale. Q1 represents the first quartile; Q2 represents the second quartile; Q3 represents the third quartile; and Q4 represents the fourth quartile. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 25 Feb, 2025 Read the published version in BMC Nursing → Version 1 posted Editorial decision: Revision requested 04 Nov, 2024 Editor assigned by journal 01 Nov, 2024 Submission checks completed at journal 01 Nov, 2024 First submitted to journal 13 Oct, 2024 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-5257180","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":373664292,"identity":"bf6c4108-f60c-460d-8612-51cd78afebd1","order_by":0,"name":"Julan Xiao","email":"","orcid":"","institution":"ShenZhen People’s Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Julan","middleName":"","lastName":"Xiao","suffix":""},{"id":373664293,"identity":"29aa0273-b18f-48cc-be0b-0c116fb0a6b8","order_by":1,"name":"Lili Liu","email":"","orcid":"","institution":"Yunnan Minzu University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lili","middleName":"","lastName":"Liu","suffix":""},{"id":373664294,"identity":"750c90bf-d542-4c18-b192-e56f1ebc6be2","order_by":2,"name":"Yueming Peng","email":"","orcid":"","institution":"ShenZhen People’s Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yueming","middleName":"","lastName":"Peng","suffix":""},{"id":373664295,"identity":"0a41e9db-c737-4810-b2c5-dfb336c4d579","order_by":3,"name":"Xia Lv","email":"","orcid":"","institution":"ShenZhen People’s Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xia","middleName":"","lastName":"Lv","suffix":""},{"id":373664296,"identity":"2906efd5-f92d-4315-9e7f-3e7c82a874ab","order_by":4,"name":"Chunfeng Xing","email":"","orcid":"","institution":"Shenzhen Guangming Distract People’s Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chunfeng","middleName":"","lastName":"Xing","suffix":""},{"id":373664297,"identity":"37ae06d9-87ee-409b-b238-2b51dead1a2d","order_by":5,"name":"Yanling Tao","email":"","orcid":"","institution":"Longgang Central Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yanling","middleName":"","lastName":"Tao","suffix":""},{"id":373664298,"identity":"427b332f-2241-42e5-bfde-54c9a7a812b2","order_by":6,"name":"Shening Zhu","email":"","orcid":"","institution":"Shenzhen Maternity and Child Healthcare Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shening","middleName":"","lastName":"Zhu","suffix":""},{"id":373664299,"identity":"f404f873-e14b-40da-886e-1fea1d3cbc7a","order_by":7,"name":"Aihuan Mai","email":"","orcid":"","institution":"ShenZhen People’s 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01:08:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5257180/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5257180/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12912-025-02851-1","type":"published","date":"2025-02-25T15:57:01+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":68933138,"identity":"b0dbfb97-4ca9-4566-bc92-df232ef29e01","added_by":"auto","created_at":"2024-11-13 15:56:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":74755,"visible":true,"origin":"","legend":"\u003cp\u003eBar graph of the prevalence of turnover intention\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5257180/v1/6f1549dc9d78667210999471.png"},{"id":77622314,"identity":"55dc25b7-2bc5-40b1-872d-d1062d46121f","added_by":"auto","created_at":"2025-03-03 16:04:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2184963,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5257180/v1/ce3c5ee6-7bb5-4a7f-8be7-0ec01dabe3ec.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"What is behind high turnover intention among hospital nurses during the full liberalization of COVID-19 and post-pandemic era in China: a 2-wave repeated multicenter survey","fulltext":[{"header":"1 | BACKGROUND","content":"\u003cp\u003eThe COVID−19 pandemic posed a dramatic effect and significant threat to hospitals nurses of the entire world owing to the urgency and uncertainty of the situation[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. As the fight against the epidemic progresses, the National Health Commission of the People’s Republic of China (NHCPR) issued a circular on further optimizing prevention and control measures against COVID−19 and suddenly adopted a policy of full liberalization, called daily nucleic acid testing or temperature screening of patients with COVID−19 infection[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In a very short and fast time, whether they were medical workers or the general population, most people were infected. However, hospital nurses had to cope with the challenges and difficulties such as the huge increase in the number of critically ill COVID−19 patients, and had to work while themselfs were infected. Previous research has reported that nurses were particularly vulnerable to developing physical and psychological issues from excessive and high intensity workloads and long-term usage of personal protective equipment[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. It was proved to associate with increased turnover intention[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eNurses component the largest proportion of hospital health care workers, play a crucial role in the treatment, care, and control of patients’ disease progression during disease outbreaks, disasters, and emergency situations[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In addition, nurses have the closest proximity to COVID−19 patients worldwide and spent more time caring for patients than other health care professionals[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Nurses were at the forefront of defeating the pandemic and were therefore at risk of developing high levels of stress and physical and psychological depletion[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Moreover, previous evidence has reported that nurses witnessed the high number of death among critically ill COVID−19 patients, relatives and colleagues developing a tremendous psychological burden[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. During the COVID−19 pandemic, A meta-analysis showed that the overall prevalence of burnout, depression, anxiety, insomnia and psychological distress among healthcare workers was 37.4%, 31.8%, 34.4%, 27.8% and 46.1%, respectively[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. After the sudden full liberalization of COVID−19, nurses experienced acute and exacerbated psychological problem. A multicenter cross-sectional analysis during the full liberalization of COVID−19 period found that frontline nurses experienced turnover intentions (37.66%), depressive symptoms (69.20%), anxiety (62.51%) and insomnia (76.78%) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. More importantly, poor psychological health among nurses is a significant predictor of turnover intention[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. With a shortage of nurses, excavating the factors associated with nurses’ turnover intention and reducing nurses’ turnover intention are the essential issues that nursing managers and scholars need to attention.\u003c/p\u003e\u003cp\u003eTurnover intention, which refers to employees’ proclivity withdrawal from their current position and consider for new possibilities, is a crucial predictor of turnover, however it does not always result in real turnover[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Nurses tended to have higher turnover intentions compared to other professions, a research illustrated that the proportion of hospital nurses with turnover intention ranged from 20.2 to 56.1%[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], stating that nurses cohort is erratic and constantly in a state of change. At the moment, sufficient evidence illustrated that turnover intention is the direct premise of turnover behavior[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], which is a strong predictor of actual turnover[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Turnover intention may greatly hinder the development of nursing career [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Frequent nurse turnover has many negative impact on nursing work quality, patients outcomes, medical organization stability and so on, it may lead to emotional instability and slack behavior of other nurses in the organization, and aggravates the hospital’s potentially high expenditure in new staff recruitment, hiring and training[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], especially in remote and poverty-stricken rural areas of China, where experienced a shortage of hospital nursing workers[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The loss of experienced nurses, particularly specialty ones, may adversely have an adverse impact on the provision and continuity of nursing services, which may result in higher incidence rates of nursing adverse events and patient mortality[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The State of the World Nursing 2020 declared that the shortage of hospital nurses will reach 5.7\u0026nbsp;million by 2030 in global, which means that the high turnover rate of nurses will undoubtedly be a great challenge for the chinese healthcare system[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In consequence, a greater insight into turnover intention contribute to decision makers taking preventive measures to reduce eliminate the nurse intention to leave before actual turnover occurs and saving cost for the organization[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eChina has gone through the last two stages in the process of fighting the COVID−19 pandemic, including a full liberalization of COVID−19 (the National Health Commission of the People’s Republic of China issued a circular on further optimizing prevention and control measures against COVID−19 and suddenly adopted a policy of full liberalization) and post-pandemic era (pandemic prevention and controlwork shifted from a full liberalization state to a normal state). However, to the best of our knowledge, most previous studies have had cross-sectional designs. Little is known about turnover intention and changing trends among hospital nurses since the full liberalization of the pandemic, which may hinder nursing managers and policymakers from formulating preventive and promotive interventions to prevent the adverse effects of turnover intention.\u003c/p\u003e\u003cp\u003eDuring the COVID−19 pandemic, the prevalence of turnover intention among hospital nurses and nursing assistants was already high[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, there is scarce evidence about the topic during the full liberalization of COVID−19 and post-pandemic era. And a comparative study on the turnover intention of Chinese hospital nurses during the full liberalization of COVID−19 period and post-pandemic era has not been reported. In addition, it is unclear which potential factors may be associated with turnover intention of nurses in the different periods, and this lack of research may hinder timely and effectively interventions of turnover intention. To fill this grap, the current study was conducted a large-sample repeated cross-sectional study of Chinese hospital nurses during the full liberalization of COVID−19 period (T1: 27 December 2022 to 7 January 2023), and post-pandemic era (T2: 11 May to 23 May 2023), aiming to determine the prevalence of turnover intention and identify its significantly associated factors among nurses in different periods of COVID−19 and post-pandemic era. Consequently, our study will make a positive contribution to the literature on nursing mangers and policymakers pay close attention to turnover intention, investigate antecedents, and implement targeted strategies to reduce nurses’ turnover intention.\u003c/p\u003e"},{"header":"2 | METHODS","content":"\u003cp\u003e\u003cb\u003e2.1 | Study setting and sample\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis large-scale repeated cross-sectional survey was carried out in Shenzhen, Guangdong Province, which is one of the highest-income cities in China. Convenience sampling was used for the recruitment strategy, in total, 25 hospitals’ nurses were selected to participate in the current survey, which was conducted from 27 December 2022 to 7 January 2023 (T1) and 11 May to 23 May 2023 (T2). The online questionnaire was shared via “Wenjuan Xing”, a professional questionnaire survey platform that is widely used in China[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. We provided a normative notice applicable to these 25 hospitals, exploring the questionnaire survey’s purpose, significance, mode of participation, completion method of this research and deadline. The online survey was first disseminated through an instant messaging system, the WeChat group, to nursing managers at Shenzhen hospitals, who were encouraged to pass it on to other nurses at Shenzhen hospitals. The inclusion criteria for participants were as follows: (1) nurses aged 18 and above; (2) normal language expression and understanding, and the ability to understand the investigation content and cooperate with the research; and (3) the ability to give voluntary informed consent. The exclusion criteria were (1) history of mental illness and (2) serious physical diseases. Screening criteria included a response time of fewer than 200s[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] to complete the survey and the deletion of incomplete or repeated answers. The effective sample sizes of T1 and T2 were 1,766 and 2,643, respectively. And the effective rates were 96.34% and 94.87%, respectively.\u003c/p\u003e\u003cp\u003e\u003cb\u003e2.2 | Data collection\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAll investigators received unified training on the online survey. Each question was a mandatory item and could only be answered once by the same IP address. In addition, all of the participants were allowed to stopped at any time, and anonymity would be guaranteed. The online questionnaire began with informed consent. Hospital nurses needed to read the informed consent and choose the “agree” option to begin filling out the questionnaire; otherwise, the questionnaire could not be completed. To ensure the feasibility and suitability of the questionnaire, online pilot survey including 45 nurses from Shenzhen was accomplished. Then the nurses who participated were also asked for suggestion on questionnaire modification. Pilot test data were not used for the final statistics. The trained authors of this article distributed the final version of the questionnaires to the hospital nurses for data collection.\u003c/p\u003e\u003cp\u003e \u003cb\u003e2.3 | Measures\u003c/b\u003e \u003c/p\u003e\u003cp\u003e \u003cb\u003e2.3.1 | Sociodemographic and work-related characteristics\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA general information questionnaire (created by the authors from literature reviews and group discussions) included two parts. The first part collected data on nurses’ characteristics included gender, age, educational level, working years, marital status, monthly income per year, employment type, professional technical titles, hierarchy of nurse, job duty, night shifts per month, hospital type, and working unit. The second part included job satisfaction, interest in nursing, and compared with the outbreak period, the attitude toward the current epidemic.\u003c/p\u003e\u003cp\u003e\u003cb\u003e2.3.2 | Perceived Stress\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe 10-item Perceived Stress Scale (PSS−10) is based on the theory of psychological stress and was developed in 1983 by Cohen et al[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The PSS−10 is used to measure how stressful an individual perceives events in daily life. The PSS−10 is a 10-item self-report scale, and the total score on the PSS−10 ranges from 0–40. Higher scores indicate that the individual’s perceived stress level is high. In the current study, the Cronbach’s alpha for the PSS−10 was 0.72 at T1, and 0.82 at T2.\u003c/p\u003e\u003cp\u003e\u003cb\u003e2.3.3 | Depressive symptoms\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe 9-item Patient Health Questionnaire (PHQ−9) is a self-report scale assessing patients’ and general populations’ depressive symptoms over the past 2 weeks[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The questionnaire is based on the nine symptoms of depression in the US Diagnostic Standard for Mental Illness (DSM-IV)[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The PHQ−9 is rated on a 4-point Likert scale, and the total score ranges from 0 to 27, with higher scores indicating more obvious the state of depression. In our study, the Cronbach’s alpha for the PHQ−9 both were 0.93 at T1 and T2.\u003c/p\u003e\u003cp\u003e\u003cb\u003e2.3.4 | Anxiety\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe used the 7-item Generalized Anxiety Disorder (GAD−7) to evaluate the severity of anxiety, which is a self-report scale with good reliability and validity[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The participants were asked to report their symptoms of anxiety over the previous 2 weeks on a 4-point Likert scale from 0 (never) to 3 (almost every day), for a total score ranging from 0 to 21. Higher scores indicated higher degree of anxiety symptoms. Cronbach’s alpha in the present study was 0.97 at T1, and 0.96 at T2.\u003c/p\u003e\u003cp\u003e\u003cb\u003e2.3.5 | Insomnia\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe 7-item Insomnia Severity Index (ISI) was used to evaluate the severity of insomnia in the past week. The ISI consists of 7 items, each of which is rated on a 5-point scale, for a total score ranging from 0 to 28. Higher scores on the ISI indicates greater insomnia severity during the previous month. Cronbach’s alpha of the scale both were 0.93 at T1 and T2 in this study.\u003c/p\u003e\u003cp\u003e\u003cb\u003e2.3.6 | Intolerance Of Uncertainty\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe Intolerance of Uncertainty Scale (IUS) is a selfassessment instrument to measure intolerance of uncertainty through 12 items with a 5-point Likert scale[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The IUS calculates a total score, with a higher score illustrate a higher intolerance of uncertainty, resulting in difficulty in tolerating the negative emotion, triggered by a lack of information regarding a specific state[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Cronbach’s alpha of IUS in the present study was 0.91 at T1, and 0.92 at T2.\u003c/p\u003e\u003cp\u003e\u003cb\u003e2.3.7 | Job Burnout\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe Maslach Burnout Inventory-General Burnout Survey (MBI-GS) was used to measure burnout in HCWs[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This tool encompasses emotional exhaustion, depersonalization, and reduced personal accomplishment. The MBI-GS comprises 15 items scored on a 7-point Likert scale ranging from 0 (never) to 6 (everyday). High scores for this questionnaire indicate the incidence of burnout[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Cronbach’s alpha for the MBI-GS in the current study was 0.87 at T1, and 0.92 at T2.\u003c/p\u003e\u003cp\u003e\u003cb\u003e2.3.8 | Satisfaction with Life\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe Satisfaction with Life Scale (SWLS) was conducted to measure an individual’s global judgment regarding their life satisfaction. The SWLS contains 5 items in which 3 items reflect present life satisfaction whereas the other 2 items embody past life satisfaction. The scale is rated on a 7-point Likert scale from 1 (terrible dissatisfaction) to 7 (high satisfaction)[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Higher scores indicated higher degree of life satisfaction. Cronbach’s alpha for the SWLS in the current study was 0.93 at T1, and 0.94 at T2.\u003c/p\u003e\u003cp\u003e\u003cb\u003e2.3.9 | Turnover Intention\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe turnover intention of hospital nurses were evaluated by the Turnover Intention Scale, which included the possibility of leaving the current job, the motivation to find a new job, and the possibility of acquiring another job[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. It consists of six items with a 4-point response scale, and with a total score of four points for each item and higher scores suggesting a stronger turnover intention of nurses. Cronbach’s alpha of the scale both were 0.81 at T1 and 0.80 at T2 in this study.\u003c/p\u003e\u003cp\u003e\u003cb\u003e2.4 | Data analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eData were analyzed with SPSS version 26.0. Descriptive statistics, including frequencies and central tendencies, were calculated to characterize the sample’s demographic profile. The chi-square test was performed to test the diference between the prevalence of turnover intention from T1 to T2. Binary and multiple logistic regression analyses were conducted to determine the potential factors of turnover intention among hospital nurses during the sudden full liberalization of COVID−19 peroid and post-pandemic era. The four mental health symptoms (perceived stress, depression, anxiety, and insomnia) and other independent variables (job burnout, intolerance of uncertainty, satisfaction with life) were divided into four groups using quartiles, indicating the position of the score in the sample. The first quartile was 0–25%, the second was 25%−50%, the third was 50%−75%, and the fourth was 75%−100%. Odds ratios (ORs) and 95% confidence intervals (95% CIs) were obtained from the logistic regression models. \u003cem\u003eP\u003c/em\u003e \u0026lt;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"3 | RESULTS","content":"\u003cp\u003e\u003cb\u003e3.1 | Demographic characteristics\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe descriptive characteristics of the hospital nurses are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In the two-wave survey, most of the participants 95.92% were female in T1, and 95.27% in T2. The majority (\u0026gt;75%) were younger than 40 years of age. Contract staffs accounted for a large proportion (\u0026gt; 80%). The number of clinical nurses was approximately four times that of administrative nurse and others, with T1 clinical nurses accounting for 80.92%, and T2 accounting for 77.75%. Detailed participants demographics and mental health at the two time points are reported in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003e3.2 | Prevalence of turnover intention at T1-T2\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the prevalence of turnover intention at T1 and T2 was 73.33% (95% CI: [71.18, 75.37]), and 72.34% (95% CI: [70.64, 74.08]), respectively. The χ² test between the two groups wasn’t signifcant, χ²=7.522, \u003cem\u003eP =\u003c/em\u003e 0.057.\u003c/p\u003e\u003cp\u003e\u003cb\u003e3.3 | Correlation between anxiety, depression, perceived stress, insomnia, intolerance of uncertainty, job burnout, satisfaction with life and turnover intention\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe average turnover intention scores among all the nurses at T1 and T2 were found to be 2.49 (0.63) and 2.47 (0.62) points (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), respectively. Higher scores of turnover intention were positively correlated with anxiety, depression, perceived stress, insomia, intolerance of uncertainty, and job burnout ( all \u003cem\u003eP\u003c/em\u003e\u0026lt;0.01)in T1 and T2. Significant negative relationships were also found between turnover intention and satisfaction with life both (all \u003cem\u003eP\u003c/em\u003e\u0026lt;0.01) in T1 and T2.\u003c/p\u003e\u003cp\u003e\u003cb\u003e3.4 | Logistic regression analysis of factors associated with turnover intention\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe binary and multiple logistic regression analysis results of the factors for turnover intention are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Binary and multiple logistic regression analysis were conducted to reckon the independent odds ratio (Crude OR, cOR) and adjusted odds ratio (Adj OR, aOR) of the risk factors for turnover intention, which were used to refect the OR of each factor individually and jointly when predicting turnover intention[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMost influencing factors could independently predict turnover intention, but there were differences when they were jointly predicted. Ages; years of nursing work experience; 15000 or higher income per year; professional titles; hierarchy of nurse; marital status; night shift per month; working unit; nurses’ attitude towards the current pandemic management; perceived stress; anxiety; insomnia and intolerance of uncertainty could independently predict turnover intention, but the aOR were not signifcant from T1 to T2 when predicting turnover intention jointly (\u003cem\u003ePs\u003c/em\u003e \u0026gt; 0.05). As we can see, nurses who were average or not satisfied with job; lack of interest in nursing; higher job burnout at T1 and T2 were risk factor for turnover intention when predicting turnover intention jointly (\u003cem\u003ePs\u003c/em\u003e\u0026lt;0.05). Moreover, higher satisfaction with life at T1 and T2 were protective factor for turnover intention when predicting jointly (\u003cem\u003ePs\u003c/em\u003e\u0026lt;0.05). Employment type for contract staff was a risk factor for turnover intention when it was predicted separately [T1: cOR = 4.377, 95% CI: (3.394, 5.644), \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001; T2: cOR = 4.245, 95% CI: (3.399, 5.301), \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001]. However, when jointly predicted, this variable remained a risk factor at T2 [aOR = 2.830, 95% CI: (1.952, 4.101), \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001)], but the aOR was not signifcant at T1 when predicting jointly (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05). Job duty as administrative nurse and others was a protective factor for turnover intention when it was predicted separately [T1: cOR = 0.460, 95% CI: (0.358, 0.591), \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001; T2: cOR = 0.582, 95% CI: (0.358, 0.591), \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001]. Interestingly, when jointly predicted, this variable became a risk factor at T2 [aOR = 1.861, 95% CI: (1.358, 2.550), \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001)], but the aOR was not signifcant at T1 when predicting turnover intention jointly (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05). Depression was negatively correlated with turnover intention when predicted individually (\u003cem\u003ePs\u003c/em\u003e \u0026lt; 0.001), while depression remained a risk factor at T2 [aOR = 2.830, 95% CI: (1.952, 4.101), P \u0026lt; 0.001)], but the aOR was not signifcant at T1 when predicting jointly (P \u0026gt; 0.05).\u003c/p\u003e"},{"header":"4 | DISCUSSION","content":"\u003cp\u003eIn one of the first large-scale, multicenter, 2-wave cross-sectional survey of turnover intention among Chinese hospital nurses during the full liberalization of COVID−19 and post-pandemic era, our findings reveal an insignificant decreased trend in the prevalence of overall turnover intention among nursing staff during the full liberalization of COVID−19 and post-pandemic era periods. The results of this findings indicated that the prevalence of turnover intention were particularly higher, no matter in the full liberalization of COVID−19 period or post-pandemic era. The scores were also higher compared with another research which conducted among hospital nurses at the beginning of the outbreak of the COVID−19[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The results illustrated that while the post-pandemic era has returned to normal conditions, the long-term negative impact of the pandemic should not be overlooked. Although the post-pandemic era has returned to normal, the prevalence of turnover intention is still higher, which suggests that we need pay attention to turnover intention among nursing staff, continue to monitor and track turnover intention and identify appropriate response and intervention strategies, which were consistent with previous researches.\u003c/p\u003e\u003cp\u003eThe result demonstrated that mental health problems, such as anxiety, depression, insomnia were positively associated with turnover intention in nursing staff. Several studies have confirmed that the significant and positive associations between depression, anxiety, insomnia and turnover intention[\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e–\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. And anxiety and depression have been repeatedly identified as predictors of turnover intention in previous literature[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Notably, after adjusted other variables, anxiety and insomnia were not predictors of turnover intention among nurses, and depression symptom was not significantly associated with turnover intention at beginning of the full liberalization of COVID−19 (T1), but turn into significant at post-pandemic era periods (T2). However, Tabur et al. showed that anxiety and depression were not predictors of turnover intention among healthcare professionals[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The result appeared consistent with our study, which can largely be attributed to the discrepancies in the statistical analysis method[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Due to the significant correlation between anxiety and depression scores, statistically significant results could change when anxiety and depression were entered into the same model simultaneously[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. On the other hand, the above result may be related to a number of the variables included in the prediction model[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Because there were many variables included in the equation for joint prediction, the effect of depression symptoms was weakened or interacted with other factors[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSimilar to the results of previous studies, psychological factors such as job burnout, satisfaction with life were important factors for turnover intention. Our fingdings revealed that nursing staff with higher level job burnout experienced more turnover intention. According to previous studies focusing on the relationship between job burnout and turnover intention, greater job burnout were associated with higher turnover intention[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. This may reasonablely explain why the rate of turnover intention were particularly higher in nursing staff at different stages of the full liberalization of COVID−19 and post-pandemic era. Moreover, satisfaction with life can be measured by general wellbeing. In current study, satisfaction with life were negatively associated with turnover intention among nursing staff, and the result was similar to previous studies[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], illustrating that improving nurses’ wellbeing can decrease their turnover intentions and turnover rates. According to literature, a favorable sense of wellbeing can great enhance nurses’ psychological resilience and mental health, and it was associated with lower absence and turnover intention[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Therefore, preventive and promotive interventions and positive psychology interventions should be implemented as far as possible to reduce nurses experiencing turnover intention.\u003c/p\u003e\u003cp\u003eIn this study, nurse’s turnover intention was significantly influenced by their satisfaction with their job, nurses who were dissatisfaction with their nursing work more likely to leave their workplace, which was consistent with the result of previous literature[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. A cross-sectional study in Ethiopia showed that among hospital nurses, who were unsatisfied on their job autonomy were 2.55 more likely to intend to quit their jobs than nurses who reported to be satisfied[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Turnover theory attach importance to turnover intention begins with dissatisfaction among workers, and our study supports the theory[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. In order to alleviate nurses’ turnover intention, effective intervention measures should be implemented in time to improve their job satisfaction. Another significant predictor of turnover intention in the hospital was interest in nursing work among nurses, that is, nurses who lack of interest in their job, they usually lack clear career goals and planning; to be more serious, they even wonder whether they were competent for the job. Eventually, such nursing staff may experience higher turnover intention. Some prior studies presented that professional nurses in emergency or critical care medicine were tend to higher overwork and stress than those of other departments, which could lead to stronger turnover intention[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. However, no such associations were revealed in current study. On account of nurses in the 2-wave repeated multicenter survey were mostly medical, surgical, and outpatient nurses, and low number of nurses in the emergency department and ICU, the results in our study were not similar to those in previous studies. Therefore, working department may not be factors that influence turnover intention among chinese nursing staffs, and further studies is needed to shed light on this issue.\u003c/p\u003e\u003cp\u003eFurthermore, our fingdings revealed that nursing staff employed as contract staff compared with permanent staff, administrative nurses and others with clinical nurses, have higher rate of turnover intention at T2. The main reason may be that the proportion of contract nurses (81.99%) at TI and (85.19%) at T2 in present study were higher than that reported by Cao[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] (65.72%) and Chen[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] (69.46%), which may lead to a higher turnover intention. Due to the 2-wave repeated multicenter survey were conducted at different special times, the above result were not significant in T1, these also confirmed that the factors related to high turnover intention at different periods were not consistent. To be exact, the chinese nursing employment system has two parallel types of employment for nursing staff: permanent nurses and contract nurses. Although two parallel types of nurses undertake the same work, contract nurses receive lower salaries and fewer benefits than permanent nurses; therefore, contract nurses are more likely to intend to leave their positions[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. For the sake of reducing nurse turnover, chinese government priorities should take reasonable policies which was to developed to eliminate the disparities between permanent nurses and contract nurses, improve development opportunities for hospital nursing staffs[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Our study found that administrative nurses had a increased risk for turnover intention than clinical nurses. Even though the professional careers of these administrative nurses were relatively successful, and they were having more work experience, better position and higher salary, the turnover intentions do not show a decreasing tendency. It is possible that they were undertaking more nursing management and quality and safety work. And moreover, the appointment of administrative nurses relatively conservative in China’s hospitals, as most administrative nurses were elected from the nurses with excellent clinical skills and rich nursing knowledge[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] irrespective of management abilities and without further administration training. Thus, administrative nurses have very few standards upon which could guide their work[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] and may feel they are not qualified enough for their position, especially in the special period of public emergency.\u003c/p\u003e\u003cp\u003eThe fingdings of this study may be helpful for guiding hospital administrators to systematically understand nurses’ responses to turnover intention during the pre- and post-pandemic era and further provide preventive and promotive interventions to reduce turnover intention and improve mental health in this special population. This study clarified factors associated with intention to leave the nursing profession during the full liberalization of COVID−19 period and post-pandemic era.\u003c/p\u003e\u003cp\u003eThere are several limitations in this current study, which involved in the sample size and sampling method. First, The convenience sampling method may not be representative or generalizable to the hospital nurses in China. The impact of the COVID−19 epidemic on nursing staff’s turnover intention may have been over or underestimated. We cannot know the prevalence of turnover intention of hospital nurses who did not participate in our research. Second, considering the limitations of turnover intention, job burnout, intolerance of uncertainty, satisfaction with life and mental health using correlation scale, this 2-wave repeated cross-sectional design limited the explanation of causality. Future longitudinal research is needed to accurate track the changes in turnover intention and mental health among nursing staff.\u003c/p\u003e"},{"header":"5 | CONCLUSION","content":"\u003cp\u003eIn conclusion, the current study first described the prevalence of turnover intention among Chinese hospital nurses during the two key windows of the full liberalization of COVID−19 and post-pandemic era. Although the post-pandemic era has returned to normal, the COVID−19 pandemic has had a long-term and far-reaching negative impact on nursing work, resulting in higher turnover intention and workforce shortages. In the meanwhile, we found that turnover intention among hospital nurses were related to multiple factors included dissatisfaction with nursing work, lack of interest in nursing, job burnout, satisfaction with life, depressive symptom, employment type and job duty. The findings contribute to a better understanding for hospital management of the prevalence of turnover intention among nursing staff since the full liberalization of COVID−19 and provide guidance and targeted practical recommendations of policy-making within nursing human resources departments. Longitudinal studies, qualitative studies even mixed studies are urgent needed to provide further evidence regarding the development of nursing career and mental health among nurses. The Chinese adaptations of the standardized questionnaires used in current study are reliable and valid and can be used to help healthcare managers identify areas of concern within their institution and take effective interventions to prevent nurses from resignation and promote their career success.\u003c/p\u003e"},{"header":"6 | IMPLICATIONS FOR NURSING MANAGEMENT","content":"\u003cp\u003eOur study suggest continuous detection of turnover intention among hospital nurses, implementing nursing job satisfaction-promoting and nurse-caring measures, cultivating nurses’ interest in nursing work and providing targeted psychological intervention services. Hospital managers should make full use of job duty advantages when allocating work according to administrative nurses’ capabilities to improve their management skills, and guide clinical nurses to devote themselves to nursing work with enthusiasm and initiative. According to the needs of nurses with different degrees, it is essential to provide different opportunities and support to help nurses pursue further education and career development, allocate human resources reasonably to remain active in completing their tasks and reduce job burnout and turnover rates. Meanwhile, selection and training of specialized nurses is also a very important career development direction. The development of specialized nursing enables specialized nurses to strengthen their work in the field, they are good at or like, and gain a sense of professional value and accomplishment.\u003c/p\u003e\u003cp\u003eAt the same time, we recommend various forms of stress reduction activities for nurses, such as psychological counseling, mindfulness-based stress reduction sandplay and so on, to improve their general wellbeing and emotional intelligence. We appeal for an increase in night shift fees and salaries for nurses, expanding reward and rest time for night nurses to ensure that nurses get enough rest after night shift and improve their professional happiness. In most developed countries, the income level of nurses is often higher than the average social income. Under the premise of ensuring the quality of nursing and patient safety, we promote the appointment and scheduling of nurses, and the head nurse tries to meet the individual needs of nurses under the premise of ensuring normal work. Only through these approaches will we be able to reduce job burnout and turnover intention of nurses, enhance career happiness and optimize the structure of nursing teams in China and, possibly, other countries as well.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eHUMAN ETHICS AND CONSENT TO PARTICIPATE DECLARATIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was in line with ethical principles, and the contents of the questionnaire didn\u0026rsquo;t involve private and sensitive topics such as names. More importantly, the study protocol was approved by the Institutional Ethics Board of Shenzhen People\u0026rsquo;s Hospital (Approval No. LL-KY-2023107-01). All individuals were given information about introducing the study and notified about their own right to withdraw at any time, and informed consent was sought from all eligible participants, which illustrated that they had understood the study in its entirety.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets that support the findings of the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJLX and WXL designed and managed the study, the methodology was developed by LLL and YMP. XL, CFX, YLT, SNZ, AHM, LJL, HYH, YF, WSP, HSX and JR were responsible for the questionnaire survey of frontline nurses in Shenzhen and data acquisition. JLX and LLL analyzed the statistics. JLX , WXL and LLL wrote the first draft of the manuscript, all authors revised the manuscript. All authors listed meet the authorship criteria according to the latest guidelines of the International Committee of Medical Journal Editors, and that all authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONFLICT OF INTEREST\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAFUNDING STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Shenzhen Peoples\u0026rsquo; Hospital and Young and Middle-aged Research Fund Project (grant number SYHL2021-N0006), Shenzhen Health Economics Society Research Fund Project (grant number 202336) and Nursing discipline research topic of Chinese Medical Association journal (grant number CMAPH-NRG2022042).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to all the nurses who took their time to participate in the study, especially those participants who provided our research with essential information about their feelings, supporting us to complete the survey.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCLINICAL TRIAL NUMBER\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHan SJ, Lee SY, Kim SE. 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Bmc Health Serv Res. 2017;17(1):112. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12913-017-2056-z\u003c/span\u003e\u003cspan address=\"10.1186/s12913-017-2056-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1 Descriptive characteristics of the sampled hospital nurses\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"553\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT1 N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT2 N (%)\u003c/strong\u003e\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: 42.9603%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e72 (4.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e125 (4.73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e1694\u0026nbsp;(95.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e2518 (95.27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge, years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003e18-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e763 (43.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e1072 (40.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003e30-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e579\u0026nbsp;(32.79%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e1022 (38.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003e\u0026ge;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e424\u0026nbsp;(24.01%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e549 (20.77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducational level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eTechnical secondary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e25\u0026nbsp;(1.42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e68 (2.57%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eJunior college\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e451\u0026nbsp;(25.54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e666 (25.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eBachelor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e1270\u0026nbsp;(71.91%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e1895 (71.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eMaster or higher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e20\u0026nbsp;(1.13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e14 (0.53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYears of nursing work experience\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eLess than 1 year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e75\u0026nbsp;(4.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e113 (4.28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003e1-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e469\u0026nbsp;(26.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e602 (22.78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003e6-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e421\u0026nbsp;(23.84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e649 (2.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003e11-20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e453\u0026nbsp;(25.65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e850 (32.16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003e21 or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e348\u0026nbsp;(19.71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e429 (16.23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMonthly income level (CHY)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003e\u0026le;4999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e105\u0026nbsp;(5.95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e234 (8.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003e5000-9999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e686\u0026nbsp;(38.84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e1205 (45.59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003e10000-14999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e686\u0026nbsp;(38.84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e827 (31.29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003e15000 or higher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e289\u0026nbsp;(16.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e377 (14.26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmployment type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003ePermanent staff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e318\u0026nbsp;(18.01%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e392 (14.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eContract staff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e1448\u0026nbsp;(81.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e2251 (85.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProfessional technical titles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003ePrimary technical title\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e1092\u0026nbsp;(61.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e1618 (61.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eIntermediate technical title\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e564\u0026nbsp;(31.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e873 (33.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eSenior technical title\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e110\u0026nbsp;(6.23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e152 (5.75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHierarchy of nurse\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eN0*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e183 (10.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e270 (10.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eN1-N2*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e988 (55.95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e1365 (51.65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eN3-N4*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e542 (30.69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e911 (34.47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eN4-N6*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e53 (3.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e97 (3.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eJob duty\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e Clinical nurse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e1429 (80.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e2055 (77.75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026nbsp;\u003c/strong\u003eAdministrative nurse and others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e337 (19.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e588 (22.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e1000\u0026nbsp;(56.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e1604 (60.69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e726\u0026nbsp;(41.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e965 (36.51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eDivorced or widowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e40\u0026nbsp;(2.27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e74 (2.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNight shift per month\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e542\u0026nbsp;(30.69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e789 (29.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003e1-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e514\u0026nbsp;(29.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e942 (35.64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003e6-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e470\u0026nbsp;(26.61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e670 (25.35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003e11 or more\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e240\u0026nbsp;(13.59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e242 (9.16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHospital type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eGeneral hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e1476\u0026nbsp;(83.58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e2204 (83.39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eSpecialist hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e290\u0026nbsp;(16.42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e439 (16.61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWorking unit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eSurgery (including\u0026nbsp;gynecology)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e578\u0026nbsp;(32.73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e761 (28.79%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eMedicine (including pediatrics)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e420\u0026nbsp;(23.78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e573 (21.68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eEmergency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e76\u0026nbsp;(4.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e154 (5.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eIntensive care unit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e59\u0026nbsp;(3.34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e100 (3.78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eOut-patient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e241\u0026nbsp;(13.65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e315 (11.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e392\u0026nbsp;(22.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e740 (28.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eJob satisfaction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eSatisfied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e856\u0026nbsp;(48.47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e1640 (62.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eNeutral satisfied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e618\u0026nbsp;(34.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e821 (31.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eNot satisfied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e292\u0026nbsp;(16.53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e182 (6.89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eL\u003c/strong\u003e\u003cstrong\u003eack of interest in nursing\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e665\u0026nbsp;(37.66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e1141 (43.17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e1101\u0026nbsp;(62.34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e1502 (56.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYour attitude towards the current pandemic management\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eVery panic, anxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e357\u0026nbsp;(20.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e77 (2.91%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eMildly nervous, worried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e500\u0026nbsp;(28.31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e340 (12.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eNormal mindset\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e854\u0026nbsp;(48.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e1968 (74.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eFeel nothing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e55\u0026nbsp;(3.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e258 (9.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGAD-7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e662 (37.49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e1300 (49.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e637 (36.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e904 (34.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e143 (8.10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e318 (12.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e324 (18.35%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e121 (4.58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePHQ-9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e544 (30.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e546 (20.66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e538 (30.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e833 (31.52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e352 (19.93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e706 (26.71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e332 (18.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e558 (21.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eISI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e410 (23.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e658 (24.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e745 (42.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e704 (26.64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e443 (25.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e657 (24.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e168 (9.51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e624 (23.61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePSS-10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e439 (24.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e645 (24.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e644 (36.47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e754 (28.53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e348 (19.71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e598 (22.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e335 (18.97%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e646 (24.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMBI-GS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e440 (24.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e660 (24.97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e445 (25.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e680 (25.73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e445 (25.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e643 (24.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e436 (24.69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e660 (24.97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIUS-12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e435 (24.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e610 (23.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e489 (27.69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e767 (29.02%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e423 (23.95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e639 (24.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e419 (23.73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e627 (23.72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSWLS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e419 (23.73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e577 (21.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e655 (37.09%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e894 (33.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e269 (15.23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e661 (25.01%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.9603%;\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6859%;\"\u003e\n \u003cp\u003e423 (23.95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.3538%;\"\u003e\n \u003cp\u003e511 (19.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: CHY1 = USD 0.14, based on the exchange rate on 24 September 2023.\u003c/p\u003e\n\u003cp\u003eN0, registered nurse who were unable to care for patients independently; N1, registered nurse and who were able to care for patients independently; N2, nurses who have been qualified for 3\u0026thinsp;years as N1 nurses; N3, nurses who have obtained intermediate titles and are able to undertake various clinical/teaching tasks and a certain managerial duty; N4, nurses who have acquired deputy senior title or above and are able to undertake the work of quality management, teaching management; N5, nurses who have acquired senior title and are able to complete the work of guidance and review; N6, on the basis of N5 nurses, working hours were longer than N5 head nurses.\u003c/p\u003e\n\u003cp\u003eGAD-7 represents the total score of the Generalized Anxiety Disorder Scale, PHQ-9 represents the total score of the 9-item Patient Health Questionnaire depressive symptoms, ISI represents the total score of the Insomnia Severity Index, PSS represents the total score of the 10-item Perceived Stress Scales, MBI-GS represents the total score of the Maslach Burnout Inventory-General Survey, IUS represents the total score of the Intolerance of Uncertainty Scale, and SWLS represents the total score of the Satisfaction with Life Scale. Q1 represents the first quartile, Q2 represents the second quartile, Q3 represents the third quartile, and Q4 represents the fourth quartile.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable2\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ecorrelation matrix between anxiety, depression, perceived stress, insomnia, intolerance of uncertainty, job burnout, satisfaction with life and turnover intention (T1 and T2)\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"910\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.7253%;\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.04396%;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.26374%;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.47253%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.36264%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.02198%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.7253%;\"\u003e\n \u003cp\u003e1.\u0026nbsp;Anxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.04396%;\"\u003e\n \u003cp\u003e7.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.26374%;\"\u003e\n \u003cp\u003e5.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.47253%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.36264%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.02198%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.7253%;\"\u003e\n \u003cp\u003e2.\u0026nbsp;Depression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.04396%;\"\u003e\n \u003cp\u003e9.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.26374%;\"\u003e\n \u003cp\u003e6.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e0.78**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.47253%;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.36264%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.02198%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.7253%;\"\u003e\n \u003cp\u003e3.\u0026nbsp;Perceived Stress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.04396%;\"\u003e\n \u003cp\u003e19.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.26374%;\"\u003e\n \u003cp\u003e5.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e0.51**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.47253%;\"\u003e\n \u003cp\u003e0.51**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.36264%;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.02198%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.7253%;\"\u003e\n \u003cp\u003e4.Insomnia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.04396%;\"\u003e\n \u003cp\u003e12.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.26374%;\"\u003e\n \u003cp\u003e6.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e0.62**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.47253%;\"\u003e\n \u003cp\u003e0.69**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.36264%;\"\u003e\n \u003cp\u003e0.41**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.02198%;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.7253%;\"\u003e\n \u003cp\u003e5.Intolerance of Uncertainty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.04396%;\"\u003e\n \u003cp\u003e37.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.26374%;\"\u003e\n \u003cp\u003e9.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e0.60**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.47253%;\"\u003e\n \u003cp\u003e0.59**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.36264%;\"\u003e\n \u003cp\u003e0.47**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.02198%;\"\u003e\n \u003cp\u003e0.47**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.7253%;\"\u003e\n \u003cp\u003e6.Job Burnout\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.04396%;\"\u003e\n \u003cp\u003e2.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.26374%;\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e0.68**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.47253%;\"\u003e\n \u003cp\u003e0.68**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.36264%;\"\u003e\n \u003cp\u003e0.43**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.02198%;\"\u003e\n \u003cp\u003e0.56**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e0.55**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.7253%;\"\u003e\n \u003cp\u003e7.Satisfaction with Life\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.04396%;\"\u003e\n \u003cp\u003e20.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.26374%;\"\u003e\n \u003cp\u003e6.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e-0.36**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.47253%;\"\u003e\n \u003cp\u003e-0.38**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.36264%;\"\u003e\n \u003cp\u003e-0.20**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.02198%;\"\u003e\n \u003cp\u003e-0.32**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e-0.29**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e-0.55**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.7253%;\"\u003e\n \u003cp\u003e8.Turnover Intention\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.04396%;\"\u003e\n \u003cp\u003e2.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.26374%;\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e0.30**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.47253%;\"\u003e\n \u003cp\u003e0.30**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.36264%;\"\u003e\n \u003cp\u003e0.20**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.02198%;\"\u003e\n \u003cp\u003e0.28**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e0.25**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e0.52**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e-0.46**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"910\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.7253%;\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.04396%;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.26374%;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.47253%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.36264%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.02198%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.7253%;\"\u003e\n \u003cp\u003e1.\u0026nbsp;Anxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.04396%;\"\u003e\n \u003cp\u003e5.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.26374%;\"\u003e\n \u003cp\u003e5.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.47253%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.36264%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.02198%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.7253%;\"\u003e\n \u003cp\u003e2.\u0026nbsp;Depression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.04396%;\"\u003e\n \u003cp\u003e6.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.26374%;\"\u003e\n \u003cp\u003e5.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e0.80**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.47253%;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.36264%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.02198%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.7253%;\"\u003e\n \u003cp\u003e3.\u0026nbsp;Perceived Stress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.04396%;\"\u003e\n \u003cp\u003e17.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.26374%;\"\u003e\n \u003cp\u003e6.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e0.66**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.47253%;\"\u003e\n \u003cp\u003e0.66**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.36264%;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.02198%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.7253%;\"\u003e\n \u003cp\u003e4.Insomnia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.04396%;\"\u003e\n \u003cp\u003e10.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.26374%;\"\u003e\n \u003cp\u003e6.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e0.60**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.47253%;\"\u003e\n \u003cp\u003e0.72**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.36264%;\"\u003e\n \u003cp\u003e0.55**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.02198%;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.7253%;\"\u003e\n \u003cp\u003e5.Intolerance of Uncertainty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.04396%;\"\u003e\n \u003cp\u003e35.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.26374%;\"\u003e\n \u003cp\u003e9.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e0.58**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.47253%;\"\u003e\n \u003cp\u003e0.56**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.36264%;\"\u003e\n \u003cp\u003e0.52**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.02198%;\"\u003e\n \u003cp\u003e0.44**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.7253%;\"\u003e\n \u003cp\u003e6.Job Burnout\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.04396%;\"\u003e\n \u003cp\u003e2.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.26374%;\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e0.68**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.47253%;\"\u003e\n \u003cp\u003e0.71**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.36264%;\"\u003e\n \u003cp\u003e0.69**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.02198%;\"\u003e\n \u003cp\u003e0.59**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e0.49**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.7253%;\"\u003e\n \u003cp\u003e7.Satisfaction with Life\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.04396%;\"\u003e\n \u003cp\u003e20.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.26374%;\"\u003e\n \u003cp\u003e6.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e-0.38**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.47253%;\"\u003e\n \u003cp\u003e-0.42**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.36264%;\"\u003e\n \u003cp\u003e-0.48**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.02198%;\"\u003e\n \u003cp\u003e-0.37**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e-0.31**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e-0.50**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.7253%;\"\u003e\n \u003cp\u003e8.Turnover Intention\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.04396%;\"\u003e\n \u003cp\u003e2.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.26374%;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.14286%;\"\u003e\n \u003cp\u003e0.29**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.47253%;\"\u003e\n \u003cp\u003e0.32**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.36264%;\"\u003e\n \u003cp\u003e0.32**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.02198%;\"\u003e\n \u003cp\u003e0.30**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e0.19**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e0.48**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e-0.37**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.24176%;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable3 Binary and multiple logistic regression analysis of the factors associated with turnover intention at T1 and T2\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"107%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 38%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 38%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrude OR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 8%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdj OR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrude OR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 8%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdj OR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e0.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e(0.130, 0.627)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e0.516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.208, 1.279)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e0.642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e(0.410, 1.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e0.749\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.438, 1.280)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e18-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e30-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e0.607\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e(0.461, 0.801)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e1.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.700, 2.475)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e0.768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e(0.624, 0.946)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e1.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.805, 1.854)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026gt;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e(0.126, 0.219)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e0.930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.391, 2.210)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e(0.190, 0.298)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e0.864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.472, 1.579)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eEducational level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003eTechnical secondary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003eJunior college\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e1.971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e(0.861, 4.510)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e0.818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.278, 2.403)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e2.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e(1.192, 3.378)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e1.348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.719, 2.528)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003eBachelor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e1.816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e(0.808, 4.082)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e0.916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.319, 2.629)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e1.549\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e(0.941, 2.552)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e1.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.644, 2.218)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003eMaster or higher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e1.556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e(0.447, 5.413)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e0.961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.181, 5.102)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e0.619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e(0.195, 1.967)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e0.680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.153, 3.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eYears of nursing work experience\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eLess than 1 year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e1-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e1.401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e(0.754, 2.604)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e1.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.460, 2.474)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e1.601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e(1.010, 2.537)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e1.362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.771, 2.407)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e6-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e1.664\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e(0.884, 3.132)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e0.948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.358, 2.508)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e1.737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e(1.097, 2.750)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e1.618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.851, 3.075)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e11-20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e0.577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e(0.316, 1.051)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e0.475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.154, 1.466)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e0.966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e(0.623, 1.500)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e0.975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.464, 2.050)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e21 or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e(0.110, 0.367)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e0.344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.095, 1.250)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e0.327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e(0.208, 0.515)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e0.741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.311, 1.767)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eIncome per year, CNY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026le;4999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e5000-9999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e0.786\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e(0.458, 1.348)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e0.953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.465, 1.953)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e1.291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e(0.881, 1.688)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e1.407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.933, 2.123)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e10000-14999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e0.653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e(0.382, 1.118)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e0.929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.437, 1.977)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e0.783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e(0.562, 1.089)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e1.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.672, 1.637)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e15000 or higher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e(0.118, 0.359)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e0.598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.260, 1.376)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e0.447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e(0.313, 0.640)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e1.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.747, 2.174)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eEmployment type\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003ePermanent staff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003eContract staff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5%;\"\u003e\n \u003cp\u003e4.377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e(3.394, 5.644)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4%;\"\u003e\n \u003cp\u003e1.441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.890, 2.333)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5%;\"\u003e\n \u003cp\u003e4.245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e(3.399, 5.301)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4%;\"\u003e\n \u003cp\u003e2.830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e(1.952, 4.101)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eProfessional titles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003ePrimary title\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eIntermediate title\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e0.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e(0.266, 0.423)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e0.848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.512, 1.406)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e0.522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e(0.434, 0.627)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e0.969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.671, 1.398)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eSenior title\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e0.123\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e(0.081, 0.186)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e0.859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.390, 1.895)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e(0.128, 0.255)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e1.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.552, 2.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eHierarchy\u0026nbsp;of nurse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003eN0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003eN1-N2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5%;\"\u003e\n \u003cp\u003e1.457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e(1.006, 2.111)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e1.535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.890, 2.646)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5%;\"\u003e\n \u003cp\u003e1.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e(0.960, 1.754)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e1.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.786, 1.763)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003eN3-N4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5%;\"\u003e\n \u003cp\u003e0.535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e(0.367, 0.779)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e2.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.998, 4.061)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5%;\"\u003e\n \u003cp\u003e0.659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e(0.486, 0.894)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e1.245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.743, 2.085)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003eN5-N6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5%;\"\u003e\n \u003cp\u003e0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e(0.126, 0.452)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e2.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.789, 6.421)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5%;\"\u003e\n \u003cp\u003e0.267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e(0.165, 0.434)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e1.268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.576, 2.793)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eJob duty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003eClinical nurse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003eAdministrative nurse and others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5%;\"\u003e\n \u003cp\u003e0.460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e(0.358, 0.591)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4%;\"\u003e\n \u003cp\u003e1.221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.824, 1.810)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5%;\"\u003e\n \u003cp\u003e0.582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e(0.497, 0.707)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4%;\"\u003e\n \u003cp\u003e1.861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e(1.358, 2.550)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e2.903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e(2.279, 3.698)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e1.240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.800, 1.921)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e2.209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e(1.818, 2.684)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e1.351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.977, 1.867)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eDivorced or widowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5%;\"\u003e\n \u003cp\u003e0.379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e(0.200, 0.719)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4%;\"\u003e\n \u003cp\u003e0.572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.250, 1.310)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5%;\"\u003e\n \u003cp\u003e0.509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e(0.319, 0.813)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4%;\"\u003e\n \u003cp\u003e0.708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.403, 1.245)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eNight shift per month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e1-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e2.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e(1.615, 2.720)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e1.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.744, 1.569)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e1.689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e(1.379, 2.068)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e1.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.805, 1.390)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e6-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e3.621\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e(2.694, 4.867)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e1.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.741, 1.798)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e2.750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e(2.161, 3.499)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e1.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.790, 1.520)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e11 or more\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e5.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e(3.369, 7.785)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e0.905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.489, 1.672)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e3.216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e(2.226, 4.648)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e1.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.697, 1.788)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eHospital type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eGeneral hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eSpecialist hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5%;\"\u003e\n \u003cp\u003e0.675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e(0.515, 0.885)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4%;\"\u003e\n \u003cp\u003e0.865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.605, 1.237)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5%;\"\u003e\n \u003cp\u003e1.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e(0.800, 1.265)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4%;\"\u003e\n \u003cp\u003e0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.747, 1.332)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eWorking unit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eSurgery\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eMedicine\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5%;\"\u003e\n \u003cp\u003e1.345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e(0.992, 1.847)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4%;\"\u003e\n \u003cp\u003e0.869\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.588, 1.286)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5%;\"\u003e\n \u003cp\u003e1.433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e(1.098, 1.869)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4%;\"\u003e\n \u003cp\u003e1.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.810, 1.518)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eEmergency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e0.680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e(0.404, 1.143)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e0.566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.288, 1.113)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e0.948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e(0.638, 1.409)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e0.805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.500, 1.296)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eIntensive care unit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5%;\"\u003e\n \u003cp\u003e2.770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e(1.166, 6.584)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4%;\"\u003e\n \u003cp\u003e0.563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.190, 1.666)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5%;\"\u003e\n \u003cp\u003e1.331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e(0.794, 2.232)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4%;\"\u003e\n \u003cp\u003e0.920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.489, 1.732)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eOut-patient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e0.490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e(0.356, 0.677)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e0.890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.562, 1.409)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e0.579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e(0.436, 0.767)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e0.819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.561, 1.195)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e0.632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e(0.476, 0.840)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e1.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.709, 1.503)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e0.622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e(0.497, 0.777)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e0.755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.575, 0.992)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eJob satisfaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eSatisfied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eAverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e6.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e(4.661, 8.142)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e2.741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(1.950, 3.852)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e4.638\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e(3.667, 5.867)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e2.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(1.635, 2.852)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003eNot satisfied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5%;\"\u003e\n \u003cp\u003e21.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e(11.370, 39.077)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4%;\"\u003e\n \u003cp\u003e6.536\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e(3.129, 13.653)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5%;\"\u003e\n \u003cp\u003e9.548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e(5.148, 17.709)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4%;\"\u003e\n \u003cp\u003e3.598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e(1.783, 7.263)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eLack of interest in nursing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5%;\"\u003e\n \u003cp\u003e5.835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e(4.639, 7.340)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4%;\"\u003e\n \u003cp\u003e2.802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e(2.100, 3.740)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5%;\"\u003e\n \u003cp\u003e4.329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e(3.606, 5.197)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4%;\"\u003e\n \u003cp\u003e2.513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e(2.014, 3.136)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eYour attitude towards the current pandemic management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eFeel nothing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eNormal mindset\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e0.491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e(0.244, 0.990)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e1.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.486, 2.856)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e0.527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e(0.380, 0.732)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e1.265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.844, 1.896)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eMildly nervous, worried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5%;\"\u003e\n \u003cp\u003e0.660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e(0.323, 1.347)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4%;\"\u003e\n \u003cp\u003e1.214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.492, 2.994)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5%;\"\u003e\n \u003cp\u003e0.836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e(0.557, 1.255)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4%;\"\u003e\n \u003cp\u003e1.342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.823, 2.188)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eVery panic, anxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5%;\"\u003e\n \u003cp\u003e0.928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e(0.445, 1.932)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4%;\"\u003e\n \u003cp\u003e0.765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.299, 1.959)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5%;\"\u003e\n \u003cp\u003e0.945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e(0.496, 1.801)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4%;\"\u003e\n \u003cp\u003e0.722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.342, 1.527)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003ePSS-10\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5%;\"\u003e\n \u003cp\u003e1.508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e(1.163, 1.954)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4%;\"\u003e\n \u003cp\u003e1.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.826, 1.629)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5%;\"\u003e\n \u003cp\u003e2.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e(1.620, 2.518)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4%;\"\u003e\n \u003cp\u003e0.954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.720, 1.264)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5%;\"\u003e\n \u003cp\u003e2.408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e(1.734, 3.344)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4%;\"\u003e\n \u003cp\u003e1.509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.961, 2.371)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5%;\"\u003e\n \u003cp\u003e3.540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e(2.741, 4.571)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4%;\"\u003e\n \u003cp\u003e0.882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.616, 1.263)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5%;\"\u003e\n \u003cp\u003e2.627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e(1.873, 3.685)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4%;\"\u003e\n \u003cp\u003e1.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.599, 1.684)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5%;\"\u003e\n \u003cp\u003e4.657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e(3.576, 6.065)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4%;\"\u003e\n \u003cp\u003e0.740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.477, 1.148)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eGAD-7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5%;\"\u003e\n \u003cp\u003e2.435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e(1.903, 3.117)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4%;\"\u003e\n \u003cp\u003e1.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.771, 1.657)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5%;\"\u003e\n \u003cp\u003e2.718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e(2.220, 3.329)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4%;\"\u003e\n \u003cp\u003e1.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.867, 1.605)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5%;\"\u003e\n \u003cp\u003e2.761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e(1.766, 4.317)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4%;\"\u003e\n \u003cp\u003e1.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.561, 2.184)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5%;\"\u003e\n \u003cp\u003e2.967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e(2.169, 4.060)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4%;\"\u003e\n \u003cp\u003e0.653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.387, 1.102)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5%;\"\u003e\n \u003cp\u003e3.362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e(2.404, 4.700)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4%;\"\u003e\n \u003cp\u003e0.970\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.496, 1.899)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5%;\"\u003e\n \u003cp\u003e2.890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e(1.782, 4.688)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4%;\"\u003e\n \u003cp\u003e0.577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.276, 1.207)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003ePHQ-9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e2.588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e(1.984, 3.376)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e1.405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.943, 2.093)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e2.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e(1.716, 2.684)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e1.334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(1.087, 1.803)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e3.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e(2.217, 4.159)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e0.789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.472, 1.348)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e4.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e(3.248, 5.399)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e1.754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(1.155, 2.663)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e3.583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e(2.566, 5.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e0.590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.300, 1.160)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e5.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e(3.916, 6.931)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e1.540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.885, 2.681)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eISI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e2.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e(1.684, 2.809)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e1.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.801, 1.629)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e1.912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e(1.527, 2.394)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e1.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.757, 1.361)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e3.328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e(2.440, 4.539)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e1.426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.888, 2.291)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e2.874\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e(2.254, 3.664)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e0.960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.683, 1.350)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e4.260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e(2.668, 6.800)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e1.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.498, 2.030)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e3.692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e(2.849, 4.784)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e0.795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.527, 1.200)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eMBI-GS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e2.979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e(2.258, 3.930)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e1.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(1.051, 1.945)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e2.412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e(1.931, 3.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e1.787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(1.357, 2.354)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e8.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e(5.792, 11.222)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e2.885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(1.817, 4.581)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e5.848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e(4.507, 7.586)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e3.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(2.170, 4.540)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5%;\"\u003e\n \u003cp\u003e14.855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e(9.984, 22.104)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4%;\"\u003e\n \u003cp\u003e4.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e(2.095, 8.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5%;\"\u003e\n \u003cp\u003e10.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e(7.578, 13.757)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4%;\"\u003e\n \u003cp\u003e3.768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e(2.333, 6.088)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eIUS-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e1.829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e(1.383, 2.417)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e0.899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.619, 1.304)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e1.624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e(1.296, 2.035)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e0.951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.716, 1.264)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e2.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e(1.561, 2.823)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e0.772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.511, 1.167)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e2.245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e(1.755, 2.873)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e1.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.752, 1.440)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e2.942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e(2.146, 4.033)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e0.683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.415, 1.121)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e2.597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e(2.014, 3.348)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e0.877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.611, 1.260)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eSWLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e0.377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e(0.256, 0.555)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e0.579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.368, 0.912)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e0.592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e(0.441, 0.794)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e0.683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.486, 0.960)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e(0.147, 0.344)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e0.549\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.328, 0.921)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e0.327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e(0.244, 0.439)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e0.605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.427, 0.856)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e(0.061, 0.133）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e0.343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.213, 0.553)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5%;\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e(0.094, 0.172)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4%;\"\u003e\n \u003cp\u003e0.395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e(0.271, 0.576)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: ***\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001, **\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01, *\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05. PSS-10 represents the total score of the Perceived Stress Scale. Q1 represents the first quartile; Q2 represents the second quartile; Q3 represents the third quartile; and Q4 represents the fourth quartile.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-nursing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurs","sideBox":"Learn more about [BMC Nursing](http://bmcnurs.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurs/default.aspx","title":"BMC Nursing","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Turnover intention, Job burnout, Risk factors, Prevalence, Nurses, full liberalization of COVID−19, post-pandemic era","lastPublishedDoi":"10.21203/rs.3.rs-5257180/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5257180/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe COVID\u0026minus;19 pandemic was a major public health crisis, which has exacerbated the difficulties nurses face, resulting in higher turnover rates and workforce shortages. While many early studies that have examined factors contributing to turnover intention, surprisingly, as yet, no studies have compared the turnover intention of Chinese hospital nurses during the full liberalization of COVID\u0026minus;19 period and post-pandemic era, and it is unclear which potential factors may be associated with turnover intention of nurses at the different periods. This 2-wave repeated survey purposed to explore the prevalence and correlates of turnover intention at different stages of the full liberalization of COVID\u0026minus;19 and post-pandemic era in a large sample of nurses in China.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eUsing a repeated cross-sectional survey design, we conducted two online surveys at 25 hospitals in Guandong, China. The 2 surveys were conducted during the full liberalization of COVID\u0026minus;19 period (T1: 27 December 2022 to 7 January 2023, N\u0026thinsp;=\u0026thinsp;1,766), and post-pandemic era (T2: 11 May to 23 May 2023, N\u0026thinsp;=\u0026thinsp;2,643). Turnover intention was measured by the six-item Turnover Intention Scale (TIS). A range of turnover intention-related factors was assessed, including sociodemographic characteristics, preceived stress, anxiety, depression, insomnia, job burnout, intolerance of uncertainty, satisfaction with life, and work-related factors.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe prevalence of turnover intention were 73.33% and 72.34% at T1 and T2, respectively. Dissatisfaction with nursing work (\u003cem\u003eaOR\u003c/em\u003e: 2.160\u0026ndash;6.536, \u003cem\u003ePs\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), lack of interest in nursing (\u003cem\u003eaOR\u003c/em\u003e: 2.513\u0026ndash;2.802, \u003cem\u003ePs\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and job burnout (\u003cem\u003eaOR\u003c/em\u003e: 1.360\u0026ndash;4.096, \u003cem\u003ePs\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) were associated with an increased risk of turnover intention. And satisfaction with life (\u003cem\u003eaOR\u003c/em\u003e: 0.343\u0026ndash;0.683, \u003cem\u003ePs\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) was associated with a reduced risk of turnover intention.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eTurnover intention were particularly higher both in the full liberalization of COVID\u0026minus;19 period and the post-pandemic era. Multiple factors, especially dissatisfaction with nursing work, lack of interest in nursing, job burnout and satisfaction with life are associated with turnover intention. Early detection of turnover intention among hospital nurses and preventive and promotive interventions should be implemented during the full liberalization of COVID\u0026minus;19 and the post-pandemic era to reduce turnover intention among nurses.\u003c/p\u003e","manuscriptTitle":"What is behind high turnover intention among hospital nurses during the full liberalization of COVID-19 and post-pandemic era in China: a 2-wave repeated multicenter survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-13 15:56:10","doi":"10.21203/rs.3.rs-5257180/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-04T06:51:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-01T05:11:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-01T05:09:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nursing","date":"2024-10-14T01:02:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nursing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurs","sideBox":"Learn more about [BMC Nursing](http://bmcnurs.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurs/default.aspx","title":"BMC Nursing","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"65b845bc-dfee-4a66-98fd-97f8746cbddb","owner":[],"postedDate":"November 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-03T15:59:11+00:00","versionOfRecord":{"articleIdentity":"rs-5257180","link":"https://doi.org/10.1186/s12912-025-02851-1","journal":{"identity":"bmc-nursing","isVorOnly":false,"title":"BMC Nursing"},"publishedOn":"2025-02-25 15:57:01","publishedOnDateReadable":"February 25th, 2025"},"versionCreatedAt":"2024-11-13 15:56:10","video":"","vorDoi":"10.1186/s12912-025-02851-1","vorDoiUrl":"https://doi.org/10.1186/s12912-025-02851-1","workflowStages":[]},"version":"v1","identity":"rs-5257180","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5257180","identity":"rs-5257180","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}