Mindful Self-Care among Oncology Nurses in China: a Latent Profile Analysis

Mindful Self-Care among Oncology Nurses in China: a Latent Profile Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Mindful Self-Care among Oncology Nurses in China: a Latent Profile Analysis Yan Shi, Peng Wang, Lamei Liu, Mengmeng Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3956160/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background Oncology nurses are considered the group with the highest risk for moral distress, compassion fatigue and burnout. Mindful self-care may help oncology nurses improve their well-being and solve psychological problems and burdens. It is important to understand oncology nurses’ mindful self-care. Objectives To investigate the situation, possible types and influencing factors of mindful self-care among oncology nurses. Design Cross-sectional descriptive study. Participants A total of 839 oncology nurses were enrolled in this survey. Methods From January to May 2023, a cross-sectional study was carried out among oncology nurses using convenient sampling. The subjects were given the brief Mindful Self-Care Scale (B-MSCS) and the General Demographic Information Questionnaire. Latent profile analysis was used to separate oncology nurses’ mindful self-care into a variety of subgroups. The SPSS 25.0 statistical program was used to analyze the data. One-way ANOVA and the chi-square test were performed to compare the score of each B-MSCS dimension in each class and the difference in sociodemographic characteristics among the subgroups. Multivariate logistic regression was used to examine the influence of the sociodemographic variables on each profile. Results The total score of the B-MSCS was 76.40 ± 13.19. The support structure dimension had the highest score, with an average mean value of 3.60, and physical care had the lowest score at 2.57. The findings of the latent profile analysis showed that respondents were divided into three profiles, low (n = 124), moderate (n = 430), and high mindful self-care (n = 285), which accounted for 14.8%, 51.2%, and 34.0%, respectively, of the total respondents. Across scale scores and dimensions, three groups demonstrated statistically significant differences ( p < 0.05). Univariate analysis revealed significant differences between the three profiles in terms of professional title, position, concern about self-care, interest in mindfulness, and experience with meditation ( p < 0.05). Profile membership was predicted by 3 factors, namely, self-care status, interest in mindfulness, and experience with meditation. Conclusion The mindful self-care ability of oncology nurses, especially mindful relaxation and physical activities, should be improved. Three latent mindful self-care profiles were found, and nursing managers should pay more attention to oncology nurses in low-mindful self-care groups. Further interventions combined with mindfulness, self-care or meditation can be performed to improve the mindful self-care ability of oncology nurses. Mindful self-care Chinese Oncology Nurses Latent profile analysis Influencing factors Figures Figure 1 Introduction The incidence and mortality of cancer have been gradually increasing in China [ 1 ] . This increasing burden of cancer poses a serious threat to public health and the economy [ 2 ] . This also requires oncology nurses to provide higher quality health care [ 3 ] . The work environment and mental health status of oncology nurses should receive increased attention. Research shows that oncology nurses are considered the group with the highest risk for moral distress [ 4 ] , compassion fatigue (CF) and burnout [ 5 ] . Moreover, these individuals often face patient deaths, which can create painful experiences and emotional burdens [ 6 ] . Moreover, oncology nurses are expected to be intensely empathic, and they must listen to sad and despairing stories but display or suppress their emotions intermittently [ 7 ] . Many nurses, particularly novice ones, are inadequately prepared to care for end-of-life patients and their families [ 8 ] . Nurses also face challenges, such as getting emotionally attached to patients and difficulty in separating work from their personal life [ 9 ] . Research has also suggested that terminal care, death anxiety, a lack of social support, and ethical issues related to patient care are work-related stressors that oncology nurses face [ 10 – 15 ] . If these stressors persist, they can negatively affect oncology nurses. These stressors can affect nurses’ psychological status and job satisfaction [ 16 ] and create job stress [ 10 ] , burnout [ 17 ] , compassion fatigue [ 18 ] , and disinterest in oncology nursing [ 19 ] . Oncology nurses have no time to rest and experience stress [ 20 ] and low levels of personal accomplishment in this busy work environment [ 21 ] . Moreover, studies have shown that burnout can cause irritability, sleeplessness, and fatigue and, consequently, alcohol and drug consumption, [ 17 ] which affect oncology nurses’ caring behaviors [ 22 ] . Burnout and job stress can increase the risk of oncology nurses failing to recognize patient distress [ 23 – 25 ] , impeding the provision of empathy-based care [ 19 ] , which is very important for oncology nurses to have effective communication [ 26 ] . Therefore, oncology nurses must know how to mentally remove themselves from tragic moments and the emotional devastation surrounding them [ 7 ] . Oncology nurses should develop the experience, skills, social support and control [ 27 ] needed to manage their psychological health and work-related stress [ 8 ] ; maintain good boundaries; and be fully present during care [ 28 ] . Resilience measures such as engaging in self-care activities, practicing mindfulness, and having positive self-talks may reduce the effects of occupational burnout [ 29 ] . Maintaining self-awareness and self-monitoring are useful coping skills [ 24 ] . Research on hospice care professionals has shown that those who frequently use self-care strategies experience a higher professional quality of life [ 30 ] . Mindfulness is also a form of self-care [ 28 ] in which a defensive style can also be used by oncology professionals to support a positive response to psychological distress [ 31 ] . Mindful self-care is an integration of mindfulness and traditional self-care practices [ 32 ] that involves mindful self-awareness and assessment of one’s internal and external needs. By developing mindful self-care abilities, oncology nurses can manage their well-being [ 28 ] and solve the psychological problems and burdens that work stress can cause . To measure informal mindful self-care abilities and promote positive embodiment and well-being, Cook-Cottone and Hotchkiss developed and validated Mindful Self-Care Scale in hospice and health care professionals [ 28 ] . Yang Z. [ 33 ] has translated and tested the psychometric properties of the B-MSCS among Chinese hospice nurses. However, the status of mindful self-care among oncology nurses in China is still unclear, and in-depth analysis of mindful self-care is lacking. LPA is a categorical latent variable modeling approach that focuses on identifying latent subpopulations within a population based on a certain set of variables [ 34 ] . Compared to traditional, nonlatent clustering methods, LPA can classify individuals into clusters based on membership probabilities estimated directly from the model and demographics, and other covariates can be used for profile description. It has been widely applied in psychology and humanities research to identify types of people who have divergent personal attribute profiles [ 35 ] . This research used LPA to better understand the heterogeneity of oncology nurses’ mindful self-care and demographic differences related to mindful self-care, which will help us identify areas of strengths and weaknesses in oncology nurses’ mindful self-care abilities and develop improvement strategies. Accordingly, this study aimed to (1) identify heterogeneity and homogeneous groups of oncology nurses on mindful self-care ability based on latent profile analysis and 2) examine the sociodemographic correlation of these profiles. Methods Study Design This study was a cross-sectional analysis. This study was designed and reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. Settings The study was conducted in the oncology department of Henan Province, which includes two affiliated comprehensive hospitals and one affiliated oncology hospital. Participants The purposive sampling method was used to collect the data. An inclusion criterion for this study was that all participants were required to have worked in the oncology department for more than 2 years. Nurses who were reluctant to participate in the study were excluded. Data collection This research collected data from January and May 2023. Questionnaires were distributed to participants using an online platform. Before completing the questionnaire, each participant was provided informed consent. Participants were asked to rate all items in the questionnaire. Only one questionnaire could be completed per IP address. Sample size LPA requires a sample size greater than 500 [ 36 ] ; therefore, the sample size for this study was at least 500. Data collection tools Demographic Questionnaire (DQ) The questionnaire included several questions to collect data on participants’ age, sex, education level, marital status, professional title, years of being in the profession, and so on. The Brief Mindful Self-Care Scale The B-MSCS is a 24-item scale that measures the self-reported frequency of self-care behaviors using Likert-type scale response anchors (1= “never”; 5= “always”) [ 37 ] . This scale was the result of exploratory and confirmatory factor analyses with large samples. The subscales of the B-MSCS include physical care (PC), supportive relationships (SR), mindful self-awareness (MA), self-compassion and purpose (SCP), mindful relaxation (MR), and support structure (SS). The Cronbach’s αs for the subscales were 0.77, 0.77, 0.86, 0.78, 0.74 and 0.79. The B-MSCS total scale and subscale have strong internal consistency and reliability [ 28 ] . Yang [ 33 ] translated the scale into Chinese and validated its reliability and validity among hospice nurses. The Cronbach’s α value of the Chinese version of the B-MSCS was 0.920, and the Cronbach’s α value of the dimensions ranged from 0.850 to 0.933. In this research, the Cronbach’s α coefficient of the total scale was 0.925, ranging from 0.698 to 0.942. Statistical analyses All the statistical analyses were performed using IBM SPSS Statistics 25.0. Descriptive statistics were conducted to summarize participants’ demographic and clinical characteristics and scores on the scales, including the numbers, frequencies, means and standard deviations (SDs). The results of the 24 items on the B-MSCS served as exogenous variables. Because the B-MSCS scores were continuous variables, latent profile analysis (LPA) was carried out using the Mplus 7.4 program. The following fit indicators were used to determine the optimal number of latent profiles: Log Likelihood (LL), Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and adjusted Bayesian Information Criterion (aBIC) were used to compare models, with lower AIC, BIC, and aBIC values indicating a better model fit. The Lo-Mendell-Rubin (LMR) and Bootstrapped Likelihood Ratio Test (BLRT) were used to determine whether a k-class model fit better than a model with k-1 classes, and a significant p value indicated that the k class was better. The entropy values were assessed to determine the classification precision of each model, with entropy values greater than 0.80 indicating adequate classification precision. The minimum percentage of potential subgroups should not be less than 5%. The fitting results of each class and the needs of the researchers were considered together to determine which was the best model. The SPSS 25.0 statistical program was used to analyze the data. Frequency and composition ratios were used to describe the categorical variables. The mean and standard deviation were used to describe the continuous variables. One-way ANOVA was used to compare the scores on each B-MSCS dimension in each profile. When significant between-group effects were observed, and post hoc analyses were performed using the Tukey HSD method. One-way ANOVA and the chi-square test were performed to compare differences in sociodemographic characteristics among the subgroups. Multivariate logistic regression was subsequently performed to examine the influence of the sociodemographic variables on each profile. A p value < 0.05 indicated statistical significance for all analyses. Ethical considerations The study was conducted in accordance with the Declaration of Helsinki. Informed consent was obtained from all participants before their enrollment in the study. The study received the approval of the Ethics Review Committee of the School of Nursing and Health, Zhengzhou University (approval number 2022 − 131). Results Participants’ characteristics Most of the respondents were female (98.8%) and aged less than 40 (94.8%). A total of 91.4% of them had no religious beliefs. Over half of the respondents (68.9%) were married and had a bachelor’s degree (53.8%). Table 1 shows the demographic profiles of the participants. Table 1 Demographic profiles of the participants (n = 839) Variables Mean(SD) n(%) Gender Male 10(1.2) Female 829(98.8) Age 31.24(5.54) Marital status Married 578(68.9) Unmarried 252(30.0) Separated/divorced 9(1.1) Education Level Bachelor degree 451(53.8) Master degree 22(2.6) Doctor degree 1(0.1) Without degree 365(43.5) Professional title Nurse 131(15.6) Nurse-in-charge 688(82.0) Co-Chief Nurse 18(2.1) Chief Nurse 2(0.2) Position Nurse 762(90.8) Head nurse 54(6.4) Deputy Director of Nursing 4(0.5) Director of Nursing 3(0.4) Others 16(1.9) Years of being in the profession 2–5 191(22.8) 6–10 354(42.2) 11–15 294(35.0) Income per month(CNY) 1000–3000 47(5.6) 3001–5000 64(7.6) 5001–7000 134(16.0) 7001–9000 323(38.5) Above 9000 271(32.3) Be concerned about self-care Yes 503(60.0) No 336(40.0) Be interested in mindfulness Yes 455(47.7) No 384(52.3) Having experience of Meditation Yes 400(47.7) No 439(52.3) The dimensions of the level of mindful self-care of oncology nurses The total score of the B-MSCS was 76.40 ± 13.19. The SS dimension had the highest score, with an average mean value of 3.60, followed by the MA, SCP, SR, MR and PC dimensions. PC had the lowest score at 2.57. Table 2 shows the details. Table 2 The level of mindful self-care of oncology nurses Variables Rank Number of the items Average Score of item of B-MSCS in oncology nurses (x̄±s) Support structure(SS) 1st 4 3.60 ± 0.69 Mindfulness self-awareness(MA) 2nd 3 3.51 ± 0.79 Self-compassion and purpose(SCP) 3rd 4 3.41 ± 0.72 Supportive relationship(SR) 4th 4 3.31 ± 0.77 Mindful relaxation(MR) 5th 4 2.93 ± 0.79 Physical care(PC) 6th 5 2.57 ± 0.66 Characteristics of the different profiles Table 3 shows the model fit indices for the one-class to four-class solutions. The LL, AIC, BIC, and aBIC generally decreased as the number of estimated profiles increased, while the entropy remained above 0.90 consistently. The LMR tests were significant except for Model 4, which showed that the Model 4 solution did not enhance the model fit considerably compared with that of the Model 3 solution ( p = 0.1142). The BLRT results were all significant. Model 3, which included three potential profiles, was determined to be the model with the best fit indices when the models’ fit indices were evaluated. Based on the results of the latent profile analysis, the scores of the three profiles on the 24 items of the B-MSCS were plotted, and their characteristics are summarized in Fig. 1 . Profile 1, profile 2, and profile 3 were designated in accordance with their distinctive distributions based on the mean scores of the MSCs in each profile. Profile 1 scored between profile 2 and profile 3; 51.2% (n = 430) of the subjects scored this profile as “moderate-level” based on its score characteristics. Profile 2 scored significantly lower than profiles 1 and 3, containing 14.8% (n = 124) of the subjects; this profile was named “low level” based on its score characteristics. Profile 3 scored significantly higher than profile 1 and profile 2, containing 34.0% (n = 285) of the subjects; this profile was named “high-level” based on its score characteristics. The latent profile memberships exhibited significant differences in the means of the six indicator variables ( p < 0.001). A post hoc analysis using the Tukey HSD method revealed that oncology nurses in the high MSC group scored higher than those in the moderate and low MSC groups on both the total scale score and each dimension. The moderate MSC group also scored higher than the low MSC group. The score for each item also showed the same trend ( p <0.05) except for Item 4. The score for Item 4 in profile 3 (3.36 ± 1.075) was significantly higher than that in profile 1 (3.17 ± 1.005), but there were no significant differences between profile 3 and profile 2 (3.37 ± 1.063) or between profile 2 and profile 1. Table 4 presents the outcomes. Table 3 Potential profile analysis indicators (n = 839) Model LL a AIC b BIC c aBIC d Entropy LMR e p value BLRT f p value Category probability (%) Model 1 -26420.678 52937.356 53164.502 53012.070 Model 2 -23806.502 47759.004 48104.456 47872.632 0.929 <0.001 <0.001 57.2/42.8 Model 3 -23078.718 46353.436 46817.193 46505.977 0.930 <0.001 <0.001 51.2/14.8/34.0 Model 4 -22585.191 45416.382 45998.444 45607.836 0.935 0.1142 <0.001 9.6/38.1/42.3/10.0 Note. a Log likelihood, b Akaike information criterion, c Bayesian information criterion, d Adjusted Bayesian information criterion, e Lo-Mendell-Rubin likelihood ratio test, f Bootstrapped likelihood ratio test. The boldface text indicates the selected model. Table 4 B-MSCS scores and dimensions in different categories (n = 839) Variables Profile 1 Mean(SD) Profile 2 Mean(SD) Profile 3 Mean(SD) F p Physical care(PC) 12.16(2.58) 10.98(2.55) 14.77(3.63) 95.331 <0.001 Supportive relationship(SR) 12.62(2.09) 9.27(2.27) 15.85(2.26) 428.447 <0.001 Mindfulness self-awareness(MA) 10.01(1.53) 7.44(1.64) 12.68(1.58) 539.343 <0.001 Self-compassion and purpose(SC) 12.90(1.66) 9.98(2.08) 16.48(1.81) 673.466 <0.001 Mindful relaxation(MR) 11.01(2.27) 8.54(2.33) 14.21(2.78) 267.002 <0.001 Support structure(SS) 13.83(1.90) 11.01(2.31) 16.78(1.84) 416.494 <0.001 Total score of B-MSCS 72.43(5.26) 57.21(6.90) 90.76(7.72) 1341.827 <0.001 Demographic and related characteristics of each profile Table 5 showed the results of the univariate analysis which revealed that significant differences between the three profiles regarding professional title( = 14.820, p = 0.022), position( = 19.990, p = 0.010), whether concerned about self-care( = 30.430, p <0.001), whether interested in mindfulness( = 32.691, p <0.001), and experience of meditation( = 23.814, p <0.001). A multinomial logistic regression analysis was carried out using profile 3 as the reference group to pinpoint the variables connected to MSC among the three profiles. The professional title and position were converted to binary to avoid floating-point overflow. The outcomes are displayed in Table 6 . The findings showed that when compared to those who were not concerned about self-care, oncology nurses concerned about self-care had lower odds of being in the moderate MSC group and low MSC group than in the high MSC group (OR: 0.643, CI: 0.446–0.928 and OR: 0.558, CI: 0.340–0.916, p <0.05). Compared to those who were not interested in mindfulness, oncology nurses interested in mindfulness had lower odds of being in the moderate MSC group and low MSC group (OR: 0.690, CI: 0.480–0.993 and OR: 0.536, CI: 0.324–0.888, p <0.05). Compared to those who were not experienced with meditation, oncology nurses with meditation experience had lower odds of being in the moderate MSC group and low MSC group than in the high MSC group (OR: 0.715, CI: 0.515–0.993, p <0.05; OR: 0.521, CI: 0.325–0.834, p <0.01). Table 5 Demographics and characteristics by latent profile (n = 839) Variables Profile 1 Profile 2 Profile 3 χ2/F p Gender 0.354 0.838 Female 424(98.6%) 123(99.2%) 282(98.9%) Male 6(1.4%) 1(0.8%) 3(1.1) Age 30.98(5.19) 30.77(4.00) 31.83(6.53) 2.510 0.082 Marital status 0.985 0.912 Married 297(69.1%) 86(69.4%) 195(68.4%) Unmarried 127(29.5%) 37(29.8%) 88(30.9%) Separated/divorced 6(1.4%) 1(0.8%) 2(0.7%) Education Level 8.347 0.214 Bachelor degree 230(53.5%) 63(50.8%) 158(55.4%) Master degree 10(2.3%) 2(1.6%) 10(3.5%) Doctor degree 0(0) 1(0.8%) 0(0) Without degree 190(44.2%) 58(46.8%) 117(41.1%) Professional title 14.820 0.022 Nurse 77(17.9%) 17(13.7%) 37(13.0%) Nurse-in-charge 345(80.2%) 107(86.3%) 236(82.8%) Co-Chief Nurse 6(1.4%) 0(0) 12(4.2%) Chief Nurse 2(0.5%) 0(0) 0(0) Position 19.990 0.010 Nurse 398(92.6%) 120(96.8%) 244(85.6%) Head nurse 20(4.7%) 3(2.4%) 31(10.9%) Deputy Director of Nursing 2(0.5%) 1(0.8%) 1(0.4%) Director of Nursing 2(0.5%) 0(0) 1(0.4%) Others 8(1.9%) 0(0) 8(2.8%) Years of being in the profession 7.558 0.109 2–5 105(24.4%) 21(16.9%) 65(22.8%) 6–10 180(41.9%) 64(51.6%) 110(38.6%) 11–15 145(33.7%) 39(31.5%) 110(38.6%) Income per month(CNY) 4.234 0.835 1000–3000 26(6.0%) 6(4.8%) 15(5.3%) 3001–5000 32(7.4%) 9(7.3%) 23(8.1%) 5001–7000 73(17.0%) 17(13.7%) 44(15.4%) 7001–9000 164(38.1%) 56(45.2%) 103(36.1%) Above 9000 135(31.4%) 36(29.0%) 100(35.1%) Concern about self-care 30.432 <0.001 Yes 239(55.6%) 58(46.8%) 206(72.3%) No 191(44.4%) 66(53.2%) 79(27.7%) Interest in mindfulness 32.691 <0.001 Yes 215(50%) 49(39.5%) 191(67.0%) No 215(50%) 75(60.5%) 94(33.0%) Experience with meditation 23.814 <0.001 Yes 192(44.7%) 42(33.9%) 166(58.2%) No 238(55.3%) 82(66.1%) 119(41.8%) Table 6 Predictors of latent profile membership Variables Profile 1 vs. Profile 3 Profile 2 vs. Profile 3 β OR 95%CL β OR 95%CL Age 0.009 1.010 0.975–1.045 -0.005 0.995 0.945–1.048 Professional title Nurse 0.374 1.454 0.891–2.371 -0.045 0.956 0.472–1.934 Nurse-in-charge and above Position Nurse 0.648 1.912 0.991–3.668 0.920 2.508 0.780–8.064 Head nurse and above Concern about self-care Yes -0.441 0.643* 0.446–0.928 -0.584 0.558* 0.340–0.916 No Interest in mindfulness Yes -0.371 0.690* 0.480–0.993 -0.623 0.536* 0.324–0.888 No Experience with meditation Yes -0.335 0.715* 0.515–0.993 -0.652 0.521** 0.325–0.834 No Note. OR, odds ratio; 95% CI, 95% confidence interval; Ref, reference * p <0.05 ** p <0.01 Discussion As a form of self-care, mindfulness can be used among oncology professionals to support a positive response to psychological distress [ 28 ] . To improve mental health through mindful self-care, we first need to understand the mental health status among oncology nurses. The purpose of our study was to divide oncology nurses into subgroups of mindful self-care and explore the factors associated with mindful self-care. First, to determine the factors influencing mindful self-care ability among oncology nurses, we analyzed the score of B-MSCS. The present study showed that the B-MSCS score of oncology nurses was higher than that of hospice professionals [ 33 ] . The rank of each dimension also differed, which also showed differences between the two populations and the heterogeneity of the oncology nurses. The support structure (SS) dimension emphasizes environmental factors, which include keeping work areas organized, constructing a manageable schedule and maintaining a pleasing and comfortable living environment [ 37 ] . Among oncology nurses, the score of this dimension ranks first, possibly because cancer treatment is currently generally standardized and process oriented and because oncology nursing is often organized, which also influences daily life. The Mindful Relaxation(MR) and Physical care(PC) scores were not high among the oncology nurses. MR can be described as a technique for self-soothing, calming, and relaxation, and these are believed to be effective tools in emotional regulation [ 37 ] . Relaxation has been linked negatively to sleeping problems, emotional exhaustion and need for recovery and positively to work performance and job satisfaction [ 38 ] . These results may be attributed to the fact that Chinese oncology nurses are always busy and have little time to relax. Chinese oncology nurses should learn new strategies on how to relax and be aware of when they should relax. PC emphasizes on basic nutrition, hydration, and exercise practices [ 37 ] , which are related to people’s mood and reduce stress [ 39 ] . Physical inactivity leads to a range of adverse health consequences [ 40 ] . Oncology nurses should realize the importance of physical activity and participate in exercise, sports, mind-body practice and other activities. We used the LPA technique to group oncology nurses by their level of mindful self-care ability and identified three distinct profiles—the moderate MSC group, high MSC group, and low MSC group—for which the average scores were 57.21, 72.43, and 90.76, respectively. The largest proportion (51.25%) of oncology nurses were in the moderate MSC group, and 14.79% of oncology nurses were in the low MSC group. These results revealed that the mindful self-care ability of oncology nurses still needs to be developed. For Item 3, ‘I regularly took part in sports, dance or other activities’, and Item 5, ‘I practiced yoga or another mind-body practice’, the scores were relatively low for each item. Yoga practice integrates bottom-up neurophysiological and top-down neurocognitive mechanisms and can help individuals achieve self-regulation and resilience, leading to improved well-being [ 41 ] . Yoga will also be useful to oncology nurses for improving well-being. For Item 2, ‘I exercised at least 30 to 60 min’ was not very low because the word ‘exercise’ may include walking and running in the workplace, which oncology nurses do every day. This can also explain why Item 4, ‘I did sedentary activities instead of exercising’, which is a reverse scoring question, was highly and closely related to each other in different profiles. The scores for Item 17, ‘I relaxed by doing some creative or intellectual activities’, and Item 20, ‘I relaxed through my sense of smell’, were also relatively low. These two items are in the same dimension, Mindful Relaxation (MR). This result further illustrated that in this dimension, most oncology nurses cannot relax through creative or intellectual activities such as doing art activities, playing musical instruments, doing creative writing, singing or cleaning. As we know, music intervention and playing music can be a cost-effective resource for reducing stress and improving the well-being of healthy people and oncology nurses [ 42 – 44 ] . Research has also shown that storytelling through music can address work-related emotions and psychosocial stress in oncology nurses [ 45 ] . Additionally, mindful cleaning, such as organizing drawers, washing dishes, or mopping floors, can allow people to experience greater state mindfulness [ 46 ] , increase people’s mood and reduce their anxiety [ 47 ] . Smells, including using essential oils, being exposed to nature, lighting candles, and baking, can affect mood and psychiatric disorders, such as depression and anxiety, and can improve people’s connection to nature and well-being [ 48 ] . In the future, mindfulness self-care interventions for oncology nurses could focus on these aspects. Our research also revealed that the three profiles differed in terms of their social characteristics, including professional title and position. However, these characteristics were not the influencing factors for the different profiles according to the results of the multinomial logistic regression analysis. However, whether oncology nurses concern about self-care, interest in mindfulness and have experience with meditation influences the profile of mindful self-care. This may be because oncology nurses who have such characteristics want to change their situation through mindfulness, self-care and mediation. These results indicated that oncology nurses need to self-regulate, which is the same result as that from the research of Kohli D. [ 49 ] , in which a poor degree of self-care was identified as a major risk factor and highlights the need for self-care for oncology professionals. Mindfulness meditation is also effective at decreasing stress and burnout in nurses [ 50 ] and is increasingly incorporated into mental health interventions [ 51 ] . Through self-regulation and exploration of mindfulness, self-care and meditation, oncology nurses may improve their mindful self-care ability. Mindfulness is practiced both formally and informally to fully engage in the present moment. Formal mindfulness encompassing self-care practices requires separate time be set aside, but informal practices can be integrated into one’s personal and professional life. The results also indicated that informal mindful practices can be the main way to improve the mindful self-care ability of oncology nurses. These are all some insights for future research on mindfulness self-care interventions. Limitations This research has several limitations. First, our cross-sectional study utilized only convenience sampling, and the questionnaire was self-reported and web-based. This may cause bias. Random sampling should be used in further research. Second, in addition to personal characteristics, this research did not use other scales to determine the potential influencing factors of mindful self-care. Based on relative theory, further research should be performed to determine the relationships among other factors and to explore the mesomeric or moderating effects of these factors. Research on interventions for mindful self-care among oncology nurses can also be performed further in China. Conclusions Our study divided mindful self-care among oncology nurses into three subgroups, each with distinct characteristics, which improved our understanding of mindful self-care in different subgroups. To our knowledge, this appears to be the first article to apply LPA to analyze mindful self-care among oncology nurses in China. These findings may help nursing managers identify subgroups of oncology nurses engaged in mindful self-care. Health care professionals and nursing managers should pay more attention to oncology nurses with low levels of mindful self-care ability. Our study also provided evidence for the implementation of interventions to promote mindful self-care in oncology nurses. Declarations Funding No Acknowledgements None. Authors’ contributions Yan Shi contributed to the conception, design, data acquisition, interpretation of the outcomes and manuscript writing of the study. Lamei Liu and Mengmeng Li contributed to the questionnaire distribution. Peng Wang contributed to study supervision, project administration and funding acquisition and contributed to the crucial revision of the manuscript for important intellectual content, provided final confirmation of the revised version to be published and are responsible for the overall content as the guarantor. All authors have read and approved the manuscript. Ethics approval and consent to participate A full compliance with ethical guidelines and regulations was observed in all methods used in this study. The Zhengzhou University Ethics Committee gave its approval to this study(approval number 2022-131), and the subjects gave their informed consent before their data was collected. Consent for publication Not applicable. 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Job satisfaction among oncology nurse practitioners[J]. J Am Assoc Nurse Pract. 2020;33(2):133–42. Piotrkowska R, Jarzynkowski P, Książek J, et al. Satisfaction with life of oncology nurses in Poland[J]. Int Nurs Rev. 2019;66(3):374–80. Peterson B. Middle Range Theories: Application to Nursing Research and Practice. Philadelphia, PA[J]: Wolters Kluwer; 2013. Wazqar DY. Oncology nurses’ perceptions of work stress and its sources in a university- teaching hospital: A qualitative study[J]. Nurs Open, 2019(6):100–8. Hotchkiss JT, Cook-Cottone CP. Validation of the Mindful Self-Care Scale (MSCS) and development of the Brief-MSCS among hospice and healthcare professionals: a confirmatory factor analysis approach to validation[J]. Palliat Supportive Care. 2019;17(6):628–36. Michael S, Villarreal P, Ferguson M, et al. Virtual Reality–Based Resilience Programs: Feasibility and Implementation for Inpatient Oncology Nurses[J]. Clin J Oncol Nurs. 2019;23(6):664–7. Hotchkiss JT. Mindful Self-Care and Secondary Traumatic Stress Mediate a Relationship Between Compassion Satisfaction and Burnout Risk Among Hospice Care Professionals[J]. Am J Hospice Palliat Medicine®. 2018;35(8):1099–108. Di Giuseppe M, Ciacchini R, Piarulli A, et al. Mindfulness dispositions and defense style as positive responses to psychological distress in oncology professionals[J]. Eur J Oncol Nurs. 2019;40:104–10. Cook-Cottone CP. Incorporating positive body image into the treatment of eating disorders: A model for attunement and mindful self-care[J]. Body Image. 2015;14:158–67. Yang Z, Chen FM, Liu SQ, et al. Psychometric Properties of the Chinese Version of the Brief-Mindful Self-Care Scale: A Translation and Validation Study.[J]. Front Psychol. 2021;12(715507):10–3389. Spurk D, Hirschi A, Wang M, et al. Latent profile analysis: A review and how to guide of its application within vocational behavior research[J]. J Vocat Behav. 2020;120:103445. Hensel DJ. Using Latent Profle Analysis and Related Approaches in Adolescent Health Research[J]. 2020,2(67):153–154. Nylund KL, Asparouhov T, Muthén BO. Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study[J]. Struct Equ Model. 2007;4(14):535–69. Cook-Cottone CP, Guyker WM. The Development and Validation of the Mindful Self-Care Scale (MSCS): an Assessment of Practices that Support Positive Embodiment[J]. Mindfulness. 2018;9(1):161–75. Gillet N, Fernet C, Colombat P, et al. Bullying, supervisor support, relaxation, and personal and work outcomes: Testing a moderated mediation model[J]. J Nurs Adm Manag. 2021;00:1–10. ADAA. Physical activity reduces stress[EB/OL]. (2022-11-28)[2024-02-02]. https://adaa.org/understanding-anxiety/related-illnesses/other-related-conditions/stress/physical-activity-reduces-st . Pastor D, Ballester-Ferrer JA, Carbonell-Hernández L et al. physical exercise and cognitive func source int j environ res public health so 2022[J]. Public Health, 2022(19):9564. Cook-Cottone C, Cox AE, Neumark-Sztainer D, et al. Future directions for research on yoga and positive embodiment[J]. Eat Disord. 2020;28(4):542–7. Ploukou S, Panagopoulou E. Playing music improves well being of oncology nurses[J]. Appl Nurs Res, 2018(39):77–80. Adiasto K, Beckers DGJ, van Hooff MLM, Roelofs K. Music listening and stress recovery in healthy individuals: A systematic review with meta-analysis of experimental studies.[J]. PLoS ONE. 2022;6(17):e270031. De Witte M, Pinho ADS, van Hooren et al. Music therapy for stress reduction: a systematic review and meta-analysis[J]. Health Psychol Rev, 2020(11). Phillips CS, Volker DL, Becker H et al. Storytelling Through Music to Improve Well-being in Oncology Nurses[J]. Cancer Nurs, 2020, Publish Ahead of Print. Hanley AW, Warner AR, Dehili VM, et al. Washing Dishes to Wash the Dishes: Brief Instruction in an Informal Mindfulness Practice[J]. Mindfulness. 2015;6(5):1095–103. Nancy JL. How And Why Cleaning Can Improve Your Mental Health[EB/OL]. (2021-11-02)[2024-02-02]. https://www.psycom.net/anxiety/mental-health- benefits-cleaning . McEwan K, Potter V, Kotera Y et al. ‘This Is What the Colour GreenSmells Like!’: Urban Forest Bathing Improved Adolescent Nature Connection and Wellbeing[J]. Int J Environ Res Public Health, 2022(19):15594. Kohli D, Padmakumari P, Self-Care. Burnout, and Compassion Fatigue in Oncology Professionals[J]. Indian J Occup Environ Med. 2020;24(3):168–71. Green AA, Kinchen EV. The Effects of Mindfulness Meditation on Stress and Burnout in Nurses[J]. J Holist Nurs. 2021;39(4):356–68. Wielgosz J, Goldberg SB, Kral TRA, et al. Mindfulness Meditation and Psychopathology[J]. Ann Rev Clin Psychol. 2019;15(1):285–316. Additional Declarations No competing interests reported. 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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-3956160","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":273763299,"identity":"ae84aee5-c63f-446e-b172-4c5fc008d0b1","order_by":0,"name":"Yan Shi","email":"","orcid":"","institution":"School of nursing and health, Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Shi","suffix":""},{"id":273763300,"identity":"50684856-ea47-4a71-a136-051317970793","order_by":1,"name":"Peng Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAs0lEQVRIiWNgGAWjYFAC5oYDDBUQpgSRWhiBWs6QqoWBsY0ULQa3GxsP/pxXZ29wgPngbR4GuzzCWu4cbDgguY2N2eAAW7I1D0NyMWEtNxIbDhhu42EzOMBjJs3DcCCxgSgtiXMkeAwO8H8jQcvBBgMJoC1sxGmRBPrlYMOxBAPJw2zGlnMMkglr4bvdfPjjj5o6e77jzQ9vvKmwI6wFERfMYHcSVM9AfIyPglEwCkbBCAYAo4Q8b2qmrdQAAAAASUVORK5CYII=","orcid":"","institution":"School of nursing and health, Zhengzhou University","correspondingAuthor":true,"prefix":"","firstName":"Peng","middleName":"","lastName":"Wang","suffix":""},{"id":273763301,"identity":"a381b333-9341-4bf2-aed3-b6160cf5fc94","order_by":2,"name":"Lamei Liu","email":"","orcid":"","institution":"School of nursing and health, Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Lamei","middleName":"","lastName":"Liu","suffix":""},{"id":273763302,"identity":"405ccc8c-f8f0-4cac-bf5e-7c62942999a5","order_by":3,"name":"Mengmeng Li","email":"","orcid":"","institution":"Zhengzhou Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mengmeng","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-02-14 13:18:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3956160/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3956160/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51445905,"identity":"3b6c31ec-f0ee-4453-ae3d-239bfa585986","added_by":"auto","created_at":"2024-02-21 18:12:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":60708,"visible":true,"origin":"","legend":"\u003cp\u003eLatent profiles of MSC\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3956160/v1/5efb3939bb77f73f59b55107.png"},{"id":51447100,"identity":"11bfd9dc-50cc-41cd-ad92-62ee54a66ac9","added_by":"auto","created_at":"2024-02-21 18:20:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":612610,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3956160/v1/cf1c8f2d-2794-4670-8201-ee22964eda24.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mindful Self-Care among Oncology Nurses in China: a Latent Profile Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe incidence and mortality of cancer have been gradually increasing in China\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. This increasing burden of cancer poses a serious threat to public health and the economy\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. This also requires oncology nurses to provide higher quality health care\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. The work environment and mental health status of oncology nurses should receive increased attention.\u003c/p\u003e \u003cp\u003eResearch shows that oncology nurses are considered the group with the highest risk for moral distress\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e, compassion fatigue (CF) and burnout\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Moreover, these individuals often face patient deaths, which can create painful experiences and emotional burdens\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Moreover, oncology nurses are expected to be intensely empathic, and they must listen to sad and despairing stories but display or suppress their emotions intermittently\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Many nurses, particularly novice ones, are inadequately prepared to care for end-of-life patients and their families\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Nurses also face challenges, such as getting emotionally attached to patients and difficulty in separating work from their personal life\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Research has also suggested that terminal care, death anxiety, a lack of social support, and ethical issues related to patient care are work-related stressors that oncology nurses face\u003csup\u003e[\u003cspan additionalcitationids=\"CR11 CR12 CR13 CR14\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. If these stressors persist, they can negatively affect oncology nurses. These stressors can affect nurses\u0026rsquo; psychological status and job satisfaction\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e and create job stress\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e, burnout\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e, compassion fatigue\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e, and disinterest in oncology nursing\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Oncology nurses have no time to rest and experience stress \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e and low levels of personal accomplishment in this busy work environment\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Moreover, studies have shown that burnout can cause irritability, sleeplessness, and fatigue and, consequently, alcohol and drug consumption,\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e which affect oncology nurses\u0026rsquo; caring behaviors\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Burnout and job stress can increase the risk of oncology nurses failing to recognize patient distress\u003csup\u003e[\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e, impeding the provision of empathy-based care\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e, which is very important for oncology nurses to have effective communication\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Therefore, oncology nurses must know how to mentally remove themselves from tragic moments and the emotional devastation surrounding them\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Oncology nurses should develop the experience, skills, social support and control\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e needed to manage their psychological health and work-related stress\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e; maintain good boundaries; and be fully present during care\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eResilience measures such as engaging in self-care activities, practicing mindfulness, and having positive self-talks may reduce the effects of occupational burnout\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Maintaining self-awareness and self-monitoring are useful coping skills\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Research on hospice care professionals has shown that those who frequently use self-care strategies experience a higher professional quality of life\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Mindfulness is also a form of self-care\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e in which a defensive style can also be used by oncology professionals to support a positive response to psychological distress\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Mindful self-care is an integration of mindfulness and traditional self-care practices\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e that involves mindful self-awareness and assessment of one\u0026rsquo;s internal and external needs. By developing mindful self-care abilities, oncology nurses can manage their well-being\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e and solve the psychological problems and burdens that work stress can cause .\u003c/p\u003e \u003cp\u003eTo measure informal mindful self-care abilities and promote positive embodiment and well-being, Cook-Cottone and Hotchkiss developed and validated Mindful Self-Care Scale in hospice and health care professionals\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Yang Z.\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e has translated and tested the psychometric properties of the B-MSCS among Chinese hospice nurses. However, the status of mindful self-care among oncology nurses in China is still unclear, and in-depth analysis of mindful self-care is lacking. LPA is a categorical latent variable modeling approach that focuses on identifying latent subpopulations within a population based on a certain set of variables\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. Compared to traditional, nonlatent clustering methods, LPA can classify individuals into clusters based on membership probabilities estimated directly from the model and demographics, and other covariates can be used for profile description. It has been widely applied in psychology and humanities research to identify types of people who have divergent personal attribute profiles\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. This research used LPA to better understand the heterogeneity of oncology nurses\u0026rsquo; mindful self-care and demographic differences related to mindful self-care, which will help us identify areas of strengths and weaknesses in oncology nurses\u0026rsquo; mindful self-care abilities and develop improvement strategies.\u003c/p\u003e \u003cp\u003eAccordingly, this study aimed to (1) identify heterogeneity and homogeneous groups of oncology nurses on mindful self-care ability based on latent profile analysis and 2) examine the sociodemographic correlation of these profiles.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eThis study was a cross-sectional analysis. This study was designed and reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSettings\u003c/h2\u003e \u003cp\u003eThe study was conducted in the oncology department of Henan Province, which includes two affiliated comprehensive hospitals and one affiliated oncology hospital.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eThe purposive sampling method was used to collect the data. An inclusion criterion for this study was that all participants were required to have worked in the oncology department for more than 2 years. Nurses who were reluctant to participate in the study were excluded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eThis research collected data from January and May 2023. Questionnaires were distributed to participants using an online platform. Before completing the questionnaire, each participant was provided informed consent. Participants were asked to rate all items in the questionnaire. Only one questionnaire could be completed per IP address.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eSample size\u003c/h2\u003e \u003cp\u003eLPA requires a sample size greater than 500\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e; therefore, the sample size for this study was at least 500.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData collection tools\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eDemographic Questionnaire (DQ)\u003c/h2\u003e \u003cp\u003eThe questionnaire included several questions to collect data on participants\u0026rsquo; age, sex, education level, marital status, professional title, years of being in the profession, and so on.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eThe Brief Mindful Self-Care Scale\u003c/h2\u003e \u003cp\u003eThe B-MSCS is a 24-item scale that measures the self-reported frequency of self-care behaviors using Likert-type scale response anchors (1= \u0026ldquo;never\u0026rdquo;; 5= \u0026ldquo;always\u0026rdquo;) \u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. This scale was the result of exploratory and confirmatory factor analyses with large samples. The subscales of the B-MSCS include physical care (PC), supportive relationships (SR), mindful self-awareness (MA), self-compassion and purpose (SCP), mindful relaxation (MR), and support structure (SS). The Cronbach\u0026rsquo;s αs for the subscales were 0.77, 0.77, 0.86, 0.78, 0.74 and 0.79. The B-MSCS total scale and subscale have strong internal consistency and reliability\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Yang\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e translated the scale into Chinese and validated its reliability and validity among hospice nurses. The Cronbach\u0026rsquo;s α value of the Chinese version of the B-MSCS was 0.920, and the Cronbach\u0026rsquo;s α value of the dimensions ranged from 0.850 to 0.933. In this research, the Cronbach\u0026rsquo;s α coefficient of the total scale was 0.925, ranging from 0.698 to 0.942.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eAll the statistical analyses were performed using IBM SPSS Statistics 25.0. Descriptive statistics were conducted to summarize participants\u0026rsquo; demographic and clinical characteristics and scores on the scales, including the numbers, frequencies, means and standard deviations (SDs).\u003c/p\u003e \u003cp\u003eThe results of the 24 items on the B-MSCS served as exogenous variables. Because the B-MSCS scores were continuous variables, latent profile analysis (LPA) was carried out using the Mplus 7.4 program. The following fit indicators were used to determine the optimal number of latent profiles: Log Likelihood (LL), Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and adjusted Bayesian Information Criterion (aBIC) were used to compare models, with lower AIC, BIC, and aBIC values indicating a better model fit. The Lo-Mendell-Rubin (LMR) and Bootstrapped Likelihood Ratio Test (BLRT) were used to determine whether a k-class model fit better than a model with k-1 classes, and a significant p value indicated that the k class was better. The entropy values were assessed to determine the classification precision of each model, with entropy values greater than 0.80 indicating adequate classification precision. The minimum percentage of potential subgroups should not be less than 5%. The fitting results of each class and the needs of the researchers were considered together to determine which was the best model.\u003c/p\u003e \u003cp\u003eThe SPSS 25.0 statistical program was used to analyze the data. Frequency and composition ratios were used to describe the categorical variables. The mean and standard deviation were used to describe the continuous variables. One-way ANOVA was used to compare the scores on each B-MSCS dimension in each profile. When significant between-group effects were observed, and post hoc analyses were performed using the Tukey HSD method. One-way ANOVA and the chi-square test were performed to compare differences in sociodemographic characteristics among the subgroups. Multivariate logistic regression was subsequently performed to examine the influence of the sociodemographic variables on each profile. A p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicated statistical significance for all analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eEthical considerations\u003c/h2\u003e \u003cp\u003e The study was conducted in accordance with the Declaration of Helsinki. Informed consent was obtained from all participants before their enrollment in the study. The study received the approval of the Ethics Review Committee of the School of Nursing and Health, Zhengzhou University (approval number 2022\u0026thinsp;\u0026minus;\u0026thinsp;131).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u0026rsquo; characteristics\u003c/h2\u003e \u003cp\u003eMost of the respondents were female (98.8%) and aged less than 40 (94.8%). A total of 91.4% of them had no religious beliefs. Over half of the respondents (68.9%) were married and had a bachelor\u0026rsquo;s degree (53.8%). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the demographic profiles of the participants.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic profiles of the participants (n\u0026thinsp;=\u0026thinsp;839)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean(SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10(1.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e829(98.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31.24(5.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e578(68.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e252(30.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeparated/divorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9(1.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation Level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBachelor degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e451(53.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaster degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22(2.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDoctor degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1(0.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithout degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e365(43.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProfessional title\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNurse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e131(15.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNurse-in-charge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e688(82.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCo-Chief Nurse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18(2.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChief Nurse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2(0.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePosition\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNurse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e762(90.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHead nurse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54(6.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeputy Director of Nursing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4(0.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirector of Nursing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3(0.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16(1.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYears of being in the profession\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e191(22.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e354(42.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u0026ndash;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e294(35.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIncome per month(CNY)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1000\u0026ndash;3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47(5.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3001\u0026ndash;5000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64(7.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5001\u0026ndash;7000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e134(16.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7001\u0026ndash;9000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e323(38.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbove 9000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e271(32.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBe concerned about self-care\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e503(60.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e336(40.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBe interested in mindfulness\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e455(47.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e384(52.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHaving experience of Meditation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e400(47.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e439(52.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eThe dimensions of the level of mindful self-care of oncology nurses\u003c/h2\u003e \u003cp\u003eThe total score of the B-MSCS was 76.40\u0026thinsp;\u0026plusmn;\u0026thinsp;13.19. The SS dimension had the highest score, with an average mean value of 3.60, followed by the MA, SCP, SR, MR and PC dimensions. PC had the lowest score at 2.57. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the details.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe level of mindful self-care of oncology nurses\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of the items\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAverage Score of item of B-MSCS\u003c/p\u003e \u003cp\u003ein oncology nurses\u003c/p\u003e \u003cp\u003e(x̄±s)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupport structure(SS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1st\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMindfulness self-awareness(MA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2nd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-compassion and purpose(SCP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3rd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupportive relationship(SR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMindful relaxation(MR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e2.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical care(PC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e2.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of the different profiles\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the model fit indices for the one-class to four-class solutions. The LL, AIC, BIC, and aBIC generally decreased as the number of estimated profiles increased, while the entropy remained above 0.90 consistently. The LMR tests were significant except for Model 4, which showed that the Model 4 solution did not enhance the model fit considerably compared with that of the Model 3 solution (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.1142). The BLRT results were all significant. Model 3, which included three potential profiles, was determined to be the model with the best fit indices when the models\u0026rsquo; fit indices were evaluated. Based on the results of the latent profile analysis, the scores of the three profiles on the 24 items of the B-MSCS were plotted, and their characteristics are summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Profile 1, profile 2, and profile 3 were designated in accordance with their distinctive distributions based on the mean scores of the MSCs in each profile. Profile 1 scored between profile 2 and profile 3; 51.2% (n\u0026thinsp;=\u0026thinsp;430) of the subjects scored this profile as \u0026ldquo;moderate-level\u0026rdquo; based on its score characteristics. Profile 2 scored significantly lower than profiles 1 and 3, containing 14.8% (n\u0026thinsp;=\u0026thinsp;124) of the subjects; this profile was named \u0026ldquo;low level\u0026rdquo; based on its score characteristics. Profile 3 scored significantly higher than profile 1 and profile 2, containing 34.0% (n\u0026thinsp;=\u0026thinsp;285) of the subjects; this profile was named \u0026ldquo;high-level\u0026rdquo; based on its score characteristics.\u003c/p\u003e \u003cp\u003eThe latent profile memberships exhibited significant differences in the means of the six indicator variables (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A post hoc analysis using the Tukey HSD method revealed that oncology nurses in the high MSC group scored higher than those in the moderate and low MSC groups on both the total scale score and each dimension. The moderate MSC group also scored higher than the low MSC group. The score for each item also showed the same trend (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05) except for Item 4. The score for Item 4 in profile 3 (3.36\u0026thinsp;\u0026plusmn;\u0026thinsp;1.075) was significantly higher than that in profile 1 (3.17\u0026thinsp;\u0026plusmn;\u0026thinsp;1.005), but there were no significant differences between profile 3 and profile 2 (3.37\u0026thinsp;\u0026plusmn;\u0026thinsp;1.063) or between profile 2 and profile 1. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the outcomes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePotential profile analysis indicators (n\u0026thinsp;=\u0026thinsp;839)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLL\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAIC\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBIC\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eaBIC\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEntropy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLMR\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBLRT\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCategory probability (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-26420.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52937.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53164.502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e53012.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-23806.502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47759.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48104.456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47872.632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e57.2/42.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-23078.718\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e46353.436\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e46817.193\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e46505.977\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.930\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e51.2/14.8/34.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-22585.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45416.382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45998.444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45607.836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.1142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9.6/38.1/42.3/10.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cem\u003eNote.\u003c/em\u003e \u003csup\u003ea\u003c/sup\u003e Log likelihood, \u003csup\u003eb\u003c/sup\u003e Akaike information criterion, \u003csup\u003ec\u003c/sup\u003e Bayesian information criterion, \u003csup\u003ed\u003c/sup\u003e Adjusted Bayesian information criterion, \u003csup\u003ee\u003c/sup\u003e Lo-Mendell-Rubin likelihood ratio test, \u003csup\u003ef\u003c/sup\u003e Bootstrapped likelihood ratio test. The boldface text indicates the selected model.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eB-MSCS scores and dimensions in different categories (n\u0026thinsp;=\u0026thinsp;839)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProfile 1\u003c/p\u003e \u003cp\u003eMean(SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProfile 2\u003c/p\u003e \u003cp\u003eMean(SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProfile 3\u003c/p\u003e \u003cp\u003eMean(SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical care(PC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.16(2.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.98(2.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.77(3.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e95.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupportive relationship(SR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.62(2.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.27(2.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.85(2.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e428.447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMindfulness self-awareness(MA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.01(1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.44(1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.68(1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e539.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-compassion and purpose(SC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.90(1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.98(2.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.48(1.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e673.466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMindful relaxation(MR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.01(2.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.54(2.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.21(2.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e267.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupport structure(SS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.83(1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.01(2.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.78(1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e416.494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal score of B-MSCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72.43(5.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57.21(6.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90.76(7.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1341.827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eDemographic and related characteristics of each profile\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e showed the results of the univariate analysis which revealed that significant differences between the three profiles regarding professional title(\u003cspan class=\"InlineEquation\"\u003e\u003c/span\u003e=\u0026thinsp;14.820, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022), position(\u003cspan class=\"InlineEquation\"\u003e\u003c/span\u003e=\u0026thinsp;19.990, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010), whether concerned about self-care(\u003cspan class=\"InlineEquation\"\u003e\u003c/span\u003e=\u0026thinsp;30.430, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001), whether interested in mindfulness(\u003cspan class=\"InlineEquation\"\u003e\u003c/span\u003e=\u0026thinsp;32.691, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001), and experience of meditation(\u003cspan class=\"InlineEquation\"\u003e\u003c/span\u003e=\u0026thinsp;23.814, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001).\u003c/p\u003e \u003cp\u003eA multinomial logistic regression analysis was carried out using profile 3 as the reference group to pinpoint the variables connected to MSC among the three profiles. The professional title and position were converted to binary to avoid floating-point overflow. The outcomes are displayed in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe findings showed that when compared to those who were not concerned about self-care, oncology nurses concerned about self-care had lower odds of being in the moderate MSC group and low MSC group than in the high MSC group (OR: 0.643, CI: 0.446\u0026ndash;0.928 and OR: 0.558, CI: 0.340\u0026ndash;0.916, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05). Compared to those who were not interested in mindfulness, oncology nurses interested in mindfulness had lower odds of being in the moderate MSC group and low MSC group (OR: 0.690, CI: 0.480\u0026ndash;0.993 and OR: 0.536, CI: 0.324\u0026ndash;0.888, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05). Compared to those who were not experienced with meditation, oncology nurses with meditation experience had lower odds of being in the moderate MSC group and low MSC group than in the high MSC group (OR: 0.715, CI: 0.515\u0026ndash;0.993, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05; OR: 0.521, CI: 0.325\u0026ndash;0.834, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.01).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographics and characteristics by latent profile (n\u0026thinsp;=\u0026thinsp;839)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProfile 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProfile 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProfile 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ2/F\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.838\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e424(98.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e123(99.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e282(98.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(0.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.98(5.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.77(4.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.83(6.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.912\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e297(69.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86(69.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e195(68.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e127(29.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37(29.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88(30.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeparated/divorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(0.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation Level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBachelor degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e230(53.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63(50.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e158(55.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaster degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10(2.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(1.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10(3.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDoctor degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(0.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithout degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e190(44.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58(46.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e117(41.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProfessional title\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNurse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77(17.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(13.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37(13.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNurse-in-charge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e345(80.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107(86.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e236(82.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCo-Chief Nurse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12(4.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChief Nurse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePosition\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNurse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e398(92.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120(96.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e244(85.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHead nurse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(4.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(2.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31(10.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeputy Director of Nursing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(0.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirector of Nursing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8(2.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYears of being in the profession\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105(24.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21(16.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65(22.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e180(41.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64(51.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e110(38.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u0026ndash;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e145(33.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39(31.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e110(38.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIncome per month(CNY)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.835\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1000\u0026ndash;3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26(6.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(4.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15(5.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3001\u0026ndash;5000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32(7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(7.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23(8.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5001\u0026ndash;7000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73(17.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(13.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44(15.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7001\u0026ndash;9000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e164(38.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56(45.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e103(36.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbove 9000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e135(31.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36(29.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100(35.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConcern about self-care\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30.432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e239(55.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58(46.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e206(72.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e191(44.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66(53.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79(27.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInterest in mindfulness\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32.691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e215(50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49(39.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e191(67.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e215(50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75(60.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94(33.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eExperience with meditation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e192(44.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42(33.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e166(58.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e238(55.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82(66.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e119(41.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePredictors of latent profile membership\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eProfile 1 vs. Profile 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eProfile 2 vs. Profile 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95%CL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95%CL\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.975\u0026ndash;1.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.945\u0026ndash;1.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProfessional title\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNurse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.891\u0026ndash;2.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.472\u0026ndash;1.934\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNurse-in-charge and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePosition\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNurse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.991\u0026ndash;3.668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.780\u0026ndash;8.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHead nurse and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConcern about self-care\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.643*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.446\u0026ndash;0.928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.558*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.340\u0026ndash;0.916\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInterest in mindfulness\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.690*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.480\u0026ndash;0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.536*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.324\u0026ndash;0.888\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eExperience with meditation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.715*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.515\u0026ndash;0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.521**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.325\u0026ndash;0.834\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003cp\u003eNote. OR, odds ratio; 95% CI, 95% confidence interval; Ref, reference\u003c/p\u003e \u003cp\u003e*\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05 **\u003cem\u003ep\u003c/em\u003e\u0026lt;0.01\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAs a form of self-care, mindfulness can be used among oncology professionals to support a positive response to psychological distress\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. To improve mental health through mindful self-care, we first need to understand the mental health status among oncology nurses. The purpose of our study was to divide oncology nurses into subgroups of mindful self-care and explore the factors associated with mindful self-care. First, to determine the factors influencing mindful self-care ability among oncology nurses, we analyzed the score of B-MSCS. The present study showed that the B-MSCS score of oncology nurses was higher than that of hospice professionals\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. The rank of each dimension also differed, which also showed differences between the two populations and the heterogeneity of the oncology nurses. The support structure (SS) dimension emphasizes environmental factors, which include keeping work areas organized, constructing a manageable schedule and maintaining a pleasing and comfortable living environment\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. Among oncology nurses, the score of this dimension ranks first, possibly because cancer treatment is currently generally standardized and process oriented and because oncology nursing is often organized, which also influences daily life. The Mindful Relaxation(MR) and Physical care(PC) scores were not high among the oncology nurses. MR can be described as a technique for self-soothing, calming, and relaxation, and these are believed to be effective tools in emotional regulation\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. Relaxation has been linked negatively to sleeping problems, emotional exhaustion and need for recovery and positively to work performance and job satisfaction\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. These results may be attributed to the fact that Chinese oncology nurses are always busy and have little time to relax. Chinese oncology nurses should learn new strategies on how to relax and be aware of when they should relax. PC emphasizes on basic nutrition, hydration, and exercise practices\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e, which are related to people\u0026rsquo;s mood and reduce stress\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. Physical inactivity leads to a range of adverse health consequences\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. Oncology nurses should realize the importance of physical activity and participate in exercise, sports, mind-body practice and other activities.\u003c/p\u003e \u003cp\u003e We used the LPA technique to group oncology nurses by their level of mindful self-care ability and identified three distinct profiles\u0026mdash;the moderate MSC group, high MSC group, and low MSC group\u0026mdash;for which the average scores were 57.21, 72.43, and 90.76, respectively. The largest proportion (51.25%) of oncology nurses were in the moderate MSC group, and 14.79% of oncology nurses were in the low MSC group. These results revealed that the mindful self-care ability of oncology nurses still needs to be developed. For Item 3, \u0026lsquo;I regularly took part in sports, dance or other activities\u0026rsquo;, and Item 5, \u0026lsquo;I practiced yoga or another mind-body practice\u0026rsquo;, the scores were relatively low for each item. Yoga practice integrates bottom-up neurophysiological and top-down neurocognitive mechanisms and can help individuals achieve self-regulation and resilience, leading to improved well-being\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. Yoga will also be useful to oncology nurses for improving well-being. For Item 2, \u0026lsquo;I exercised at least 30 to 60 min\u0026rsquo; was not very low because the word \u0026lsquo;exercise\u0026rsquo; may include walking and running in the workplace, which oncology nurses do every day. This can also explain why Item 4, \u0026lsquo;I did sedentary activities instead of exercising\u0026rsquo;, which is a reverse scoring question, was highly and closely related to each other in different profiles.\u003c/p\u003e \u003cp\u003eThe scores for Item 17, \u0026lsquo;I relaxed by doing some creative or intellectual activities\u0026rsquo;, and Item 20, \u0026lsquo;I relaxed through my sense of smell\u0026rsquo;, were also relatively low. These two items are in the same dimension, Mindful Relaxation (MR). This result further illustrated that in this dimension, most oncology nurses cannot relax through creative or intellectual activities such as doing art activities, playing musical instruments, doing creative writing, singing or cleaning. As we know, music intervention and playing music can be a cost-effective resource for reducing stress and improving the well-being of healthy people and oncology nurses\u003csup\u003e[\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. Research has also shown that storytelling through music can address work-related emotions and psychosocial stress in oncology nurses\u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e. Additionally, mindful cleaning, such as organizing drawers, washing dishes, or mopping floors, can allow people to experience greater state mindfulness\u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e, increase people\u0026rsquo;s mood and reduce their anxiety\u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e. Smells, including using essential oils, being exposed to nature, lighting candles, and baking, can affect mood and psychiatric disorders, such as depression and anxiety, and can improve people\u0026rsquo;s connection to nature and well-being\u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e. In the future, mindfulness self-care interventions for oncology nurses could focus on these aspects.\u003c/p\u003e \u003cp\u003eOur research also revealed that the three profiles differed in terms of their social characteristics, including professional title and position. However, these characteristics were not the influencing factors for the different profiles according to the results of the multinomial logistic regression analysis. However, whether oncology nurses concern about self-care, interest in mindfulness and have experience with meditation influences the profile of mindful self-care. This may be because oncology nurses who have such characteristics want to change their situation through mindfulness, self-care and mediation. These results indicated that oncology nurses need to self-regulate, which is the same result as that from the research of Kohli D.\u003csup\u003e[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e, in which a poor degree of self-care was identified as a major risk factor and highlights the need for self-care for oncology professionals. Mindfulness meditation is also effective at decreasing stress and burnout in nurses\u003csup\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/sup\u003e and is increasingly incorporated into mental health interventions\u003csup\u003e[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/sup\u003e. Through self-regulation and exploration of mindfulness, self-care and meditation, oncology nurses may improve their mindful self-care ability. Mindfulness is practiced both formally and informally to fully engage in the present moment. Formal mindfulness encompassing self-care practices requires separate time be set aside, but informal practices can be integrated into one\u0026rsquo;s personal and professional life. The results also indicated that informal mindful practices can be the main way to improve the mindful self-care ability of oncology nurses. These are all some insights for future research on mindfulness self-care interventions.\u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis research has several limitations. First, our cross-sectional study utilized only convenience sampling, and the questionnaire was self-reported and web-based. This may cause bias. Random sampling should be used in further research. Second, in addition to personal characteristics, this research did not use other scales to determine the potential influencing factors of mindful self-care. Based on relative theory, further research should be performed to determine the relationships among other factors and to explore the mesomeric or moderating effects of these factors. Research on interventions for mindful self-care among oncology nurses can also be performed further in China.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study divided mindful self-care among oncology nurses into three subgroups, each with distinct characteristics, which improved our understanding of mindful self-care in different subgroups. To our knowledge, this appears to be the first article to apply LPA to analyze mindful self-care among oncology nurses in China. These findings may help nursing managers identify subgroups of oncology nurses engaged in mindful self-care. Health care professionals and nursing managers should pay more attention to oncology nurses with low levels of mindful self-care ability. Our study also provided evidence for the implementation of interventions to promote mindful self-care in oncology nurses.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYan Shi contributed to the conception, design, data acquisition, interpretation of the outcomes and manuscript writing of the study. Lamei Liu and Mengmeng Li contributed to the questionnaire distribution. Peng Wang contributed to study supervision, project administration and funding acquisition and contributed to the crucial revision of the manuscript for important intellectual content, provided final confirmation of the revised version to be published and are responsible for the overall content as the guarantor. All authors have read and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA full compliance with ethical guidelines and regulations was observed in all methods used in this study. The Zhengzhou University Ethics Committee gave its approval to this study(approval number 2022-131), and the subjects gave their informed consent before their data was collected.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSun D. Cancer burden in China: trends, risk factors and prevention[J]. 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Ann Rev Clin Psychol. 2019;15(1):285\u0026ndash;316.\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":"Mindful self-care, Chinese, Oncology Nurses, Latent profile analysis, Influencing factors","lastPublishedDoi":"10.21203/rs.3.rs-3956160/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3956160/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eOncology nurses are considered the group with the highest risk for moral distress, compassion fatigue and burnout. Mindful self-care may help oncology nurses improve their well-being and solve psychological problems and burdens. It is important to understand oncology nurses\u0026rsquo; mindful self-care.\u003c/p\u003e\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eTo investigate the situation, possible types and influencing factors of mindful self-care among oncology nurses.\u003c/p\u003e\u003ch2\u003eDesign\u003c/h2\u003e \u003cp\u003eCross-sectional descriptive study.\u003c/p\u003e\u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eA total of 839 oncology nurses were enrolled in this survey.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eFrom January to May 2023, a cross-sectional study was carried out among oncology nurses using convenient sampling. The subjects were given the brief Mindful Self-Care Scale (B-MSCS) and the General Demographic Information Questionnaire. Latent profile analysis was used to separate oncology nurses\u0026rsquo; mindful self-care into a variety of subgroups. The SPSS 25.0 statistical program was used to analyze the data. One-way ANOVA and the chi-square test were performed to compare the score of each B-MSCS dimension in each class and the difference in sociodemographic characteristics among the subgroups. Multivariate logistic regression was used to examine the influence of the sociodemographic variables on each profile.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe total score of the B-MSCS was 76.40\u0026thinsp;\u0026plusmn;\u0026thinsp;13.19. The support structure dimension had the highest score, with an average mean value of 3.60, and physical care had the lowest score at 2.57. The findings of the latent profile analysis showed that respondents were divided into three profiles, low (n\u0026thinsp;=\u0026thinsp;124), moderate (n\u0026thinsp;=\u0026thinsp;430), and high mindful self-care (n\u0026thinsp;=\u0026thinsp;285), which accounted for 14.8%, 51.2%, and 34.0%, respectively, of the total respondents. Across scale scores and dimensions, three groups demonstrated statistically significant differences (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Univariate analysis revealed significant differences between the three profiles in terms of professional title, position, concern about self-care, interest in mindfulness, and experience with meditation (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Profile membership was predicted by 3 factors, namely, self-care status, interest in mindfulness, and experience with meditation.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe mindful self-care ability of oncology nurses, especially mindful relaxation and physical activities, should be improved. Three latent mindful self-care profiles were found, and nursing managers should pay more attention to oncology nurses in low-mindful self-care groups. Further interventions combined with mindfulness, self-care or meditation can be performed to improve the mindful self-care ability of oncology nurses.\u003c/p\u003e","manuscriptTitle":"Mindful Self-Care among Oncology Nurses in China: a Latent Profile Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-21 18:12:49","doi":"10.21203/rs.3.rs-3956160/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-03-26T05:40:15+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-04T18:08:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"5a28e4ff-c2cb-412c-8fe9-d120921e76d2","date":"2024-02-23T03:26:07+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-02-22T23:43:58+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-02-19T10:43:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-19T10:40:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-02-19T10:40:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nursing","date":"2024-02-14T13:04:13+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":"d93e7d56-9c00-4e77-915d-538afb97d2a8","owner":[],"postedDate":"February 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-07-08T06:53:39+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-21 18:12:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3956160","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3956160","identity":"rs-3956160","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}