Person-centered exploration of emotional labor patterns, predictors, and their association with mental health in obstetrical nurses: a multicenter cross-sectional study

Person-centered exploration of emotional labor patterns, predictors, and their association with mental health in obstetrical nurses: a multicenter cross-sectional study | 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 Person-centered exploration of emotional labor patterns, predictors, and their association with mental health in obstetrical nurses: a multicenter cross-sectional study Danni Song, Congshan Pu, Jiaai Xia, Liulan Li, Yiting Wang, Zhu Zhu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7288519/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 Obstetrical nurses encounter a multitude of challenging circumstances, emotional labor is an indispensable component of nursing practice and impact on mental well-being significantly. This study aimed to identify different profiles of emotional labor for obstetrical nurses and explore their association with mental health. Methods A cross-sectional survey design using convenience sampling involved 1,480 obstetrics nurses from 32 hospitals in Jiangsu, China. The emotional labor was evaluated using the Emotional Labor Scale for Nurses, while mental health status was measured with the Depression, Anxiety, and Stress Scale. The study employed latent profile analysis to categorize obstetrical nurses into homogeneous profiles based on patterns of emotional labor, partial least squares regression(PLSR) analyses characterized the profiles in terms of sociodemographic and work-related variables, and a general linear regression model was employed to examine the relationship between mental health across different profiles. Results Among the 1,480 obstetrical nurses examined, three distinct profiles were identified: “low emotional labor profile” (n = 112, 7.57%), “moderate emotional labor profile”(n = 908, 61.35%), and “high emotional labor profile”(n = 460, 31.08%). By PLSR, there were three potential factors in which VIP values were greater than 1, namely the number of children, work experience, and interpersonal relationships at work. And the emotional labor profile demonstrated a negative association with stress, anxiety, and depression. Conclusion This study employs a person-centered approach to examine the emotional labor of obstetrical nurses, identifying predictors of profile membership and their relationship with mental health. These findings can inform the development of targeted interventions designed to alleviate stress and enhance the mental health of obstetrical nurses. Emotional labor Latent profile analysis Mental health Obstetrical nurses Figures Figure 1 Figure 2 Introduction In recent years, the opening of the three-child policy [ 1 ], the wider adoption of assisted reproductive technologies [ 2 ], and the rising prevalence of obstetric and gynecological diseases [ 3 ] have converged to swell both the volume and complexity of obstetrical nursing. Care is delivered under ever-tightening pressure, yet nurses cross the ward threshold bound by Nightingale’s pledge—a covenant that obliges them to conform to the emotions required at work. Compared with other departments, obstetrical nurses are confronted with a multitude of challenging circumstances[ 4 ]: nurses must safeguard physically and mentally vulnerable pregnant women, protect newborns whose immunity systems are fragile and unable to express themselves, and reassure families whose expectations of medicine are high. It is imperative that nurses demonstrate patience, compassion, and maintain close communication with patients and their families. This necessitates a commitment to providing a ‘positive’ service to patients, while avoiding the expression of negative emotions. This characteristic of nursing work is inevitably related to “emotional labor”. The term was initially introduced in 1983 by sociologist Arlie Hochschild[ 5 ], who defined it as “the management of feeling to create a publicly observable facial and bodily display”—a deliberate regulation of inner emotions to meet organizational“feeling rules”in exchange for a wage. Hochschild’s framework was later refined for the nursing context by Theodosius[ 6 ], who identified three inter-related domains: therapeutic, collegial, and instrumental emotional labor. Therapeutic emotional labor governs the empathic engagement between nurses and patients (or families); instrumental emotional labor encompasses the nurses’ interpersonal communication skills and confidence in performing clinical tasks used to reduce pain, discomfort, or concerns relating to clinical processes and procedures; Collegial emotional labor underpins the cooperative exchanges among nurses that sustain safe, informed, and effective care. Each domain is continuous, interactive interpersonal processes within the nurse-patient, family, or colleague relationship [ 6 ]. Emotional labor is inherently multidimensional, with its distinct facets frequently co-occurring in everyday practice [ 7 ]. Previous studies have explored composite frameworks to investigate emotional labor patterns utilizing statistical methodologies focusing on variables, such as Factor Analysis [ 8 , 9 ]. However, variable-centered statistical approaches aim to depict patterns or correlations between variables and outcomes, operating under the assumption of data homogeneity within the sampled population, limited in understanding emotional labor in real-world work situations. Therefore, it is essential to take a deeper look into real-life emotional labor situations based on person-oriented research [ 10 ]. Latent profile analysis (LPA), a probabilistic person-centered method, has the advantage of uncovering naturally occurring subgroups that share similar patterns on the sub-dimensions of a construct. Over the past decade, LPA has been used to profile work-related emotional labor among service providers [ 10 ], teachers [ 11 ], office employees [ 12 ], and nurses [ 7 ]. For instance, Korean researchers[ 7 ] discovered five profiles of profiles of emotional labor management strategies among nurses: non-actors, surface actors, moderators, regulators and high regulators. Nevertheless, within the distinctive sociocultural and organizational milieu of Chinese healthcare, where obstetrical nurses confront particularly intense emotional demands[ 13 ]. To the best of our knowledge, person-centered statistical methodologies have yet to be utilized to explore the profiles of emotional labor among Chinese obstetrical nurses. In addition to delineating the various subgroups of obstetrical nurses who experience diverse emotional labor, it is crucial to identify potential risks and protective factors that distinguish one profile from another. Accumulating evidence[ 14 – 16 ] have revealed that sociodemographic and work-related variables, such as age, work experience, professional title, perceived nursing work environment, spiritual climate, and career orientation and planning correlate significantly with how nurses manage their emotions on the job. Heightened emotional labor, in turn, was associated with adverse mental health outcomes such as stress, anxiety, depression, and burnout in individuals [ 17 , 18 ]. With the acceleration of work rhythms and changes in the medical environment, safeguarding nurses’ mental health has become a significant concern. Obstetric units in China, in particular, report some of high rates of psychological distress among nurses[ 19 , 20 ]. However, the precise relationship between the pattern of emotional labor and mental health status remains unclear. Accordingly, this study aimed to categorize emotional labor among obstetrical nurses into different profiles using LPA and explore their correlation with sociodemographic and work-related variables, as well as mental health status encompassing anxiety, depression, and stress. The finding of this study may offer valuable insights for understanding the complexities of emotional labor in obstetrical nurses and workplace interventions targeting adverse mental health outcomes reduction. Methods Participants and procedures The present study utilized a multicenter, cross-sectional design, inviting all eligible obstetrical nurses of 32 hospitals in 13 cities of Jiangsu Province, China, to participate voluntarily from August 2022 to September 2022. Inclusion criteria were as follows: 1) full-time obstetrical nurses with Chinese Practicing Nurse certificates; 2) have worked in the obstetrical department for more than 3 months; 3) willing to participate in the study. Exclusion criteria included: 1) nurses who were rotating to the obstetrical department; 2) nurses from external hospitals who were engaged in continuous studies; and 3) nurses from non-clinical departments. A total of 1,518 registered obstetrical nurses from 32 hospitals were selected as eligible participants. Survey links were distributed to obstetrical nurses, who were invited to participate voluntarily. Of the 1,518 questionnaires distributed to the participants, 1,500 were returned, yielding a response rate of 98.81%. The data from 20 individuals were excluded from the analysis. These individuals were removed due to two criteria: firstly, a high rate of missing responses (at least 50% missing values), and secondly, responding with a single choice of options. Therefore, the final sample consists of 1,480 participants, representing a 98.67% completion rate. The participants provided electronic informed consent to participate in the study. This study was approved by the Ethics Committee of Nanjing Women and Children’s Healthcare Hospital (2022KY-090-01). All participants provided informed consent for this study. Measures Sociodemographic and work-related variables The sociodemographic variables that were collected in this study were as follows: age(18 ~ 29, 30 ~ 39, 40 ~ 49, and ≥ 50 years old), education level(technical secondary school, junior college, and bachelor’s degree or above), marital status(single, married and widowed or separated), number of children(0, 1 and ≥ 2), and personality type(introversion type, middle type and extroversion type). Work-related variables that were collected in this study were as follows: work experience( 15 years), level of the hospital(level B secondary hospital, level A secondary hospital, level B tertiary hospital, and level A tertiary hospital), type of hospital(comprehensive hospital and specialized hospital), professional title(junior, intermediate and senior), employment method(permanent contract and personnel agency), monthly income( 10000 RMB), overtime hours(≤ 5h and > 5h), night shift status(no and yes), monthly night shifts(0, 1 ~ 4, 5 ~ 10 and > 10), and interpersonal relationships at work(not harmonious or generally harmonious, relatively harmonious and fully harmonious). Emotional labor The level of emotional labor in obstetrical nurses was measured with the Chinese version of the Emotional Labor Scale(ELS) for nurses[ 9 ], which was developed by Hong and Kim [ 21 ]. The questionnaire consists of 16 items that constitute 3 dimensions: emotional control effort in the profession, patient-focused emotional suppression, and emotional pretense by norms. All items were assessed on a five-point Likert scale, ranging from 1(strongly disagree) to 5 (strongly agree), with a total score ranging from 16 to 80, a higher score signifies stronger emotional labor management for nurses. The internal consistency of the ELS for nurses in this study was favorable, with Cronbach’s α coefficients of 0.905. Additionally, the structure of the Chinese version of the ELS was consistent with that of the original scale, and the Cronbach's α coefficients of the 3 dimensions were 0.764 ~ 0.881, Item-Level Content Validity Index(I-CVI) was 0.83∼1.00, Scale-Level Content Validity Index(S-CVI) was 0.969, which can effectively and reliably evaluate the emotional labor of nurses in China [ 9 ]. Mental health The mental health in in obstetrical nurses was measured with the Chinese version of Depression, Anxiety, and Stress Scale (DASS-21), which was developed by Lovibond [ 22 ], and devised by Gong Xu [ 23 ]. This scale comprises 3 dimensions, each dimension contains 7 items. All items were assessed on a 4-point Likert scale, ranging from 0(did not apply to me at all) to 3 (applied to me very much or most of the time), the total score is computed by multiplying the sum of the items by two, with a total score ranging from 0 to 126, a higher score was indicative of a worse mental health status. The internal consistency of the DASS-21 in this study was favorable, with Cronbach’s α coefficients of 0.971. Additionally, the Pearson correlation coefficient between the scores of depression, anxiety, and stress subscales and the DASS-21 scale was 0.895–0.910, and the correlation coefficient between each subscale was 0.708–0.741 (all P < 0.01), which can effectively and reliably evaluate the DASS-21[ 24 ]. Statistical analysis Descriptive statistics were calculated to characterize participant demographics, work-related variables, and mental health scores. Results were presented as numbers and percentages [n (%)] for categorical variables, as means and standard deviations (mean ± SD) for continuous variables that were normally distributed, and as the median and interquartile range[M(Q25, Q75)] for continuous variables that were not normally distributed. Latent profile analysis (LPA) was used to identify obstetrical nurses’ subgroups sharing similar patterns based on emotional labor[ 25 ]. Models with varying numbers of latent classes were tested to find the optimal number. Following prior recommendations, we evaluated each model using several criteria: Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), adjusted Bayesian Information Criteria(aBIC), Entropy, Lo-Mendell-Rubin Likelihood Ratio Test(LMRT), and Bootstrapped Likelihood Ratio Test (BLRT). A lower value of AIC, BIC, aBIC, and a significant p-value in the LMRT and BLRT tests indicates a better model fit. Entropy ranges between 0 and 1 with values close to 1 indicating high classification accuracy[ 25 ]. We investigated disparities in sociodemographic factors, work-related variables, and mental health scores among the identified emotional labor profiles. Chi-square tests were used for categorical variables, and Kruskal-Wallis H tests for continuous variables that were not normally distributed. Because some variables were potentially collinear, a robust multivariate statistical technique was selected to execute the conceptual model and perform the aforementioned ranking, namely Partial Least Squares Regression(PLSR) [ 26 ], which is based on multiple regression and principal component regression, creates an optimal linear relationship between the predictors and the response specified in a conceptual model [ 27 ]. In the PLSR, variable importance on projection(VIP) values were calculated to reflect important variables that greatly affected emotional labor profiles. In general, VIP values above 1.0 were considered to signify important independent variables. Additionally, the general linear regression was applied to compute mean differences and 95% CI s for mental health across classes with two different models. Model 1 was a crude model, which was unadjusted, while Model 2 incorporated adjustments for potential confounders. These confounders were identified as factors correlated with work-related stress profiles with P values < 0.05 in univariate analysis, including age, marital status, number of children, personality type, work experience, level of hospital, type of hospital, professional title, employment method, night shift status, monthly night shifts, and interpersonal relationships at work. All analyses were performed with Mplus 8.3 and SPSS 26.0 software, a two-sided significance level of P values < 0.05 was considered statistically significant. Results Characteristics of study participants Of the 1480 obstetrical nurses who participated in this study, the largest proportion, 690(46.6%), were aged 30 to 39 years. A total of 1271(85.9%) held a college degree or higher, 1084(73.2%) were married, 737(49.8%) had one child, and 935(63.2%) reported middle personality type. Additionally, most nurses 788(53.2%) were from tertiary hospital Grade A hospitals(Table 1 ). Table 1 Sociodemographic and work-related variables, and mental health scores according to emotional labor profiles. Variables All samples( n = 1480) Low( n = 112) Middle( n = 908) High( n = 460) Test value P Sociodemographic variables Age, n(%) 16.646 a 0.011 18 ~ 29 years 524(35.4) 49(43.8) 325(35.8) 150(32.6) 30 ~ 39 years 690(46.6) 56(50.0) 425(46.8) 209(45.4) 40 ~ 49 years 204(13.8) 6(5.4) 120(13.2) 78(17.0) ≥ 50 years 62(4.1) 1(0.9) 38(4.2) 23(5.0) Education level, n(%) 2.216 b 0.671 Technical secondary school 12(0.8) 1(0.9) 6(0.7) 5(1.1) Junior college 197(13.3) 18(16.1) 122(13.4) 57(12.4) Bachelor’s degree or above 1271(85.9) 93(83.0) 780(85.9) 398(86.5) Marital status, n(%) 17.657 a 0.001 Single 368(24.9) 40(35.7) 235(25.9) 93(20.2) Married 1084(73.2) 70(62.5) 651(71.7) 363(78.9) Widowed or separated 28(1.9) 2(1.8) 22(2.4) 4(0.9) Number of children, n(%) 18.569 a 0.001 0 child 470(31.8) 50(44.6) 297(32.7) 123(26.7) 1 child 737(49.8) 52(46.4) 435(47.9) 250(54.3) ≥ 2 children 273(18.4) 10(8.9) 176(19.4) 87(18.9) Personality type, n(%) Introversion type 298(20.1) 30(26.8) 177(19.5) 91(19.8) 12.871 a 0.012 Middle type 935(63.2) 68(60.7) 595(65.5) 272(59.1) Extroversion type 247(16.7) 14(12.5) 136(15.0) 97(21.1) Work-related variables Work experience, n(%) 22.528 a 0.013 < 1 years 36(2.4) 5(4.5) 22(2.4) 9(2.0) 1 ~ 3 years 182(12.3) 23(20.5) 112(12.3) 47(10.2) 4 ~ 6 years 221(14.9) 16(14.3) 137(15.1) 68(14.8) 7 ~ 9 years 283(19.1) 25(22.3) 174(19.2) 84(18.3) 10 ~ 15 years 433(29.3) 31(27.7) 272(30.0) 130(28.3) > 15 years 325(22.0) 12(10.7) 191(21.0) 122(26.5) Level of hospital, n(%) Secondary grade B hospital 71(4.8) 4(3.6) 44(4.8) 23(5.0) 14.650 a 0.023 Secondary grade Ahospital 300(20.3) 16(14.3) 192(21.1) 92(20.0) Tertiary grade B hospital 321(21.7) 23(20.5) 175(19.3) 123(26.7) Tertiary grade A hospital 788(53.2) 69(61.6) 497(54.7) 222(48.3) Type of hospital, n(%) 6.906 a 0.032 Comprehensive hospital 936(63.2) 71(63.4) 552(60.8) 313(68.0) Specialized hospital 544(36.8) 41(36.6) 356(39.2) 147(32.0) Professional title, n(%) 13.492 a 0.009 Junior 757(51.1) 72(64.3) 462(50.9) 223(48.5) Intermediate 540(36.5) 34(30.4) 339(37.3) 167(36.3) Senior 183(12.4) 6(5.4) 107(11.8) 70(15.2) Employment method, n(%) 6.489 a 0.039 Permanent contract 337(22.8) 16(14.3) 204(22.5) 117(25.4) Personnel agency 1143(77.2) 96(85.7) 704(77.5) 343(74.6) Monthly income,n(%) 3.322 a 0.505 10000 RMB 221(14.9) 11(9.8) 134(14.8) 76(16.5) Overtime hours(weekly), n(%) 5.342 a 0.069 ≤ 5h 1009(68.2) 71(63.4) 639(70.4) 299(65.0) > 5h 471(31.8) 41(36.6) 269(29.6) 161(35.0) Night shift status, n(%) 14.256 a 0.001 No 373(25.2) 18(16.1) 213(23.5) 142(30.9) Yes 1107(74.8) 94(83.9) 695(76.5) 318(69.1) Monthly night shifts, n(%) 14.571 a 0.024 0 268(18.1) 14(12.5) 151(16.6) 103(22.4) 1ཞ4 294(19.9) 16(14.3) 184(20.3) 94(20.4) 5ཞ10 693(46.8) 64(57.1) 435(47.9) 194(42.2) > 10 225(15.2) 18(16.1) 138(15.2) 69(15.0) Interpersonal relationships at work, n(%) 98.100 < 0.001 Not harmonious or generally harmonious 363(24.5) 50(44.6) 236(26.0) 77(16.7) Relatively harmonious 829(56.0) 53(47.3) 543(59.8) 233(50.7) Fully harmonious 288(19.5) 9(8.0) 129(14.2) 150(32.6) Mental health M(Q25, Q75) Stress 11(8,14) 14(12,16) 11(8,14) 10(7,13) 65.819 c < 0.001 Anxiety 11(8,14) 14(12,17) 11(8,14) 10(7,14) 74.735 c < 0.001 Depression 11(8,14) 14(12,18) 12(9,14) 10(7,14) 72.46 c < 0.001 DASS score 33(25,42) 42(36,51) 33(26,42) 29(22,40) 74.536 c < 0.001 DASS: Depression, Anxiety, and Stress Scale. a Pearson’s Chi-squared test; b Fisher’s exact test; c Kruskal-Wallis H test. Latent profile of emotional labor among obstetrical nurses Three potential category models were fitted, and the model fit statistics are presented in Table 2 . The 3-profile model exhibited comparatively more minor AIC, BIC, and aBIC values, higher entropy scores, and significant P values in the LMRT and BLRT. The scores for the three profiles are illustrated in Fig. 1 . Profile 1, designated as the “low emotional labor profile”, comprised 112(7.6%) of participants. Profile 2, characterized by moderate levels across all items, was termed the “moderate emotional labor profile”, Profile 3, referred to as the “low emotional labor profile”, accounted for 112(7.6%). Table 2 Model fit statistics and latent profiles of the participants. Models AIC BIC aBIC Entropy LMRT p -value BLRT p -value Category probability (%) 1 56480.634 56650.227 56548.573 - - - - 2 48434.340 48694.030 48538.372 0.977 < 0.001 < 0.001 68.04 / 31.96 3 44209.996 44559.783 44350.120 0.987 < 0.001 < 0.001 7.57 / 61.35 / 31.08 4 42807.307 43247.190 42983.523 0.971 0.1353 < 0.001 7.57 / 19.66 / 59.80 / 12.97 5 41661.341 42191.321 41873.651 0.977 0.1093 < 0.001 0.68 / 8.99 / 12.70 / 57.97 / 19.66 Note: AIC = Akaike Information Criteria, BIC = Bayesian Information Criteria, aBIC = adjusted Bayesian Information Criteria, LMRT = Lo-Mendell-Rubin Likelihood Ratio Test, BLRT = Bootstrapped Likelihood Ratio Test. Comparisons of sociodemographic and work-related characteristics by classes The sociodemographic and work-related variables and mental health scores of each latent profile are shown in Table 1 . Statistically significant differences were observed between profiles in terms of four sociodemographic variables (age, marital status, number of children, and personality type), eight work-related variables (work experience, level of hospital, type of hospital, professional title, employment method, night shift status, monthly night shifts, and interpersonal relationships at work) and all domains of mental health (stress, anxiety, and depression; all P 0.05). To identify predictors of emotional labor, multivariate analyses were performed between “high emotional labor” and “low emotional labor” to assess whether sociodemographic and work-related variables differed in emotional labor across classes. PLSR analyzed the relationship between the emotional labor profiles and influencing factors. The default five latent factors of the PLSR modeling system were selected initially. Results (Table 3 ) indicated that the first three potential factors have strong explanatory power for the dependent variables. As the first three latent factors extracted 46.2% of the information from the independent variables and the fourth and fifth latent factors explained less than 0.001 of the information from the dependent variable, the first three latent factors were selected for extraction in this study. In detail, there were three potential factors in which VIP values were greater than 1, namely number of children (VIP = 1.228, 1.019, 1.005), work experience (VIP = 1.258, 1.063, 1.048), and the interpersonal relationships at work(VIP = 2.209, 2.823, 2.806), which were essential variables affecting the emotional labor in obstetrical nurses(Table 4 )( Fig. 2 ). The VIP values of age, marital status, work experience, and night shift status were greater than 0.8, suggesting that these factors also influence the emotional labor in obstetrical nurses to a certain extent. The VIP contribution values of other factors were all less than 0.8, indicating that they were not important factors. Table 3 Quality evaluation of partial least squares regression (PLS) model of ELS and various factors t X Variance Cumulative X Variance Y Variance Cumulative Y Variance (R 2 ) Adjusted R 2 ELS 1 0.260 0.260 0.086 0.086 0.084 2 0.118 0.377 0.040 0.126 0.123 3 0.084 0.462 0.004 0.130 0.125 4 0.070 0.531 < 0.001 0.130 0.124 5 0.053 0.584 < 0.001 0.131 0.123 Note: t is the components extracted from independent variables in ELS analysis; Table 4 Regression coefficient and the variable importance projection (VIP) of potential factors Factor Regression coefficient The VIP value of the potential factor 1 2 3 X 1 : Age 0.029 1.140 0.994 0.980 X 2 : Education level < 0.001 0.252 0.331 0.337 X 3 : Marital status 0.013 0.983 0.841 0.844 X 4 : Number of children 0.059 1.228 1.019 1.005 X 5 : Personality type 0.046 0.727 0.753 0.770 X 6 : Work experience 0.036 1.258 1.063 1.048 X 7 : Level of hospital -0.075 0.712 0.956 0.947 X 8 : Type of hospital 0.003 0.294 0.286 0.417 X 9 : Professional title 0.023 1.075 0.947 0.934 X 10 : Employment method -0.017 0.783 0.692 0.682 X 11 : Monthly income 0.025 0.415 0.442 0.542 X 12 : Overtime hours 0.006 0.100 0.103 0.197 X 13 : Night shift status -0.034 0.978 0.807 0.800 X 14 : Monthly night shifts -0.029 0.818 0.675 0.673 X 15 : Interpersonal relationships at work 0.279 2.209 2.823 2.806 Association of emotional labor profiles with mental health When exploring the association between emotional labor profiles and mental health, the results (Table 5 ) showed that the high emotional labor profile exhibited negative associations across all mental health domain scores compared to the low emotional labor profile in both the crude and adjusted models. Specifically, the high emotional labor profile showed negative associations with DASS-21 scores( β =-0.172, 95% CI =-4.687~-1.654), stress( β =-0.151, 95% CI =-1.449~-0.426), anxiety ( β =-0.171, 95% CI =-1.564~-0.547), and depression ( β =-4.401, 95% CI =-1.703~ -0.652) after adjusting for confounders. Table 5 The associations between latent profile membership and mental health score using linear regression. Mental health scores Crude model a Adjusted model β t 95% CI b P value β t 95% CI b P value Stress -0.249 -6.142 -2.035, -1.049 < 0.001 -0.151 -3.600 -1.449, -0.426 < 0.001 Anxiety -0.264 -6.534 -2.119, -1.139 < 0.001 -0.171 -4.079 -1.564,-0.547 < 0.001 Depression -0.278 -6.903 -2.283, -1.272 < 0.001 -0.184 -4.401 -1.703, -0.652 < 0.001 DASS-21 -0.268 -6.645 -6.411, -3.486 < 0.001 -0.172 -4.107 -4.687,-1.654 < 0.001 a Adjusted factors: age, marital status, number of children, personality type, work experience, level of hospital, type of hospital, professional title, employment method, night shift status, monthly night shifts, and interpersonal relationships at work. b CI = Confidence Interval. Discussion In this study, LPA was used to examine emotional labor among 1480 obstetrical nurses in Jiangsu Province. The analysis revealed three distinct profiles: low emotional labor profile, moderate emotional labor profile and high emotional labor profile, each defined by correspondingly low, moderate, or high levels of emotional labor. Education level, level of hospital, and interpersonal relationships at work emerged as key predictors of these profiles. Importantly, higher emotional labor profiles were consistently associated with poorer mental health outcomes. The current study employed a person-centered methodology to uncover three unique latent classes of emotional labor among obstetrical nurses, offering a perspective rarely taken in prior nursing research. Traditionally, investigations of emotional labor in nursing have relied on variable-centered designs [ 8 , 28 – 30 ]. For example, a cross-sectional investigation involving 561 nurses recruited from five tertiary hospitals in China utilized exploratory factor analysis to distill emotional labor into three dimensions that together explained 62.28% of the cumulative variance contribution [ 8 ]. Similar studies[ 28 – 30 ] have mapped linear relationships between emotional labor and relevant variables such as perceived organizational justice, professional identity, job performance, job stress, instrumental support, coaching leadership, and psychological costs. However, these studies are often constrained by their reliance on variable-centered methods, assuming homogeneity in populations and aiming primarily to establish connections between variables, ignoring that individuals might employ more complex combinations of deviant behaviors that do not fit neatly within the existing variable frameworks [ 31 ]. In contrast, the person-centered approach focuses on identifying latent subpopulations based on various observed characteristics, offering a more nuanced understanding than the variable-centered model [ 32 , 33 ]. Our three-profile solution diverges from the five-profile taxonomy reported by Park et al.[ 7 ] among Korean nurses—non-actors, surface actors, moderators, regulators, and high regulators—most likely because of the disparate emotional labor scales employed. The present findings underscore the value of moving beyond single-score averages to reveal clinically meaningful sub-populations. Such insights could aid in developing more targeted intervention and prevention strategies tailored that address the specific emotional labor patterns exhibited by different groups of obstetrical nurses. To eliminate the effects of covariance between variables, this study employed PLSR. Introduced by Wold and Abano[ 34 ] in 1983 for chemometric analyses, PLSR is designed to handle highly correlated explanatory variables and to incorporate multiple predictors simultaneously. Over the past three decades, the method has been refined theoretically and applied far beyond chemistry to fields across the natural and social sciences [ 35 – 37 ]. In the present study, PLSR identified two predictors of obstetrical nurses’ emotional labor: number of children and years of work experience. First, the positive effect of having children echoes findings among preschool teachers, where parenthood intensifies emotional labor demands [ 38 ]. Obstetric nurses occupy a uniquely multifaceted role that involves not only caring for parturients but also safeguarding the earliest moments of neonatal life [ 39 ]. Nurses who are parents themselves often exhibit heightened emotional sensitivity and expressiveness, allowing them to draw on personal experience to engage more naturally and empathically with patients. Second, work experience emerged as a key determinant across every dimension of emotional labor. Consistent with prior studies[ 15 , 40 ], seasoned nurses accumulate sophisticated emotional-regulation and communication competencies, enabling rapid, contextually attuned responses to clinical crises and a more nuanced appreciation of the subtleties inherent in emotional labor。 Additionally, there were significant differences between the high and low emotional labor profiles based on interpersonal relationships at work. Based on the Job Demands-Resources (JD-R) model and the Conservation of Resources (COR) theory [ 41 ], supportive collegial ties function as a critical job resource that effectively buffers the negative impact of work demand. A cohesive team climate can be a potentially valuable resource for nurses to replenish resources, mitigate the impact of emotional labor concealment, thereby curb burnout and turnover intentions [ 42 ]. Moreover, the interpersonal relationships at work represent an organizational identification, a form of social identity defined by the degree of identification, emotional dependence, and participation of organization members, and the sense of belonging that they are members of the organization [ 43 ]. Strong identification transforms the workplace from a contractual setting into a “community of shared destiny,” motivating individuals to internalize the organization’s mission and to pursue its goals with volitional commitment [ 44 ]. Previous studies have highlighted that nurses seek identification from their organization in the process of managing the emotional demands of the profession [ 45 , 46 ]. In the Chinese context, where traditional “benevolence” (ren) and societal expectations endow the nursing role with moral significance, nurses who identify closely with their organization are more inclined to engage in deep acting rather than surface acting. This internalized form of emotional regulation not only sustains authenticity but also yields favorable attitudinal outcomes, reinforcing the pivotal role of organizational identification in shaping constructive responses to emotional labor. The current study revealed a complex relationship between emotional labor profiles and mental health, revealing both potential strains and salutary effects. On one hand, emotional labor can exact a psychological toll; on the other, it may serve as a catalyst for the cultivation of emotional intelligence (EI) and, ultimately, personal growth [ 47 ]. EI can be defined as the capacity to accurately perceive, appraise, and integrate internal and external emotional cues in order to guide one’s thoughts and behaviors. Nurses with high EI demonstrate greater sensitivity to shift in their patients' affects, better able to regulate their own emotional expression in response, and more adept at managing the inherent demands of emotional labor [ 48 ]. When these competencies are mobilized as part of professional role requirements, they constitute emotional labor itself. Consistent with this view, prior research has highlighted that EI is significantly and positively correlated with nurses’ well-being and self-efficacy, and negatively correlated with perceived stress[ 47 ]. Emotional labor, as a specific form of emotional regulation, entails cognitive reappraisal and is bolstered by both personal and external protective factors. Together, these elements can foster adaptive responses that restore psychological equilibrium following adverse events [ 49 ]. Obstetrical nurses with high emotional labor may be more willing to understand the underlying motives for patient’s behaviors and to adopt the patient’s perspective. This empathic attunement facilitates effectively self-regulation, encourages positive reframing, and nurtures genuine compassion. Consequently, emotional labor becomes a relational resource: it helps nurses establish authentic, caring connections with patients [ 50 ]. Such nurses typically exhibit openness, curiosity, and respect—qualities that enable them to explore patients’ needs and preferences more fully. By sensitively responding to these needs, they not only enhance the quality of care but also elicit appreciation and emotional support from patients and families. This reciprocal affirmation bolsters nurses’ sense of professional accomplishment and reinforces adaptive emotion regulation. Moreover, the daily empathy process with patients always involves reciprocal sharing, which can help patients and relatives better understand the complexities of nursing work and provide emotional feedback[ 50 ], which improve nurses to manage their own affective states, ultimately promoting better mental health. The present study demonstrates the value of utilizing real-world data collected from a diverse range of hospitals, thereby reducing the potential for biases associated with a single-center clinical approach. Another strength is the high response and completion rates signal robust engagement among obstetrical nurses, enhancing the representativeness of the sample and the credibility of the findings. However, we must acknowledge several limitations. Firstly, the sample comprises obstetrical nurses from Jiangsu Province, and regional nuances in staffing models, organizational culture, or health-care policies may constrain the broader applicability of the results. Secondly, despite collecting various sociodemographic and work-related characteristics, there may be unmeasured factors like job satisfaction, salary satisfaction, relevant training experience, mental resilience, and workload that could impact emotional labor. Lastly, mental health outcomes were evaluated via self-report questionnaires rather than standardized interviews or clinical examinations, increasing the likelihood of under-recognition or under-reporting of clinically significant distress. Conclusion Overall, the study highlights the impact of emotional labor on the mental health of obstetrical nurses, offering a detailed insight into the nuances of emotional labor. The results indicate that the obstetrical nurses sampled in Jiangsu Province could be categorized into three qualitatively distinct profiles based on emotional labor. Membership in these profiles was predicted by factors including the number of children, work experience, and interpersonal relationships at work. These findings may facilitate the development of bespoke interventions designed to mitigate the levels of emotional labor experienced by obstetrical nurses, thereby enhancing mental health. Declarations Acknowledgments We sincerely thank the obstetrical nurses who participated in this study, as well as the nurse managers of hospitals and healthcare facilities who provided invaluable assistance throughout the investigation. Author contributions D.S. and C.P.: Conceptualization, Methodology, Software, Writing- original draft. J.X.: Supervision, Writing– review & editing. L.L.: Validation, Methodology. Y.W.: Data curation, Methodology. Z.Z.: Conceptualization, Supervision, Writing- review & editing. C.S.: Conceptualization, Supervision, Writing- review & editing. Funding This study was supported by the Chinese Medical Journals Publishing House Nursing Discipline Research Topics for 2025 (CMAPH-NRD 2022017). Data availability The data that support the findings of this study are available on request from the corresponding authors. Ethical statement The study was conducted in accordance with Helsinki Declaration’s ethical principles, and approved by the Ethics Committee of Nanjing Maternity and Child Health Care Hospital (2022KY-090-01). Consent for publication Not applicable. Competing Interest The authors declare that they have no competing financial interests. References Tatum M: China's three-child policy. Lancet (London, England) 2021, 397(10291):2238. Baker VL, Dyer S, Chambers GM, Keller E, Banker M, de Mouzon J, Elgindy E, Bai FM, Ishihara O, Jwa SC et al: International Committee for Monitoring Assisted Reproductive Technologies (ICMART): world report for cycles conducted in 2017-2018. Human reproduction (Oxford, England) 2025, 40(6):1110-1126. 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Ajibewa TA, Colangelo LA, Chirinos DA, Kershaw KN, Carnethon MR, Allen NB: A person-centered approach to understanding psychosocial stressor subgroups and cardiovascular disease: new perspectives from the Multi-Ethnic Study of Atherosclerosis (MESA) study. Journal of the American Heart Association 2024:e038844. Bigelow B, Kautz J, Carpenter NC, Harris TB: A person-centered approach to behaving badly at work: An examination of workplace deviance patterns. The Journal of applied psychology 2024, 109(11):1742-1764. Wold S, Esbensen K: Pattern recognition: finding and using regularities in multivariate data Food research, how to relate sets of measurements or observations to each other. 1983. Liu C, Zhang X, Nguyen TT, Liu J, Wu T, Lee E, Tu XM: Partial least squares regression and principal component analysis: similarity and differences between two popular variable reduction approaches. General psychiatry 2022, 35(1):e100662. Meacham-Hensold K, Montes CM, Wu J, Guan K, Fu P, Ainsworth EA, Pederson T, Moore CE, Brown KL, Raines C et al: High-throughput field phenotyping using hyperspectral reflectance and partial least squares regression (PLSR) reveals genetic modifications to photosynthetic capacity. Remote sensing of environment 2019, 231:111176. Cheng H, Yi DH, Hu JQ, et al.A Research on an Comprehensive Indicator Establishing Method. Statistics and Decision 2017; (03): 21-3. Wang YX. Situation and Influencing Factors of Preschool Teachers' Use of Emotional Labor Strategies ——Based on Working Situation of Kindergarten Parents. Journal of Longyan University 2022; 40(03): 118-125. Demaria LM, Campero L, Vidler M, Walker D: Non-physician providers of obstetric care in Mexico: Perspectives of physicians, obstetric nurses and professional midwives. Human resources for health 2012, 10:6. Zha LL. Cognition and Behavior of ICU Nurses on Emotional Labor. Practical Clinical Medicine.2021; 22(01): 81-85. He G, An R, Zhang F: Cultural Intelligence and Work-Family Conflict: A Moderated Mediation Model Based on Conservation of Resources Theory. International journal of environmental research and public health 2019, 16(13). Cheng C, Bartram T, Karimi L, Leggat SG: The role of team climate in the management of emotional labour: implications for nurse retention. Journal of advanced nursing 2013, 69(12):2812-2825. Blake, E., Ashforth, Fred, Review MJAoM: Social Identity Theory and the Organization. 1989. Simbula S, Margheritti S, Avanzi L: Building Work Engagement in Organizations: A Longitudinal Study Combining Social Exchange and Social Identity Theories. Behavioral sciences (Basel, Switzerland) 2023, 13(2). Hur WM, Moon TW, Jun JKJIJoCHM: The role of perceived organizational support on emotional labor in the airline industry. 2013, 25(1). Salahieh ZJD, Gradworks T-: The moderating role of perceived organizational support on the relationship between bullying and work behaviors. 2015. Edward KL, Hercelinskyj G, Giandinoto JA: Emotional labour in mental health nursing: An integrative systematic review. International journal of mental health nursing 2017, 26(3):215-225. Hong E, Lee YS: The mediating effect of emotional intelligence between emotional labour, job stress, burnout and nurses' turnover intention. International journal of nursing practice 2016, 22(6):625-632. Delgado C, Evans A, Roche M, Foster K: Mental health nurses' resilience in the context of emotional labour: An interpretive qualitative study. International journal of mental health nursing 2022, 31(5):1260-1275. Wang YL, Yang ZW, Tang YZ, Li HL, Zhou LS: A qualitative exploration of "empathic labor" in Chinese hospice nurses. BMC palliative care 2022, 21(1):23. Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":86412,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMean scores of 16 items of emotional labor scale within each profile.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7288519/v1/30e4cf5ce514b6fcdeb1a310.png"},{"id":91842069,"identity":"4e3fb22f-a605-42d5-a655-455c81c10dac","added_by":"auto","created_at":"2025-09-22 09:57:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":87018,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImportance value projection (VIP) of each factor to ELS\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7288519/v1/6521ce3f67719ec5024afce6.png"},{"id":91845818,"identity":"2318082a-edfe-45e2-addd-853858684305","added_by":"auto","created_at":"2025-09-22 10:13:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1549799,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7288519/v1/8f14d975-ae19-4700-b026-6699a455eab4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Person-centered exploration of emotional labor patterns, predictors, and their association with mental health in obstetrical nurses: a multicenter cross-sectional study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn recent years, the opening of the three-child policy [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], the wider adoption of assisted reproductive technologies [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], and the rising prevalence of obstetric and gynecological diseases [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] have converged to swell both the volume and complexity of obstetrical nursing. Care is delivered under ever-tightening pressure, yet nurses cross the ward threshold bound by Nightingale\u0026rsquo;s pledge\u0026mdash;a covenant that obliges them to conform to the emotions required at work. Compared with other departments, obstetrical nurses are confronted with a multitude of challenging circumstances[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]: nurses must safeguard physically and mentally vulnerable pregnant women, protect newborns whose immunity systems are fragile and unable to express themselves, and reassure families whose expectations of medicine are high. It is imperative that nurses demonstrate patience, compassion, and maintain close communication with patients and their families. This necessitates a commitment to providing a \u0026lsquo;positive\u0026rsquo; service to patients, while avoiding the expression of negative emotions.\u003c/p\u003e\u003cp\u003eThis characteristic of nursing work is inevitably related to \u0026ldquo;emotional labor\u0026rdquo;. The term was initially introduced in 1983 by sociologist Arlie Hochschild[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], who defined it as \u0026ldquo;the management of feeling to create a publicly observable facial and bodily display\u0026rdquo;\u0026mdash;a deliberate regulation of inner emotions to meet organizational\u0026ldquo;feeling rules\u0026rdquo;in exchange for a wage. Hochschild\u0026rsquo;s framework was later refined for the nursing context by Theodosius[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], who identified three inter-related domains: therapeutic, collegial, and instrumental emotional labor. Therapeutic emotional labor governs the empathic engagement between nurses and patients (or families); instrumental emotional labor encompasses the nurses\u0026rsquo; interpersonal communication skills and confidence in performing clinical tasks used to reduce pain, discomfort, or concerns relating to clinical processes and procedures; Collegial emotional labor underpins the cooperative exchanges among nurses that sustain safe, informed, and effective care. Each domain is continuous, interactive interpersonal processes within the nurse-patient, family, or colleague relationship [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eEmotional labor is inherently multidimensional, with its distinct facets frequently co-occurring in everyday practice [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Previous studies have explored composite frameworks to investigate emotional labor patterns utilizing statistical methodologies focusing on variables, such as Factor Analysis [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, variable-centered statistical approaches aim to depict patterns or correlations between variables and outcomes, operating under the assumption of data homogeneity within the sampled population, limited in understanding emotional labor in real-world work situations. Therefore, it is essential to take a deeper look into real-life emotional labor situations based on person-oriented research [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Latent profile analysis (LPA), a probabilistic person-centered method, has the advantage of uncovering naturally occurring subgroups that share similar patterns on the sub-dimensions of a construct. Over the past decade, LPA has been used to profile work-related emotional labor among service providers [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], teachers [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], office employees [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], and nurses [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. For instance, Korean researchers[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] discovered five profiles of profiles of emotional labor management strategies among nurses: non-actors, surface actors, moderators, regulators and high regulators. Nevertheless, within the distinctive sociocultural and organizational milieu of Chinese healthcare, where obstetrical nurses confront particularly intense emotional demands[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. To the best of our knowledge, person-centered statistical methodologies have yet to be utilized to explore the profiles of emotional labor among Chinese obstetrical nurses.\u003c/p\u003e\u003cp\u003eIn addition to delineating the various subgroups of obstetrical nurses who experience diverse emotional labor, it is crucial to identify potential risks and protective factors that distinguish one profile from another. Accumulating evidence[\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] have revealed that sociodemographic and work-related variables, such as age, work experience, professional title, perceived nursing work environment, spiritual climate, and career orientation and planning correlate significantly with how nurses manage their emotions on the job. Heightened emotional labor, in turn, was associated with adverse mental health outcomes such as stress, anxiety, depression, and burnout in individuals [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. With the acceleration of work rhythms and changes in the medical environment, safeguarding nurses\u0026rsquo; mental health has become a significant concern. Obstetric units in China, in particular, report some of high rates of psychological distress among nurses[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, the precise relationship between the pattern of emotional labor and mental health status remains unclear.\u003c/p\u003e\u003cp\u003eAccordingly, this study aimed to categorize emotional labor among obstetrical nurses into different profiles using LPA and explore their correlation with sociodemographic and work-related variables, as well as mental health status encompassing anxiety, depression, and stress. The finding of this study may offer valuable insights for understanding the complexities of emotional labor in obstetrical nurses and workplace interventions targeting adverse mental health outcomes reduction.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eParticipants and procedures\u003c/h2\u003e\u003cp\u003e The present study utilized a multicenter, cross-sectional design, inviting all eligible obstetrical nurses of 32 hospitals in 13 cities of Jiangsu Province, China, to participate voluntarily from August 2022 to September 2022. Inclusion criteria were as follows: 1) full-time obstetrical nurses with Chinese Practicing Nurse certificates; 2) have worked in the obstetrical department for more than 3 months; 3) willing to participate in the study. Exclusion criteria included: 1) nurses who were rotating to the obstetrical department; 2) nurses from external hospitals who were engaged in continuous studies; and 3) nurses from non-clinical departments. A total of 1,518 registered obstetrical nurses from 32 hospitals were selected as eligible participants. Survey links were distributed to obstetrical nurses, who were invited to participate voluntarily.\u003c/p\u003e\u003cp\u003eOf the 1,518 questionnaires distributed to the participants, 1,500 were returned, yielding a response rate of 98.81%. The data from 20 individuals were excluded from the analysis. These individuals were removed due to two criteria: firstly, a high rate of missing responses (at least 50% missing values), and secondly, responding with a single choice of options. Therefore, the final sample consists of 1,480 participants, representing a 98.67% completion rate. The participants provided electronic informed consent to participate in the study. This study was approved by the Ethics Committee of Nanjing Women and Children\u0026rsquo;s Healthcare Hospital (2022KY-090-01). All participants provided informed consent for this study.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eSociodemographic and work-related variables\u003c/h2\u003e\u003cp\u003eThe sociodemographic variables that were collected in this study were as follows: age(18\u0026thinsp;~\u0026thinsp;29, 30\u0026thinsp;~\u0026thinsp;39, 40\u0026thinsp;~\u0026thinsp;49, and \u0026ge;\u0026thinsp;50 years old), education level(technical secondary school, junior college, and bachelor\u0026rsquo;s degree or above), marital status(single, married and widowed or separated), number of children(0, 1 and \u0026ge;\u0026thinsp;2), and personality type(introversion type, middle type and extroversion type). Work-related variables that were collected in this study were as follows: work experience(\u0026lt;\u0026thinsp;1, 1\u0026thinsp;~\u0026thinsp;3, 4\u0026thinsp;~\u0026thinsp;6, 7\u0026thinsp;~\u0026thinsp;9, 10\u0026thinsp;~\u0026thinsp;15 and \u0026gt;\u0026thinsp;15 years), level of the hospital(level B secondary hospital, level A secondary hospital, level B tertiary hospital, and level A tertiary hospital), type of hospital(comprehensive hospital and specialized hospital), professional title(junior, intermediate and senior), employment method(permanent contract and personnel agency), monthly income(\u0026lt;\u0026thinsp;5000, 5000\u0026ndash;10000 and \u0026gt;\u0026thinsp;10000 RMB), overtime hours(\u0026le;\u0026thinsp;5h and \u0026gt;\u0026thinsp;5h), night shift status(no and yes), monthly night shifts(0, 1\u0026thinsp;~\u0026thinsp;4, 5\u0026thinsp;~\u0026thinsp;10 and \u0026gt;\u0026thinsp;10), and interpersonal relationships at work(not harmonious or generally harmonious, relatively harmonious and fully harmonious).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eEmotional labor\u003c/h3\u003e\n\u003cp\u003eThe level of emotional labor in obstetrical nurses was measured with the Chinese version of the Emotional Labor Scale(ELS) for nurses[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], which was developed by Hong and Kim [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The questionnaire consists of 16 items that constitute 3 dimensions: emotional control effort in the profession, patient-focused emotional suppression, and emotional pretense by norms. All items were assessed on a five-point Likert scale, ranging from 1(strongly disagree) to 5 (strongly agree), with a total score ranging from 16 to 80, a higher score signifies stronger emotional labor management for nurses. The internal consistency of the ELS for nurses in this study was favorable, with Cronbach\u0026rsquo;s α coefficients of 0.905. Additionally, the structure of the Chinese version of the ELS was consistent with that of the original scale, and the Cronbach's α coefficients of the 3 dimensions were 0.764\u0026thinsp;~\u0026thinsp;0.881, Item-Level Content Validity Index(I-CVI) was 0.83\u0026sim;1.00, Scale-Level Content Validity Index(S-CVI) was 0.969, which can effectively and reliably evaluate the emotional labor of nurses in China [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eMental health\u003c/h3\u003e\n\u003cp\u003eThe mental health in in obstetrical nurses was measured with the Chinese version of Depression, Anxiety, and Stress Scale (DASS-21), which was developed by Lovibond [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], and devised by Gong Xu [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This scale comprises 3 dimensions, each dimension contains 7 items. All items were assessed on a 4-point Likert scale, ranging from 0(did not apply to me at all) to 3 (applied to me very much or most of the time), the total score is computed by multiplying the sum of the items by two, with a total score ranging from 0 to 126, a higher score was indicative of a worse mental health status. The internal consistency of the DASS-21 in this study was favorable, with Cronbach\u0026rsquo;s α coefficients of 0.971. Additionally, the Pearson correlation coefficient between the scores of depression, anxiety, and stress subscales and the DASS-21 scale was 0.895\u0026ndash;0.910, and the correlation coefficient between each subscale was 0.708\u0026ndash;0.741 (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), which can effectively and reliably evaluate the DASS-21[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eDescriptive statistics were calculated to characterize participant demographics, work-related variables, and mental health scores. Results were presented as numbers and percentages [n (%)] for categorical variables, as means and standard deviations (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD) for continuous variables that were normally distributed, and as the median and interquartile range[M(Q25, Q75)] for continuous variables that were not normally distributed. Latent profile analysis (LPA) was used to identify obstetrical nurses\u0026rsquo; subgroups sharing similar patterns based on emotional labor[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Models with varying numbers of latent classes were tested to find the optimal number. Following prior recommendations, we evaluated each model using several criteria: Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), adjusted Bayesian Information Criteria(aBIC), Entropy, Lo-Mendell-Rubin Likelihood Ratio Test(LMRT), and Bootstrapped Likelihood Ratio Test (BLRT). A lower value of AIC, BIC, aBIC, and a significant p-value in the LMRT and BLRT tests indicates a better model fit. Entropy ranges between 0 and 1 with values close to 1 indicating high classification accuracy[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWe investigated disparities in sociodemographic factors, work-related variables, and mental health scores among the identified emotional labor profiles. Chi-square tests were used for categorical variables, and Kruskal-Wallis H tests for continuous variables that were not normally distributed. Because some variables were potentially collinear, a robust multivariate statistical technique was selected to execute the conceptual model and perform the aforementioned ranking, namely Partial Least Squares Regression(PLSR) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], which is based on multiple regression and principal component regression, creates an optimal linear relationship between the predictors and the response specified in a conceptual model [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In the PLSR, variable importance on projection(VIP) values were calculated to reflect important variables that greatly affected emotional labor profiles. In general, VIP values above 1.0 were considered to signify important independent variables.\u003c/p\u003e\u003cp\u003eAdditionally, the general linear regression was applied to compute mean differences and 95% \u003cem\u003eCI\u003c/em\u003es for mental health across classes with two different models. Model 1 was a crude model, which was unadjusted, while Model 2 incorporated adjustments for potential confounders. These confounders were identified as factors correlated with work-related stress profiles with \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in univariate analysis, including age, marital status, number of children, personality type, work experience, level of hospital, type of hospital, professional title, employment method, night shift status, monthly night shifts, and interpersonal relationships at work. All analyses were performed with Mplus 8.3 and SPSS 26.0 software, a two-sided significance level of \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eCharacteristics of study participants\u003c/h2\u003e\u003cp\u003eOf the 1480 obstetrical nurses who participated in this study, the largest proportion, 690(46.6%), were aged 30 to 39 years.\u003c/p\u003e\u003cp\u003eA total of 1271(85.9%) held a college degree or higher, 1084(73.2%) were married, 737(49.8%) had one child, and 935(63.2%) reported middle personality type. Additionally, most nurses 788(53.2%) were from tertiary hospital Grade A hospitals(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003eSociodemographic and work-related variables, and mental health scores according to emotional labor profiles.\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\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\u003eAll samples(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1480)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;112)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMiddle(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;908)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHigh(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;460)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTest value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\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\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSociodemographic variables\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eAge, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e16.646\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18\u0026thinsp;~\u0026thinsp;29 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e524(35.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49(43.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e325(35.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e150(32.6)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e30\u0026thinsp;~\u0026thinsp;39 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e690(46.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56(50.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e425(46.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e209(45.4)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e40\u0026thinsp;~\u0026thinsp;49 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e204(13.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6(5.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e120(13.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e78(17.0)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;50 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62(4.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1(0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38(4.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23(5.0)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eEducation level, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.216\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.671\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTechnical secondary school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12(0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1(0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6(0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5(1.1)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJunior college\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e197(13.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18(16.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e122(13.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e57(12.4)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBachelor\u0026rsquo;s degree or above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1271(85.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e93(83.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e780(85.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e398(86.5)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eMarital status, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17.657\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSingle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e368(24.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40(35.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e235(25.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e93(20.2)\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\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\u003e1084(73.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70(62.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e651(71.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e363(78.9)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWidowed or separated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28(1.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2(1.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22(2.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4(0.9)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eNumber of children, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e18.569\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0 child\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e470(31.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50(44.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e297(32.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e123(26.7)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1 child\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e737(49.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52(46.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e435(47.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e250(54.3)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;2 children\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e273(18.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10(8.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e176(19.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e87(18.9)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003ePersonality type, n(%)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntroversion type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e298(20.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30(26.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e177(19.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e91(19.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.871\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e935(63.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68(60.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e595(65.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e272(59.1)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExtroversion type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e247(16.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14(12.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e136(15.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e97(21.1)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWork-related variables\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eWork experience, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e22.528\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;1 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36(2.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5(4.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22(2.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9(2.0)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u0026thinsp;~\u0026thinsp;3 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e182(12.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23(20.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e112(12.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e47(10.2)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u0026thinsp;~\u0026thinsp;6 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e221(14.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16(14.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e137(15.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e68(14.8)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u0026thinsp;~\u0026thinsp;9 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e283(19.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25(22.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e174(19.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e84(18.3)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u0026thinsp;~\u0026thinsp;15 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e433(29.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31(27.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e272(30.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e130(28.3)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;15 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e325(22.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12(10.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e191(21.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e122(26.5)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eLevel of hospital, n(%)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecondary grade B hospital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e71(4.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4(3.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44(4.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23(5.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14.650\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecondary grade Ahospital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e300(20.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16(14.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e192(21.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e92(20.0)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertiary grade B hospital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e321(21.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23(20.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e175(19.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e123(26.7)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertiary grade A hospital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e788(53.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69(61.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e497(54.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e222(48.3)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eType of hospital, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.906\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComprehensive hospital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e936(63.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71(63.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e552(60.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e313(68.0)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpecialized hospital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e544(36.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41(36.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e356(39.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e147(32.0)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eProfessional title, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13.492\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJunior\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e757(51.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e72(64.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e462(50.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e223(48.5)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntermediate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e540(36.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34(30.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e339(37.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e167(36.3)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSenior\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e183(12.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6(5.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e107(11.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e70(15.2)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eEmployment method, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.489\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePermanent contract\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e337(22.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16(14.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e204(22.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e117(25.4)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePersonnel agency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1143(77.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e96(85.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e704(77.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e343(74.6)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eMonthly income,n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.322\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.505\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;5000 RMB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36(2.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25(22.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e186(20.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95(20.7)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5000\u0026ndash;10000 RMB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e182(12.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e76(67.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e588(64.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e289(62.8)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;10000 RMB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e221(14.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11(9.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e134(14.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e76(16.5)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eOvertime hours(weekly), n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.342\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.069\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;5h\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1009(68.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71(63.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e639(70.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e299(65.0)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;5h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e471(31.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41(36.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e269(29.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e161(35.0)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNight shift status, n(%)\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\u003cp\u003e14.256\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.001\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\u003cp\u003e373(25.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18(16.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e213(23.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e142(30.9)\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\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\u003e1107(74.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e94(83.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e695(76.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e318(69.1)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eMonthly night shifts, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14.571\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e268(18.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14(12.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e151(16.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e103(22.4)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1ཞ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e294(19.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16(14.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e184(20.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e94(20.4)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5ཞ10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e693(46.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64(57.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e435(47.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e194(42.2)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e225(15.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18(16.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e138(15.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e69(15.0)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eInterpersonal relationships at work, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e98.100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot harmonious or generally harmonious\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e363(24.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50(44.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e236(26.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e77(16.7)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRelatively harmonious\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e829(56.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53(47.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e543(59.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e233(50.7)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFully harmonious\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e288(19.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9(8.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e129(14.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e150(32.6)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMental health\u003c/b\u003e M(Q25, Q75)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStress\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11(8,14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14(12,16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11(8,14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10(7,13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e65.819\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnxiety\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11(8,14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14(12,17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11(8,14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10(7,14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e74.735 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDepression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11(8,14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14(12,18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12(9,14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10(7,14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e72.46 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDASS score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33(25,42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42(36,51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33(26,42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e29(22,40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e74.536 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;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\u003cp\u003eDASS: Depression, Anxiety, and Stress Scale.\u003c/p\u003e\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Pearson\u0026rsquo;s Chi-squared test; \u003csup\u003eb\u003c/sup\u003e Fisher\u0026rsquo;s exact test; \u003csup\u003ec\u003c/sup\u003e Kruskal-Wallis H test.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eLatent profile of emotional labor among obstetrical nurses\u003c/h2\u003e\u003cp\u003eThree potential category models were fitted, and the model fit statistics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The 3-profile model exhibited comparatively more minor AIC, BIC, and aBIC values, higher entropy scores, and significant P values in the LMRT and BLRT. The scores for the three profiles are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Profile 1, designated as the \u0026ldquo;low emotional labor profile\u0026rdquo;, comprised 112(7.6%) of participants. Profile 2, characterized by moderate levels across all items, was termed the \u0026ldquo;moderate emotional labor profile\u0026rdquo;, Profile 3, referred to as the \u0026ldquo;low emotional labor profile\u0026rdquo;, accounted for 112(7.6%).\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\u003eModel fit statistics and latent profiles of the participants.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModels\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAIC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBIC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eaBIC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEntropy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLMRT \u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eBLRT \u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\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\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e56480.634\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e56650.227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e56548.573\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e48434.340\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e48694.030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e48538.372\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.977\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e68.04 / 31.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e44209.996\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e44559.783\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e44350.120\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.987\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e7.57 / 61.35 / 31.08\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e42807.307\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e43247.190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e42983.523\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.971\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.1353\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7.57 / 19.66 / 59.80 / 12.97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e41661.341\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e42191.321\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e41873.651\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.977\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.1093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.68 / 8.99 / 12.70 / 57.97 / 19.66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: AIC\u0026thinsp;=\u0026thinsp;Akaike Information Criteria, BIC\u0026thinsp;=\u0026thinsp;Bayesian Information Criteria, aBIC\u0026thinsp;=\u0026thinsp;adjusted Bayesian Information Criteria, LMRT\u0026thinsp;=\u0026thinsp;Lo-Mendell-Rubin Likelihood Ratio Test, BLRT\u0026thinsp;=\u0026thinsp;Bootstrapped Likelihood Ratio Test.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eComparisons of sociodemographic and work-related characteristics by classes\u003c/h2\u003e\u003cp\u003eThe sociodemographic and work-related variables and mental health scores of each latent profile are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Statistically significant differences were observed between profiles in terms of four sociodemographic variables (age, marital status, number of children, and personality type), eight work-related variables (work experience, level of hospital, type of hospital, professional title, employment method, night shift status, monthly night shifts, and interpersonal relationships at work) and all domains of mental health (stress, anxiety, and depression; all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). There were no statistically significant differences between profiles in terms of education level, monthly income, and overtime hours (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003eTo identify predictors of emotional labor, multivariate analyses were performed between \u0026ldquo;high emotional labor\u0026rdquo; and \u0026ldquo;low emotional labor\u0026rdquo; to assess whether sociodemographic and work-related variables differed in emotional labor across classes. PLSR analyzed the relationship between the emotional labor profiles and influencing factors. The default five latent factors of the PLSR modeling system were selected initially. Results (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) indicated that the first three potential factors have strong explanatory power for the dependent variables. As the first three latent factors extracted 46.2% of the information from the independent variables and the fourth and fifth latent factors explained less than 0.001 of the information from the dependent variable, the first three latent factors were selected for extraction in this study. In detail, there were three potential factors in which VIP values were greater than 1, namely number of children (VIP\u0026thinsp;=\u0026thinsp;1.228, 1.019, 1.005), work experience (VIP\u0026thinsp;=\u0026thinsp;1.258, 1.063, 1.048), and the interpersonal relationships at work(VIP\u0026thinsp;=\u0026thinsp;2.209, 2.823, 2.806), which were essential variables affecting the emotional labor in obstetrical nurses(Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)( Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The VIP values of age, marital status, work experience, and night shift status were greater than 0.8, suggesting that these factors also influence the emotional labor in obstetrical nurses to a certain extent. The VIP contribution values of other factors were all less than 0.8, indicating that they were not important factors.\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\u003eQuality evaluation of partial least squares regression (PLS) model of ELS and various factors\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\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003et\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eX Variance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCumulative X Variance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eY Variance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCumulative Y Variance (R\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAdjusted R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eELS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.260\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.260\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.086\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.086\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.084\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.377\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.123\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.462\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.125\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.070\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.531\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.124\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.123\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: t is the components extracted from independent variables in ELS analysis;\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\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\u003eRegression coefficient and the variable importance projection (VIP) of potential factors\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eFactor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eRegression coefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003eThe VIP value of the potential factor\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e: Age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.994\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.980\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e: Education level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.252\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.331\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.337\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e: Marital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.983\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.841\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.844\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e4\u003c/sub\u003e: Number of children\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.228\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e5\u003c/sub\u003e: Personality type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.727\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.753\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.770\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e6\u003c/sub\u003e: Work experience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.048\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e7\u003c/sub\u003e: Level of hospital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.712\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.956\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.947\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e8\u003c/sub\u003e: Type of hospital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.294\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.286\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.417\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e9\u003c/sub\u003e: Professional title\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.947\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.934\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e10\u003c/sub\u003e: Employment method\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.783\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.692\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.682\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e11\u003c/sub\u003e: Monthly income\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.415\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.442\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.542\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e12\u003c/sub\u003e: Overtime hours\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.197\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e13\u003c/sub\u003e: Night shift status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.978\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.807\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.800\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e14\u003c/sub\u003e: Monthly night shifts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.818\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.675\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.673\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e15\u003c/sub\u003e: Interpersonal relationships at work\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.279\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.209\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.823\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.806\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eAssociation of emotional labor profiles with mental health\u003c/h2\u003e\u003cp\u003eWhen exploring the association between emotional labor profiles and mental health, the results (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) showed that the high emotional labor profile exhibited negative associations across all mental health domain scores compared to the low emotional labor profile in both the crude and adjusted models. Specifically, the high emotional labor profile showed negative associations with DASS-21 scores(\u003cem\u003eβ\u003c/em\u003e=-0.172, 95%\u003cem\u003eCI\u003c/em\u003e=-4.687~-1.654), stress(\u003cem\u003eβ\u003c/em\u003e=-0.151, 95%\u003cem\u003eCI\u003c/em\u003e=-1.449~-0.426), anxiety (\u003cem\u003eβ\u003c/em\u003e=-0.171, 95%\u003cem\u003eCI\u003c/em\u003e=-1.564~-0.547), and depression (\u003cem\u003eβ\u003c/em\u003e=-4.401, 95%\u003cem\u003eCI\u003c/em\u003e=-1.703~ -0.652) after adjusting for confounders.\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\u003eThe associations between latent profile membership and mental health score using linear regression.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\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=\"\u0026minus;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\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=\"\u0026minus;\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMental health scores\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eCrude model\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eAdjusted model\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95% \u003cem\u003eCI\u003c/em\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e95% \u003cem\u003eCI\u003c/em\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStress\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.249\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-6.142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e-2.035, -1.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.151\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-3.600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c9\"\u003e\u003cp\u003e-1.449, -0.426\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnxiety\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-6.534\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e-2.119, -1.139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.171\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-4.079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c9\"\u003e\u003cp\u003e-1.564,-0.547\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDepression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.278\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-6.903\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e-2.283, -1.272\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-4.401\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c9\"\u003e\u003cp\u003e-1.703, -0.652\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDASS-21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-6.645\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e-6.411, -3.486\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.172\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-4.107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c9\"\u003e\u003cp\u003e-4.687,-1.654\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;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\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Adjusted factors: age, marital status, number of children, personality type, work experience, level of hospital, type of hospital, professional title, employment method, night shift status, monthly night shifts, and interpersonal relationships at work.\u003c/p\u003e\u003cp\u003e\u003csup\u003eb\u003c/sup\u003e CI\u0026thinsp;=\u0026thinsp;Confidence Interval.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, LPA was used to examine emotional labor among 1480 obstetrical nurses in Jiangsu Province. The analysis revealed three distinct profiles: low emotional labor profile, moderate emotional labor profile and high emotional labor profile, each defined by correspondingly low, moderate, or high levels of emotional labor. Education level, level of hospital, and interpersonal relationships at work emerged as key predictors of these profiles. Importantly, higher emotional labor profiles were consistently associated with poorer mental health outcomes.\u003c/p\u003e\u003cp\u003eThe current study employed a person-centered methodology to uncover three unique latent classes of emotional labor among obstetrical nurses, offering a perspective rarely taken in prior nursing research. Traditionally, investigations of emotional labor in nursing have relied on variable-centered designs [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. For example, a cross-sectional investigation involving 561 nurses recruited from five tertiary hospitals in China utilized exploratory factor analysis to distill emotional labor into three dimensions that together explained 62.28% of the cumulative variance contribution [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Similar studies[\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] have mapped linear relationships between emotional labor and relevant variables such as perceived organizational justice, professional identity, job performance, job stress, instrumental support, coaching leadership, and psychological costs. However, these studies are often constrained by their reliance on variable-centered methods, assuming homogeneity in populations and aiming primarily to establish connections between variables, ignoring that individuals might employ more complex combinations of deviant behaviors that do not fit neatly within the existing variable frameworks [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In contrast, the person-centered approach focuses on identifying latent subpopulations based on various observed characteristics, offering a more nuanced understanding than the variable-centered model [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Our three-profile solution diverges from the five-profile taxonomy reported by Park et al.[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] among Korean nurses\u0026mdash;non-actors, surface actors, moderators, regulators, and high regulators\u0026mdash;most likely because of the disparate emotional labor scales employed. The present findings underscore the value of moving beyond single-score averages to reveal clinically meaningful sub-populations. Such insights could aid in developing more targeted intervention and prevention strategies tailored that address the specific emotional labor patterns exhibited by different groups of obstetrical nurses.\u003c/p\u003e\u003cp\u003eTo eliminate the effects of covariance between variables, this study employed PLSR. Introduced by Wold and Abano[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] in 1983 for chemometric analyses, PLSR is designed to handle highly correlated explanatory variables and to incorporate multiple predictors simultaneously. Over the past three decades, the method has been refined theoretically and applied far beyond chemistry to fields across the natural and social sciences [\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In the present study, PLSR identified two predictors of obstetrical nurses\u0026rsquo; emotional labor: number of children and years of work experience. First, the positive effect of having children echoes findings among preschool teachers, where parenthood intensifies emotional labor demands [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Obstetric nurses occupy a uniquely multifaceted role that involves not only caring for parturients but also safeguarding the earliest moments of neonatal life [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Nurses who are parents themselves often exhibit heightened emotional sensitivity and expressiveness, allowing them to draw on personal experience to engage more naturally and empathically with patients.\u003c/p\u003e\u003cp\u003eSecond, work experience emerged as a key determinant across every dimension of emotional labor. Consistent with prior studies[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], seasoned nurses accumulate sophisticated emotional-regulation and communication competencies, enabling rapid, contextually attuned responses to clinical crises and a more nuanced appreciation of the subtleties inherent in emotional labor。\u003c/p\u003e\u003cp\u003eAdditionally, there were significant differences between the high and low emotional labor profiles based on interpersonal relationships at work. Based on the Job Demands-Resources (JD-R) model and the Conservation of Resources (COR) theory [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], supportive collegial ties function as a critical job resource that effectively buffers the negative impact of work demand. A cohesive team climate can be a potentially valuable resource for nurses to replenish resources, mitigate the impact of emotional labor concealment, thereby curb burnout and turnover intentions [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Moreover, the interpersonal relationships at work represent an organizational identification, a form of social identity defined by the degree of identification, emotional dependence, and participation of organization members, and the sense of belonging that they are members of the organization [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Strong identification transforms the workplace from a contractual setting into a \u0026ldquo;community of shared destiny,\u0026rdquo; motivating individuals to internalize the organization\u0026rsquo;s mission and to pursue its goals with volitional commitment [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Previous studies have highlighted that nurses seek identification from their organization in the process of managing the emotional demands of the profession [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. In the Chinese context, where traditional \u0026ldquo;benevolence\u0026rdquo; (ren) and societal expectations endow the nursing role with moral significance, nurses who identify closely with their organization are more inclined to engage in deep acting rather than surface acting. This internalized form of emotional regulation not only sustains authenticity but also yields favorable attitudinal outcomes, reinforcing the pivotal role of organizational identification in shaping constructive responses to emotional labor.\u003c/p\u003e\u003cp\u003eThe current study revealed a complex relationship between emotional labor profiles and mental health, revealing both potential strains and salutary effects. On one hand, emotional labor can exact a psychological toll; on the other, it may serve as a catalyst for the cultivation of emotional intelligence (EI) and, ultimately, personal growth [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. EI can be defined as the capacity to accurately perceive, appraise, and integrate internal and external emotional cues in order to guide one\u0026rsquo;s thoughts and behaviors. Nurses with high EI demonstrate greater sensitivity to shift in their patients' affects, better able to regulate their own emotional expression in response, and more adept at managing the inherent demands of emotional labor [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. When these competencies are mobilized as part of professional role requirements, they constitute emotional labor itself. Consistent with this view, prior research has highlighted that EI is significantly and positively correlated with nurses\u0026rsquo; well-being and self-efficacy, and negatively correlated with perceived stress[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Emotional labor, as a specific form of emotional regulation, entails cognitive reappraisal and is bolstered by both personal and external protective factors. Together, these elements can foster adaptive responses that restore psychological equilibrium following adverse events [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Obstetrical nurses with high emotional labor may be more willing to understand the underlying motives for patient\u0026rsquo;s behaviors and to adopt the patient\u0026rsquo;s perspective. This empathic attunement facilitates effectively self-regulation, encourages positive reframing, and nurtures genuine compassion. Consequently, emotional labor becomes a relational resource: it helps nurses establish authentic, caring connections with patients [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Such nurses typically exhibit openness, curiosity, and respect\u0026mdash;qualities that enable them to explore patients\u0026rsquo; needs and preferences more fully. By sensitively responding to these needs, they not only enhance the quality of care but also elicit appreciation and emotional support from patients and families. This reciprocal affirmation bolsters nurses\u0026rsquo; sense of professional accomplishment and reinforces adaptive emotion regulation. Moreover, the daily empathy process with patients always involves reciprocal sharing, which can help patients and relatives better understand the complexities of nursing work and provide emotional feedback[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], which improve nurses to manage their own affective states, ultimately promoting better mental health.\u003c/p\u003e\u003cp\u003eThe present study demonstrates the value of utilizing real-world data collected from a diverse range of hospitals, thereby reducing the potential for biases associated with a single-center clinical approach. Another strength is the high response and completion rates signal robust engagement among obstetrical nurses, enhancing the representativeness of the sample and the credibility of the findings. However, we must acknowledge several limitations. Firstly, the sample comprises obstetrical nurses from Jiangsu Province, and regional nuances in staffing models, organizational culture, or health-care policies may constrain the broader applicability of the results. Secondly, despite collecting various sociodemographic and work-related characteristics, there may be unmeasured factors like job satisfaction, salary satisfaction, relevant training experience, mental resilience, and workload that could impact emotional labor. Lastly, mental health outcomes were evaluated via self-report questionnaires rather than standardized interviews or clinical examinations, increasing the likelihood of under-recognition or under-reporting of clinically significant distress.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOverall, the study highlights the impact of emotional labor on the mental health of obstetrical nurses, offering a detailed insight into the nuances of emotional labor. The results indicate that the obstetrical nurses sampled in Jiangsu Province could be categorized into three qualitatively distinct profiles based on emotional labor. Membership in these profiles was predicted by factors including the number of children, work experience, and interpersonal relationships at work. These findings may facilitate the development of bespoke interventions designed to mitigate the levels of emotional labor experienced by obstetrical nurses, thereby enhancing mental health.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely thank the obstetrical nurses who participated in this study, as well as the nurse managers of hospitals and healthcare facilities who provided invaluable assistance throughout the investigation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eD.S. and C.P.: Conceptualization, Methodology, Software, Writing- original draft. J.X.: Supervision, Writing\u0026ndash; review \u0026amp; editing. L.L.: Validation, Methodology. Y.W.: Data curation, Methodology. Z.Z.: Conceptualization, Supervision, Writing- review \u0026amp; editing. C.S.: Conceptualization, Supervision, Writing- review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Chinese Medical Journals Publishing House Nursing Discipline Research Topics for 2025 (CMAPH-NRD 2022017).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available on request from the corresponding authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with Helsinki Declaration\u0026rsquo;s ethical principles, and approved by the Ethics Committee of Nanjing Maternity and Child Health Care Hospital (2022KY-090-01).\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\u003eCompeting Interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing financial interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTatum M: China\u0026apos;s three-child policy. 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BMC palliative care 2022, 21(1):23.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"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":"Emotional labor, Latent profile analysis, Mental health, Obstetrical nurses","lastPublishedDoi":"10.21203/rs.3.rs-7288519/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7288519/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eObstetrical nurses encounter a multitude of challenging circumstances, emotional labor is an indispensable component of nursing practice and impact on mental well-being significantly. This study aimed to identify different profiles of emotional labor for obstetrical nurses and explore their association with mental health.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA cross-sectional survey design using convenience sampling involved 1,480 obstetrics nurses from 32 hospitals in Jiangsu, China. The emotional labor was evaluated using the Emotional Labor Scale for Nurses, while mental health status was measured with the Depression, Anxiety, and Stress Scale. The study employed latent profile analysis to categorize obstetrical nurses into homogeneous profiles based on patterns of emotional labor, partial least squares regression(PLSR) analyses characterized the profiles in terms of sociodemographic and work-related variables, and a general linear regression model was employed to examine the relationship between mental health across different profiles.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAmong the 1,480 obstetrical nurses examined, three distinct profiles were identified: \u0026ldquo;low emotional labor profile\u0026rdquo; (n\u0026thinsp;=\u0026thinsp;112, 7.57%), \u0026ldquo;moderate emotional labor profile\u0026rdquo;(n\u0026thinsp;=\u0026thinsp;908, 61.35%), and \u0026ldquo;high emotional labor profile\u0026rdquo;(n\u0026thinsp;=\u0026thinsp;460, 31.08%). By PLSR, there were three potential factors in which VIP values were greater than 1, namely the number of children, work experience, and interpersonal relationships at work. And the emotional labor profile demonstrated a negative association with stress, anxiety, and depression.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study employs a person-centered approach to examine the emotional labor of obstetrical nurses, identifying predictors of profile membership and their relationship with mental health. These findings can inform the development of targeted interventions designed to alleviate stress and enhance the mental health of obstetrical nurses.\u003c/p\u003e","manuscriptTitle":"Person-centered exploration of emotional labor patterns, predictors, and their association with mental health in obstetrical nurses: a multicenter cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-22 09:57:48","doi":"10.21203/rs.3.rs-7288519/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-09-16T11:18:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"37966901767277314182009764447861869466","date":"2025-09-13T22:41:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"302095441146703102232192953940676451875","date":"2025-09-12T00:26:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-11T17:14:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-09T14:21:27+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-18T03:38:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-14T01:18:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nursing","date":"2025-08-14T01:15:11+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":"c4778c79-be8a-4c0b-bf2b-50dc37492771","owner":[],"postedDate":"September 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-09-22T09:57:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-22 09:57:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7288519","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7288519","identity":"rs-7288519","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}