Abstract
Background
Psychiatric nurses experience unique emotional challenges, requiring management of both their own emotions and those of patients with mental health disorders. Emotional labor, particularly surface acting, has been identified as a significant stressor contributing to burnout. This study examines how emotion regulation strategies might moderate the relationship between emotional labor and burnout.
Methods
This cross-sectional study recruited 244 psychiatric nurses. Participants completed questionnaires assessing emotional labor, emotion regulation strategies, and burnout. Multiple regression and moderation analyses were performed.
Results
Surface acting was positively associated with burnout (β = 0.44, p < 0.001), while natural expression (β = -0.19, p = 0.02) was negatively associated with burnout. Cognitive reappraisal was negatively associated with burnout (β = -0.44, p < 0.001), while expressive inhibition was positively associated with burnout (β = 0.15, p = 0.04). Furthermore, cognitive reappraisal significantly moderated the relationship between surface acting and burnout (β = -0.57, p = 0.01). Simple slope analysis revealed that the association between surface acting and burnout was strongest at low levels of cognitive reappraisal (β = 0.50, p < 0.001) and weakest at high levels (β = 0.22, p = 0.004), becoming non-significant when cognitive reappraisal exceeded a standardized value of 1.38.
Conclusion
Cognitive reappraisal serves as a protective factor against the detrimental effects of surface acting on burnout among psychiatric nurses. Interventions should focus on minimizing reliance on surface acting while enhancing cognitive reappraisal skills through mindfulness-based techniques, potentially improving resilience in emotionally demanding environments.
Keywords: Emotional labor, Surface acting, Burnout, Cognitive reappraisal, Psychiatric nurses, Emotion regulation
Introduction
Psychiatric nurses encounter distinct emotional challenges that differentiate their work from other healthcare domains. They must manage not only their own emotions but also navigate the complex emotional states of patients with mental health disorders, often in high-stress environments with limited resources [1]. This continuous emotional management, conceptualized as emotional labor, is recognized as a substantial occupational stressor contributing to burnout in this population [2]. Emotional labor in psychiatric nursing primarily involves three components: surface acting (displaying emotions not genuinely felt), deep acting (modifying inner feelings to align with required expressions), and natural expression (spontaneously expressing genuine emotions) [3]. Psychiatric nursing settings necessitate particularly high levels of emotional labor, as practitioners must consistently maintain therapeutic environments while managing their own emotional responses.
Burnout, a syndrome, characterized by emotional exhaustion, cynicism, and diminished professional efficacy, is a state of physical, emotional, and mental depletion resulting from prolonged exposure to occupational stressors. Among healthcare professionals, burnout is linked to decreased quality of care, increased medical errors, higher turnover rates, and poorer physical and mental health outcome [4]. The prevalence of burnout among psychiatric nurses has been reported to be particularly high compared to other nursing specialties, with rates ranging from 27.3 to 59.8% across different studies [5, 6]. A robust body of nursing literature has documented the association between emotional labor and burnout [7, 8]. Surface acting has been consistently linked to elevated burnout [9], whereas the relationship between deep acting and burnout appears more variable, with some studies suggesting potential protective effects [10].
Emotion regulation strategies, which serve as potential moderator, encompasses processes through which individuals influence the emotions they have, when they experience them, and how they express these emotions [11]. Cognitive reappraisal, an antecedent-focused strategy enacted prior to full emotional arousal, is linked to improved psychological outcomes, including reduced negative affect and enhanced interpersonal functioning [12, 13]. Conversely, expressive inhibition, a response-focused strategy enacted prior to full emotional arousal, is linked to improved psychological outcomes, including reduced negative affect and enhanced interpersonal functioning [14].
Despite increasing interest in the role of emotion regulation in occupational well-being, few studies have investigated how these strategies might moderate the relationship between different dimensions of emotional labor and burnout among psychiatric nurses. This represents a critical gap, particularly given the emotional intensity nature of psychiatric care. Understanding these relationships could inform interventions aimed at reducing burnout and promoting well-being in this population. The present study aims to address these gaps by investigating the relationships between emotional labor, emotion regulation strategies, and burnout among psychiatric nurses. Specifically, this study explore: (1) the direct relationship between emotional labor and burnout; (2) the direct relationship between emotion regulation strategies and burnout; and (3) whether emotion regulation strategies moderate the effects of emotional labor on burnout.
Method
Participants
Participants were recruited in June 2024 from a psychiatric hospital in Wenzhou City. The eligibility criteria included: (1) being a certified nurse currently employed at the institution; (2) having sufficient cognitive ability and literacy in Chinese to understand and complete the questionnaire; and (3) providing signed informed consent prior to participation. Individuals were excluded if they met any of the following conditions: (1) on long-term leave or recently resigned; (2) had a documented history of serious mental disorders that could impair cognitive functioning; (3) declined to complete the questionnaire or voluntarily withdrew from the study; or (4) demonstrated inconsistent or suspicious response patterns suggestive of inattentive answering. A total of 245 nurses were initially recruited, 244 completed the survey.
The priori power analysis was conducted using G*Power 3.1 to decide the minimum required sample size for multiple linear regression analysis. Based on conventional parameters for a medium effect size (f² = 0.15), an alpha level of 0.05, and a desired statistical power of 0.80, with 11 predictors included in the model, the analysis showed that a sample size of at least 123 participants was required. Our final sample of 244 nurses exceeded this threshold, providing sufficient statistical power to detect medium-sized effects in the regression models.
Procedures
Data was collected through an electronic questionnaire platform. A QR code linking to the online survey was created and distributed via posters. Head nurses in each ward were tasked with identifying staff members who met the inclusion criteria. Before the formal launch of the survey, designated nursing staff from each ward received standardized training to ensure a clear understanding of all questionnaire items. This preparatory step was designed to reduce response bias and enhance the reliability of the collected data. Eligible participants were invited to complete the questionnaire by scanning the QR code in a quiet setting.
Measurements
Demographic information
Demographic data were collected using a self-administered questionnaire. The questionnaire included items assessing participants’ gender (male/female), age (in years), educational years (total years of formal education), and occupational years (total years of professional work).
Emotion labor
Emotional labor was assessed using the Emotional Labor Scale (ELS) [15]. This scale includes 14 items, divided into three dimensions: surface acting (e.g., faking or suppressing emotions), deep acting (e.g., modifying feelings to match expected displays), and natural expression (e.g., naturally felt emotions). Each item is rated on a 5-point Likert scale ranging from 1 (never) to 5 (always), with higher scores indicating greater emotional labor. Cronbach’s α in the present study were 0.91 for surface acting, 0.68 for deep acting, and 0.76 for natural expression.
Burnout
Burnout was assessed using the Chinese version of the burnout scale revised by Chaoping et al. [16]. This scale consists of 15 items, each item is rated on a 7-point Likert scale, ranging from 0 (never) to 6 (daily). A total burnout score was calculated as the arithmetic sum of the three subscale scores. Cronbach’s α coefficients in the current study was 0.96.
Emotion regulation strategy
Emotion regulation strategy was measured using the Emotion Regulation Questionnaire (ERQ) [17]. The ERQ is a 10-item self-report scale designed to assess individuals’ habitual use of two emotion regulation strategies: cognitive reappraisal and expressive inhibition. Participants rated each item on a 7-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree), with higher scores indicating greater use of the corresponding strategy. Internal consistency was acceptable, with Cronbach’s α values of 0.93 for cognitive reappraisal and 0.90 for expressive inhibition in current study.
Statistical analysis
All statistical analyses were conducted using SPSS 28.0. Visualization was conducted by R 4.4.1. Descriptive statistics were first calculated to summarize demographic characteristics and main study variables. The normality of distributions was evaluated using skewness and kurtosis criteria, with|skewness| < 2 and|kurtosis| < 7 considered acceptable [18].
Line regression analysis was performed to examine the associations between emotional labor dimensions (surface acting, deep acting, and natural expression), emotion regulation strategies (cognitive reappraisal and expressive inhibition), and burnout. The variance inflation factor (VIF) was calculated to assess multicollinearity, with values below 10 considered acceptable [19]. Moderation effects were tested by creating interaction terms between the significant emotional labor and emotion regulation strategies. The regression proceeded in three steps: (1) demographic variables were entered as covariates; (2) main effects of emotional labor and emotion regulation strategies were added; and (3) interaction terms between the relevant emotional labor dimension and each emotion regulation strategy were included. All continuous variables were mean-centered to minimize multicollinearity.
For significant interactions, simple slope analyses were conducted to probe the nature of moderation at low (− 1 SD), medium(mean), and high (+ 1 SD) levels of the moderator. The Johnson–Neyman technique was applied to identify the specific range of the moderator where the focal predictor had a statistically significant effect on burnout. A two-tailed p < 0.05 was considered statistically significant.
Result
Descriptive statistics
The descriptive characteristics of the participants are summarized in Table 1. A total of 244 nurses participated in this study. Participants were predominantly female (92.21%) with a mean age of 30.43 years (SD = 5.91) and average occupational experience of 8.36 years (SD = 6.16). The majority (80.74%) held bachelor’s degrees, with 17.21% completing junior college and 2.05% with high school education or below. For the main study variables, the descriptive analysis also showed in Table 1. Examination of skewness and kurtosis values for all study variables (as detailed in Table 1) indicated that the data were approximately normally distributed (|skewness| < 2,|kurtosis| < 7). The skewness values ranged from − 0.56 to 3.82, and kurtosis values ranged from − 0.48 to 1.64.
Table 1.
Demographic characteristics and main study variables (n = 244)
| Variables | M ± SD | n (%) | Kurtosis | Skewness |
|---|---|---|---|---|
| Demography | ||||
| Age | 30.43 ± 5.91 | 1.36 | 2.63 | |
| Gender | ||||
| Male | 19(7.79%) | |||
| Female | 225(92.21%) | |||
| Educational level | ||||
| High school and below | 5(2.05%) | |||
| Junior college | 42(17.21%) | |||
| Bachelor’s degree | 197(80.74%) | |||
| Occupational years | 8.36 ± 6.16 | 1.64 | 3.82 | |
| Emotion labor | ||||
| Surface acting | 21.65 ± 5.72 | -0.07 | 0.46 | |
| Deep acting | 13.88 ± 2.75 | -0.17 | 0.97 | |
| Natural expression | 10.33 ± 1.91 | 0.29 | 1.03 | |
| Emotion regulation | ||||
| Cognitive reappraisal | 30.39 ± 6.26 | 0.32 | -0.56 | |
| Expressive inhibition | 17.22 ± 5.02 | 0.23 | 0.32 | |
| Burnout | 47.30 ± 14.37 | -0.48 | -0.51 |
Regression analysis
Multiple linear regression analysis was conducted to examine the effects of emotional labor and emotion regulation strategies on burnout, while controlling for demographic variables. As shown in Table 2, surface acting (β = 0.44, p < 0.001) and cognitive reappraisal (β = -0.44, p < 0.001) were significant influence factor of burnout. Additionally, natural expression (β = -0.19, p = 0.02) and expressive inhibition (β = 0.15, p 0.04) were also significantly associated with burnout, though in differing directions. Deep acting was not a significant predictor (β = -0.12, p = 0.18). Among the control variables, none reached statistical significance. The overall model was great (F = 16.91, p < 0.001), with adjusted-R² = 0.37, indicating that approximately 37% of the variance in burnout was explained by the model. Variance inflation factors (VIFs) were all below 10, suggesting no severe multicollinearity.
Table 2.
Linear regression analysis about burnout, emotional labor, emotion regulation, and demographic variables
| Model | β | SE | t | P | VIF | |
|---|---|---|---|---|---|---|
| Independent Variable | Emotion labor | |||||
| Surface acting | 0.44 | 0.08 | 5.32 | < 0.001 | 2.64 | |
| Deep acting | -0.12 | 0.09 | -1.36 | 0.18 | 2.88 | |
| Natural expression | -0.19 | 0.08 | -2.42 | 0.02 | 2.28 | |
| Emotion regulation | ||||||
| Cognitive reappraisal | -0.44 | 0.07 | 6.20 | < 0.001 | 1.94 | |
| Expressive inhibition | 0.15 | 0.07 | 2.05 | 0.04 | 2.06 | |
| Control Variables | Age | 0.05 | 0.14 | 0.37 | 0.71 | 7.23 |
| Gender | 0.06 | 0.05 | 1.05 | 0.29 | 1.10 | |
| Educational years | -0.10 | 0.05 | 1.87 | 0.06 | 1.03 | |
| Occupational years | 0.11 | 0.14 | 0.80 | 0.42 | 7.32 | |
| F | 16.91*** | |||||
| Adjusted-R 2 | 0.37 | |||||
Note. All beta values presented in the manuscript are standardized coefficients. ***p < 0.001
Moderation analysis
Hierarchical regression analyses were conducted to examine the moderate effect of emotion regulation. According to the result of the linear regression, surface acting and natural expression were included in the moderation analyses, as these two aspects of emotional labor were significant influences on burnout. Specifically, interaction terms were constructed between surface acting and emotion regulation (i.e., cognitive reappraisal, expression inhibition), and between natural expression and the same two regulation strategies. As showed in Table 3−1, surface acting (β = 0.29, p < 0.001), cognitive reappraisal (β = -0.58, p < 0.001), and expressive inhibition (β = 0.18, p = 0.02) were added to the model, significantly increasing the explained variance (adjusted R² = 0.31). The interaction between surface acting and cognitive reappraisal was significant (β = -0.57, p = 0.01), while the interaction between surface acting and expressive inhibition was not significant (β = 0.05, p = 0.79). The addition of interaction terms contributed an additional 3% to the explained variance (adjusted R² = 0.03). The moderator model of natural expression showed in Table 3−2, neither the interaction between natural expression and cognitive reappraisal (β = -0.08, p = 0.35) nor the interaction between natural expression and expressive inhibition (β = 0.02, p = 0.85) was significant.
Table 3−1.
Hierarchical regression testing emotion regulation as moderator of surface acting on burnout
| Variables | β | SE | t | P | |
|---|---|---|---|---|---|
| Step I | Age | 0.18 | 0.17 | 1.06 | 0.29 |
| Gender | -0.10 | 0.06 | -1.59 | 0.11 | |
| Educational years | -0.11 | 0.07 | 1.74 | 0.08 | |
| Occupational years | -0.30 | 0.17 | -1.80 | 0.07 | |
| Step II | Surface acting | 0.29 | 0.07 | 4.16 | < 0.001 |
| Cognitive reappraisal | -0.58 | 0.06 | 9.94 | < 0.001 | |
| Expression inhibition | 0.18 | 0.07 | 2.37 | 0.02 | |
| Step III | Surface acting × Cognitive reappraisal | -0.57 | 0.23 | -2.47 | 0.01 |
| Surface acting × Expression inhibition | 0.05 | 0.19 | 0.27 | 0.79 |
Table 3−2.
Hierarchical regression testing emotion regulation as moderator of natural expression on burnout
| Variables | β | SE | t | P | |
|---|---|---|---|---|---|
| Step I | Age | 0.18 | 0.17 | 1.06 | 0.29 |
| Gender | -0.10 | 0.06 | -1.59 | 0.11 | |
| Educational years | -0.11 | 0.07 | 1.74 | 0.08 | |
| Occupational years | -0.30 | 0.17 | -1.80 | 0.07 | |
| Step II | Natural expression | -0.08 | 0.07 | -1.10 | 0.27 |
| Cognitive reappraisal | -0.54 | 0.07 | -7.76 | < 0.001 | |
| Expression inhibition | 0.37 | 0.06 | 5.97 | < 0.001 | |
| Step III | Natural expression × Cognitive reappraisal | -0.08 | 0.09 | -0.94 | 0.35 |
| Natural expression × Expression inhibition | 0.02 | 0.09 | 0.19 | 0.85 |
Sample slope analysis
To further interpret the significant interaction between surface acting and cognitive reappraisal identified in the moderation analysis, a simple slope analysis was conducted. As shown in Table 4; Fig. 1-A, the simple slope analysis revealed that the positive association between surface acting and burnout was significant at all three levels of cognitive reappraisal. However, the strength of this relationship decreased as the level of cognitive reappraisal increased. Specifically, the effect of surface acting on burnout was strongest at low levels of cognitive reappraisal (β = 0.50, p < 0.001, 95% CI [0.29, 0.71]), moderate at medium levels (β = 0.36, p < 0.001, 95% CI [0.21, 0.71]), and weakest at high levels (β = 0.22, p = 0.004, 95% CI [0.07, 0.37]). Johnson–Neyman plot also reveal the correlation of surface acting on burnout became non-significant when the standardized value of cognitive reappraisal exceeded 1.38, which corresponds to a raw score of 39 in this study. This indicates that individuals with high levels of cognitive reappraisal (> 39) were protected from the adverse impact of surface acting on burnout.
Table 4.
Simple slope analysis on different levels of cognitive reappraisal
| Cognitive reappraisal | Effect | SE | t | p | 95%CI |
|---|---|---|---|---|---|
| Low (-1 SD) | 0.50 | 0.11 | 4.59 | < 0.001 | [0.29,0.71] |
| Medium (Mean) | 0.36 | 0.08 | 4.81 | < 0.001 | [0.21,0.71] |
| High (+ 1 SD) | 0.22 | 0.08 | 2.95 | 0.004 | [0.07, 0.37] |
Fig. 1.
Sample slope analysis of cognitive reappraisal on the relationship between surface acting and burnout. (A) Simple slope analysis illustrates the relationship between surface acting and burnout at low (− 1 SD), Medium (mean), and high (+ 1 SD) levels of cognitive reappraisal. (B) Johnson–Neyman plot identifying the region of significance for the moderating effect of cognitive reappraisal. The association between surface acting and burnout becomes non-significant when the standardized cognitive reappraisal score exceeds 1.38 (corresponding to a raw score of 39)
Discussion
This study investigated the relationships among emotional labor, emotion regulation strategies, and burnout in a sample of psychiatric nurses in China. Surface acting was positively associated with burnout, whereas natural expression was negatively associated. Deep acting did not show a significantly relationship with burnout. Regarding emotion regulation, cognitive reappraisal was significantly negatively correlated with burnout, expression inhibition was positively associated. Notably, significant interaction was oberserved between surface acting and cognitive reappraisal. Further analysis using simple slope and johnson–neyman analyses revealed that the positive association between surface acting and burnout was attenuated at higher levels of cognitive reappraisal.
Emotional labor and burnout
The positive association between surface acting and burnout aligns with previous research across various healthcare settings. A meta-analysis by Hülsheger and Schewe (2011), which synthesized data from 95 international studies, demonstrated that surface acting consistently predicts emotional exhaustion across diverse cultural settings and healthcare systems. Similar result have been reported in Korean samples, Park et al. (2022) observed that nurses exhibiting surface acting tendencies were more vulnerable to burnout [20]. According to the Conservation of Resources theory, individuals seek to acquire, maintain, and protect their psychological and emotional resources. When these resources are depleted or threatened—such as through persistent emotional dissonance arising from surface acting—individuals become increasingly susceptible to stress and burnout [21]. Surface acting requires psychiatric nurses to suppress authentic emotions in favor of organizationally sanctioned displays, a process that demands significant psychological effort [22]. This emotional dissonance, characterized by the discrepancy between felt and displayed emotions, is considered a key driver of emotional exhaustion, a central component of burnout [23]. In contrast, deep acting was not identified as a significant influence factor of burnout in this study. This finding aligns with previous literature report mixed outcomes and may be attributed to emerging theoretical perspective suggesting that deep acting is not a unitary construct [24]. Mann et al. (2024) proposed that deep acting encompass two distinct emotion regulation strategies: engagement-focused regulation (e.g., cognitive reappraisal) and disengagement-focused regulation (e.g., distraction or attentional deployment). These strategies have divergent consequences—engagement-focused strategies tend to be adaptive and linked to reduced burnout, whereas disengagement-focused strategies are effort-intensive and may ultimately increase the risk of burnout over time [24].
Moderating effect of cognitive reappraisal
The most noteworthy finding of our study was the moderating effect of cognitive reappraisal on the relationship between surface acting and burnout. The results indicate that the positive association between surface acting and burnout diminished as the level of cognitive reappraisal increased, suggesting that cognitive reappraisal may function as a protective factor against the detrimental effects of surface acting. For example, Holman and Jignea (2023) demonstrated that individuals with greater cognitive reappraisal ability were better able to mitigate the emotional consequences of interpersonal stressors, thereby enhancing psychological resilience [25]. As an antecedent-focused strategy, cognitive reappraisal enables individuals to reframe emotionally challenging situations before emotional responses are fully activated, thus reducing the perceived burden of surface acting. This finding is consistent with results from general occupational settings [26, 27], and support the notion that cognitive reappraisal serves as a psychological buffer in emotionally demanding roles [25]. For instance, Riepenhausen et al. (2022) found that worker who frequently employed cognitive reappraisal reported lower levels of emotional exhaustion and greater resilience across diverse professions [26]. This moderating effect can be interpreted through the framework of the Job Demands-Resources model, which posits that occupational stress arises from an imbalance between job demands (such as emotional labor) and job resources (including personal coping resources like emotion regulation strategies) [28]. While surface acting represents a demand that drains psychological resources, cognitive reappraisal serves as a resource that facilitates the reinterpretation of emotionally taxing encounters in a less stressful manner [29]. The Johnson-Neyman plot further revealed that the adverse impact of surface acting on burnout became non-significant when standardized cognitive reappraisal score exceeded 1.38 (equivalent to a raw score of 39), indicating a potential threshold effect. This finding provides novel empirical support for threshold effects in emotion regulation, extending previous theoretical propositions by Sun and Lin (2017), who described similar non-linear patterns in nurses [30]. Identifying this specific threshold has important practical implications, suggesting that targeted interventions to enhance cognitive reappraisal may be particularly beneficial for psychiatric nurses frequently engaged in surface acting.
Implications for practice
This study yields several important implications for clinical nursing management. First, efforts to mitigate burnout among psychiatric nurses should prioritize reducing reliance on surface acting, potentially through structured team debriefings or by fostering emotionally authentic communication. Second, training programs focused on developing cognitive reappraisal skills—such as mindfulness-based stress reduction or cognitive-behavioral techniques—may strengthen nurses’ resilience by equipping them with reinterpret emotionally demanding situations. Finally, increased awareness of the maladaptive effects of expressive inhibition should inform institutional policies and psychosocial support systems aimed at promoting healthy emotional expression in clinical environments.
Limitation
Several limitations should be noted. First, the cross-sectional design limits causal inference, and longitudinal studies are needed to validate the directionality of observed relationships. Second, all variables were assessed via self-report, which may be susceptible to social desirability bias or shared method variance. Third, although the study examined key emotion regulation strategies, other relevant constructions such as emotional intelligence, psychological flexibility, or organizational support were not included. Future research should employ longitudinal or experimental designs to further explore the dynamic interplay between emotional labor, emotion regulation, and burnout over time.
Acknowledgements
We extend our heartfelt gratitude to each person in charge of nursing for their valuable support and contribution to this study during the data collection process.
Abbreviations
- ELS
Emotional Labor Scale
- ERQ
Emotion Regulation Questionnaire
Author contributions
Suilin Jia, Shasha Hong and Guanghui Shen: Formal analysis, Writing – original draft, Writing – review & editing. Guanghui Shen, Suilin Jia and Tingting Jin: Writing – review & editing, Investigation, Conceptualization. Tingting Jin, Shasha Hong and Jie Liu: Investigation, Methodology, Writing – original draft. Guanghui Shen and Tingting Jin: Revision of manuscript.
Funding
This work was supported by Wenzhou Municipal Science and Technology Bureau (Grant No. Y20240046).
Data availability
The datasets presented in this article are not readily available due to the inclusion of individual-specific information. Requests to access the datasets should be directed to shenguanghuipsy@163.com.
Declarations
Ethics approval and consent to participate
The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. Written informed consent to participate was obtained from all participants prior to their inclusion in the study. All procedures involving human subjects/patients were approved by IRB in Wenzhou Seventh People’s Hospital (No. 202402). All procedures were performed in accordance with relevant guidelines from declaration of Helsinki statement.
Consent for publication
No applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Linhui Liu, Email: 18357879673@163.com.
Suilin Jia, Email: jsl307015@163.com.
Guanghui Shen, Email: shenguanghuipsy@163.com.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets presented in this article are not readily available due to the inclusion of individual-specific information. Requests to access the datasets should be directed to shenguanghuipsy@163.com.