The influence of chronic stress reflected in demographic characteristics on perceived stress and depression––Evidence from hospital employees

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However, recent research has suggested that perceived stress —the extent to which a person feels stressed—is more closely related to depression. In line with this notion, individual differences in perceived stress are expected to be more strongly linked to depression. However, little is known about how these individual differences influence depression. Method This study investigated whether demographic characteristics can alter the relationship between perceived stress and depressive severity by using a sample of hospital employees. Results It was found that perceived stress was positively related to depression ( r < .67); however, only a causal relationship from demographic characteristics to perceived stress was identified. The results of the best-fitting model (H-model) indicated that self-efficacy tends to decline with aging, whereas distress increases among individuals who are tagged by special characteristics “female” and “having marital experience”. Conclusions These findings indicated that individual differences in perceived stress may be due to chronic stress environments and, in turn, adversely affect depression severity, highlighting the crucial role of perceived stress on the onset of depression. perceived stress depression demographic characteristics chronic stress hospital employees Figures Figure 1 Introduction Depression, clinically referred to as depressive disorder, appears to be a byproduct of the rapid development of postmodern society. In China, depression has become prevalent across generations and socio-economic statuses [ 1 ] , and it generates a variety of public health issues [ 2 ] . In response to this situation, a substantial body of research has been conducted to identify key predictors of depression and to examine the impact of critical factors on its progression, thereby contributing to early detection and prevention. However, due to the complex pathogenesis of depression, even some fundamental questions remain unresolved. It is well known that stress and depression are closely related. In the Selye’s theoretical framework of Stress [ 3 ] , when a stressful life event occurs, subject is exposed to stress, which causes both mental and physical responses. If stress accumulates and cannot be relieved, subject’s mind and body may break down and become unable to adapt to environmental stressors. For this reason, depression is considered to be the result of maladaptation to environmental stressors [ 4 ] . The question of how stress leads to the onset of depression is somewhat old, but in fact there has been little direct evidence showing that there is a causal relationship between them. In addition, in recent years, it has been pointed out that stress and depression affect each other [ 5 , 6 ] . Therefore, in this study, we focused on the relationship between stress and depression and aimed to examine how demographic characteristics affect their relationships. Unidirectional or bidirectional? The still-debated link between stress and depression A strong relationship between stress and depression has been well-established by numerous studies. Evidence indicates that the occurrence of stressful life events [ 7 ] , chronic stress induced by exposure to stressors [ 8 ] , the experience of early life stress [ 9 ] are powerful predictors of the impending onset of depression. Similarly, it has been found that depressed individuals report more psychological stress than those who do not [ 10 , 11 ] . This relationship is robust, with consistent correlations observed no matter in the general population [ 12 , 13 ] or in specific occupational groups [ 11 , 14 ] . These correlational studies demonstrate stress appears to be associated with depression; however, there is still an insufficient understanding of the pathway linking environmental stressors to the onset of depression. This gap might be filled by related works that explore how the internal physiological processes related to depression are mediated by external socio-environmental stress [ 15 , 16 ] . Many, but not all, findings support the inference that stress-induced alterations in neurotrophic factor expression may contribute to the development of depression. Additionally, psychological stress can trigger a significant increase in inflammatory activity [ 17 ] (i.e., in the absence of physical injury; Glaser & Kiecolt-Glaser, 2005), and is negatively associated with susceptibility to the immune-related disease(e.g., common cold) [ 18 ] . These findings suggest that stress can have effects on the regulation of immune and inflammatory processes, offering a plausible pathway through which stress affects depression. This body of evidence undoubtedly points to stress as a contributing factor to depression, and researchers believe that stress should be a causal factor in the development of depression. Despite the fact that stress-related factors do predict the risk of becoming depressed, not all individuals develop depressive disorders after encountering stressful life events. This compels researchers to realize the necessary of explaining the relationship between stress and depression in a more nuanced manner. The influence of gene phenotypes was first considered a potential explanation for this paradoxical phenomenon [ 19 ] . It was supported by the finding that individuals with SS alleles (vs individuals with SL, LL alleles) were more sensitive to the depressogenic effects of low-threat stressful life events, suggesting heightened sensitivity to stress may be a causal factor in the depressive onset [ 20 ] . Similarly, a substantial overlap between the genetic influences on stress sensitivity and depressive symptoms has been evidenced [ 5 ] , leading to the assumption that abnormalities in stress sensitivity may be one of the early manifestations of depression. This gives rise to a further adventurous assumption: that a transient negative mood state induces a decrease in the threshold for stress sensitivity, thereby causing intense stress responses, and ultimately leading to the onset of depression. Indeed, it has been found that depressive individuals (i.e., unipolar women) are more sensitive to interpersonal conflict and consequently experience more stressful life events [ 21 ] . Moreover, the cortisol levels of depressive patients were found to recover more slowly after exposure to a stress stimulus compared to those of healthy individuals [ 22 ] . Although these findings alone cannot provide definitive evidence of an inverse relationship between stress and depression, they do suggest that the relationship is more bidirectional than unidirectional. In this context, individual differences in stress sensitivity may play a more crucial role than the stressful life events themselves on depression [ 6 , 16 ] . The influence of socio-demographic characteristics on perceived psychological stress Perceived psychological stress refers to the extent to which an individual consciously experiences stress and is most often assessed using subjective scales [ 23 – 26 ] . Given that the more vivid and intense the perceived stress is — to the same stressor or to different stressors of the same intensity — the greater the risk of developing depression, understanding the factors that influence perceived stress is of particular importance. Socio-demographic characteristics (e.g., age, sex, marital status, educational level, occupation, socio-economic status, and even race in some cases), which reflect a person’s broader social context, have attracted considerable attention due to their potential to provide insight into how perceived stress changes when certain characteristics are attached. Among these, gender has been suggested to be the most salient and robust factor in perceiving stress [ 13 , 27 – 28 ] . Evidence indicates that females report greater psychological stress than males in a large sample of general university students [ 12 ] (n = 1617) as well as in a smaller sample of medical students [ 29 ] ( β = 2.47, p = .03, n = 80). This gender difference has remained consistent over time [ 27 ] . However, although gender can reflect aspects of the social environment (e.g., sexism), it has been proposed that gender differences in perceived stress may stem from genetic rather than social influences. A recently published paper suggested that greater sensitivity to stress among women may be attributed to differences in physiological structure between the sexes [ 6 ] . The authors highlighted a growing body of evidence indicating depression may be caused by dysfunction in the hormonal systems that normally protect the body from damage resulting from excessive inflammatory responses [ 30 , 31 ] . Additionally, it has been found that women report greater social stress (e.g., loneliness, social disconnection) and more pronounced emotional changes in response to experimentally induced inflammatory responses [ 32 ] . Accordingly, it has been suggested that gender differences in both perceived stress and depression may stem from a reduced ability of cortisol to suppress inflammatory responses associated with regular fluctuations in female-specific sex hormones [ 33 ] . Therefore, the authors suggested that female-specific physiological changes in response to inflammation may underlie gender disparities in perceived stress and the higher prevalence of depression among women. On the other hand, a smaller body of evidence suggests that other demographic characteristics may increase susceptibility to stress. It has been found that job function (i.e., type of ward, type of shift) is associated with elevated psychological stress among nurses [ 14 ] . In a sample of medical undergraduate students, factors related to interpersonal stress and social rejection [ 6 ] (e.g., personal income, student score, social activity and transportation to faculty) were linked to increased scores in depression, anxiety, and stress, whereas marital status was associated only with increased stress scores [ 28 ] . However, the influence of demographic characteristics was less pronounced when examining the general population. One study found the directs effect of demographic characteristics on depression, anxiety, stress (as assessed by the DASS-21) was very modest( r s < 0.2) [ 23 ] . Indeed, demographic characteristics are often considered merely one facet of individual differences and are treated as control factors in some studies [ 34 ] . Although a broader explanation for these discrepancies may lie in subject-specific variations, these findings nonetheless suggest a potential pathway by which environmental stressors, as reflected in demographic characteristics, affect the development of depression—an issue that has received limited analytical attention and debate regarding its validity. In fact, an alternative explanation for how demographic characteristics can influence perceived stress has already been proposed [ 35 ] . It posits that demographic characteristics may predict the risk of depression due to their association with chronic stress and allostatic load. In support of this, evidence indicates a low level of education can predict the risk of depression, which is attributed to the strong causal link between lower educational level and low SES in developed countries [ 36 ] . More direct evidence was provided by a study focusing on variations in stress according to socio-demographic characteristics among individuals from low SES backgrounds (i.e., residents of deprived neighborhoods), which found that certain demographic characteristics (e.g., age, gender, ethnicity, loneliness) can influence the perception of stress [ 37 ] . Considering that the influence of these characteristics is likely to be long-term and persistent, the authors concluded that chronic stress may be the latent yet central factor embedded within socio-demographic characteristics. Thus, one can speculate that demographic characteristics, at least in part, may reflect chronic stress and therefore contribute to heightened sensitivity to stress, though they may not directly promote the development of depression. The objective of current study Although stressful experiences primarily contribute directly to the onset of depression, they are not a prerequisite for its development [ 16 ] ; recent studies have suggested that perceived stress—traditionally treated as an individual difference—is also an important factor in the onset of depression. However, questions such as how social and environmental conditions give rise to chronic stress, and how demographic characteristics reflecting these conditions moderate the relationship between perceived stress and depression have yet to be fully clarified. The current study sought to address these questions by investigating perceived stress and depression in a specific sample: hospital employees. Although still limited, some studies have reported that demographic characteristics (e.g., sex, age, occupational function, and family composition status) influence perceived stress. Therefore, we expected these characteristics to also be associated with perceived stress in the current sample. Furthermore, if characteristics more closely associated with chronic stress, such as occupational function and marital status, are found to be related only to an increase in perceived stress but not to an increase in depression, this would support the hypothesis that increased perceived stress due to chronic stress caused by socio-environmental factors contributes to the onset of depression. Accordingly, if our hypothesis is correct, it should be supported by the model comparisons, whereby a structural equation model indicating this causal relationship would demonstrate a better fit than one indicating alternative causal relationships. Method Ethical Statement The studies involving human participants were reviewed and approved by Ethics Committee of Wuhan Hankou Hospital (hyll2023060). Written Informed consent was obtained from all patients/participants after a full explanation of the nature of the study and possible risks, and all methods were performed according to the relevant guidelines and regulations. Measurements Patient Health Questionnaire 9-Items The Patient Health Questionnaire 9 Items(PHQ-9) [38] is a well-validated, self-report questionnaire used to assess depression in the variety of settings, particularly in primary care. Its validity and reliability have also been confirmed in the general population [39] . Each item (e.g., Little interest of pleasure in doing things) reflects a typical manifestation of depression and is rated on a 4-point Likert scale (i.e., 0 = not at all, 1 = several days, 2 = more than half the days, 3 = nearly every day). Participants were instructed to recall whether they had experienced these symptoms over the last two weeks. The total PHQ-9 score ranges from 0 to 27, and is typically divided into five categories of increasing severity (0–4, 5–9, 10–14,15–19, 20 and greater). However, these cut-ff scores were not employed due to the objectives of this study. The obtained scores indicated that the internal reliability of the PHQ-9 was high: Cronbach’s α = 0.89. Perceived Stress Scale 14-Items The 14-item Perceived Stress Scale(PSS-14) [24, 25] is widely used to assess perceived stress levels over a period of time, typically in general populations. The PSS-14 includes seven positive items, each rated on a 5-point Likert scale (e.g., 0 = never, 1 = almost never, 2 = sometimes, 3 = fairly often, 4 = very often). Participants were instructed to recall how often they had experienced these situations over the past month. An example of positive item is “In the last month, how often have you been upset because of something that happened unexpectedly?”. Item 4, 5, 6, 7, 9, 10, and 13 are scored in the reverse direction. The total PSS-14 score ranges from 0 to 56, with higher scores indicating a higher level of perceived stress. The obtained scores indicated that the internal reliability of the PSS-14 was high: Cronbach’s α = 0.88. Study samples and Procedures Hospital employees were invited to participate in this study from December 9, 2023 to February 28, 2024. They attended the mental health lecture and received an invitation to complete the questionnaire at the end of the lecture. The questionnaire included three sections: 1) Participant information (informed consent form and demographic information including sex, age, education, marital status, number of children, job function type); 2) PHQ-9; 3) PSS-14. Participants were informed that completing the questionnaire would take approximately 5 mins, and that participation was entirely voluntary. Data were collected via a hospital-specific platform. In total, 825 employees agreed to participate and completed the questionnaires. The descriptive statistics of participants’ characteristics are summarized in Table 1. Table 1 The descriptive statistics of participants’ characteristics. Characteristic N PHQ-9 PSS-14 Pariticipants, N 825 3.49(4.03) 17.96(8.95) Mean age, y ±SD 36.86±12.62 Gender Male 185 3.91(4.79) 18.38(9.59) Female 640 3.37(3.78) 17.83(8.76) Marital status, % Married 75.03 3.46(4.09) 18.12(8.84) Unmarried 22.91 3.37(3.66) 17.08(8.98) Divorced/separated/widowed 2.06 6.00(5.07) 21.64(11.75) Children, % 0 31.52 3.32(3.49) 17.05(8.87) 1 56.97 3.48(4.20) 18.08(8.96) ≥2 11.52 4.28(4.49) 19.80(8.93) Education, % 12 year, % 1.94 3.00(3.78) 18.69(7.94) 15 year, % 16.61 3.08(3.53) 17.99(8.62) 16 year, % 68.36 3.57(4.04) 17.93(8.99) 18 year, % 12.24 3.45(4.03) 17.84(9.24) 21 year, % 0.24 0.50(0.71) 14.00(1.41) Other 0.61 9.40(10.64) 21.80(14.25) Function type, % Doctor 29.09 3.63(4.23) 18.26(9.31) Nurse 42.55 3.46(3.83) 18.30(8.64) Medical technology 14.30 3.63(4.19) 16.85(8.58) Administration 14.06 3.19(4.11) 17.38(9.51) Statistical analyses The direct influence of each participants’ characteristic on the PHQ-9 and PSS-14 scores were assessed by using a Generalized Linear Mixed Model (GLMM) fitted for gamma distribution with an identity link. The GLMM was implemented in Rstudio software (Version 2024.12.1+56) using the function “glmer” in the package“lmerTest” (Ver. 3.1–2). Step-by-step evaluation of the influence was conducted by using the function “screenreg” in package “texreg” (Ver. 1.39.4). If the observed data follow a gamma distribution, the values should be positive, therefore, the data were processed as follow to avoid errors in the GLMM analyses.: 1) If the value of an item was 0, it was replaced with 0.01; 2) To make the number of children an ordinal scale, the values (i.e., 0, 1, ≥ 2) were changed to 1, 2, and 3, respectively. Correlations were calculated by the function “cor.test” (default method: Pearson correlation). Cronbach’s α was calculated by alpha() in the package “psych”. Confirmatory factor analysis (CFA) was conducted by using the function “cfa” in package “lavaan” (Ver. 0.6–19). The adequacy of the factor structure was evaluated based on the following criteria : GFI ≥ .95, AGFI ≥ .90, NFI ≥ .95, CFI ≥ .95, RMSEA ≤ .05, SRMR ≤ .05. Structural equation modeling (SEM) was implemented by using the function “sem” in the package “sem” (Ver. 3.1–16). Model comparison was conducted by using the function compareFit () in package “lavaan”. The goodness of fit model was evaluated based on the following criteria: GFI ≥ .95, TLI ≥ .95, SRMR ≤ .05, RMSEA ≤ .05 (adequate at .05 to .08). If the model met these criteria, it was considered acceptable. Model parsimony was compared based on the AIC and BIC (small values on both indexes indicates better fitting of the observed data). Results The moderated effect check Given that a few studies have demonstrated that participants’ characteristics can have an observable effect on the subjective assessment of depression and stress, one might consider that the direct effects of participants’ characteristics on total scores of the PHQ-9 and PSS-14 should not be the case in the current study. The histogram of total score distributions showed that the PHQ-9 score follows a monotonically decreasing curve in the first quadrant, while the PSS-14 score follows a positively skewed curve in the first quadrant, indicating a gamma distribution is suitable for fitting these data (Figure S1). GLMM analysis was conducted for both total scores, and the effects of participants’ characteristics were checked step by step (see Table. S1 & S2). It was shown that nor the factors was significantly increased r R 2 , suggesting that all the participants’ characteristics had little direct effect on the scores of the PHQ-9 as well as the PSS-14. The correlation relationship between PHQ and PSS The PHQ-9 and PSS-14 scores showed a relatively strong correlation ( M PHQ = 3.49, SD PHQ = 4.03, M PSS = 17.96, SD PSS = 8.95, r > .67, p < .001). The distribution of PHQ-9 scores in each category (0–4, 5–9, 10–14, 15–19, 20–27) was 67.39%, 24.85%, 5.82%, 1.58%, 0.36%, respectively. To test our hypothesis — whether the participant’s characteristics can influence depression via stress—we first visualized the statistical data and used violin plots to preliminarily determine which characteristics might alter the correlation. Intergroup differences in score distributions are summarized in Figure S2. The simple linear model fitted to plotted data suggests a tendency that age, sex, and number of children may increase PHQ-9 scores through greater unit increases in PSS-14 scores, especially when participants’ scores exceed the mean values of both the PHQ-9 and PSS-14. Furthermore, PHQ-9 scores were higher among participants in other group (e.g., separated/widowed) than among those in married/unmarried groups, even when PSS-14 scores were the same. This information suggests that participants’ characteristics may alter the correlation between perceived stress and depression, however, no significant influence of these characteristics on PHQ-9 and PSS-14 scores was observed in the direct effect comparison, which indicates that participants’ characteristics may manifest their effects in a more complex way. Nevertheless, these findings provide insight for this study to focus on specific participants’ characteristics and to further investigate which ones can influence perceived stress as well as depression. Confirmatory factor analysis Although the PHQ-9 and PSS-14 are well-validated questionnaires, as demonstrated by numerous previous studies, confirmatory factor analysis (CFA) was conducted as a manipulation check (Table 2). First, we fitted a one-factor model of PHQ-9 and found that GFI was acceptable but it fell to 0.852 after adjustment while the RMSEA also exceeded 0.1, indicating that the one-factor model did not fit the observed data as expected. Considering the possibility that an acceptable two-factor model existed, we discarded items one by one until all indices changed to acceptable. It was found that when Item 2 and Item 4 were discarded, the one-factor model was acceptable, whereas the full-item two-factor model still could not meet the criteria. It indicated that the 7-Item one-factor model adequately explained the observed data in the current study, ensuring that the 7-Item score can reflect the participant’s depression severity well. Second, we fitted both one- and two-factor models of the PSS-14 scores and found neither was acceptable in explaining the observed data. It seems to be a common issue when using PSS-14 [40, 41] , and the PSS-10 formed by discarding Items 4, 5, 12, 13 from the PSS-14, was evidenced better performance in two-factor CFA model [42-44] . We then fitted a two-factor model of PSS-10 and it showed this model is acceptable. Thus, 10-Item two-factor model can adequately explain the observed data in the current study, ensuring that the PSS-10 score can reflect the participant’s stress level well. Table 2 The results of the confirmatory factor analysis with varying numbers of factors. Model χ^2 p (χ^2) GFI AGFI NFI CFI RMSEA SRMR PHQ-9 One-factor 330.195 < .001 0.911 0.852 0.908 0.914 0.117 0.049 One-factor a 65.906 < .001 0.977 0.953 0.968 0.975 0.067 0.030 Two-factor b 330.092 < .001 0.911 0.846 0.908 0.914 0.119 0.049 PSS-14 One-factor 4772.398 < .001 0.367 0.137 0.419 0.422 0.272 0.270 Two-factor 739.658 < .001 0.880 0.835 0.910 0.918 0.103 0.103 PSS-10 Two-factor 213.082 < .001 0.949 0.918 0.962 0.968 0.08 0.031 One-factor a : Item 2 and Item 4 was discarded. Two-factor b : Item 2 and Item 4 was loading to the second factor. Structural Equation Modeling We employed structural equation modeling (SEM) to clarify the influence pathways of participants’ characteristics on depression. In order to test our hypothesis, the hypothesized structural model (H-model) was compared with the full structural model (F-model) and a comparison model (C-model). F-model includes three latent factors (f_D, f_S1, f_S2), and assumes that the influence of participants’ characteristics is identical to all latent factors, which regards the characteristics as individual differences that equally affect subjective assessment. H-model includes the same latent factors as F-model but assumes that participants’ characteristics only manifest their effects on the latent factors associated with perceived stress. C-model assumes that participants’ characteristics only manifest their effects on the latent factor associated with depression. Model comparisons were conducted to determine which model had the highest explanatory power for the observed data. The results showed that all models were acceptable (Table 3). Since H-model and C-model are nested within F-model, nested model comparisons were conducted. The results showed that although 6 parameters were reduced between each model, the likelihood ( L h ) of H-model was marginally worse than that of F-model (r R 2 = 11.958, p = .063),while L h of C-mode was significantly worse than L h of F-model (r R 2 = 15.835, p = .015). It indicated that removing the estimated parameters of participants’ characteristics on the latent factor f_D had minimal influence on the fit of structural model, suggesting that H-model is more concise than F-model while maintaining explanatory power of the observed data. Other fit indices also supported that H-model did not fall below acceptable benchmark: AIC and BIC in the H-model decreased (rAIC= -0.042, rBIC = -28.334) and RMSEA was slightly lower (rRMSEA = 0.00, H-model CI [0.039 – 0.049]; F-model CI [0.040 – 0.049]), but SRMR was slight higher (rSRMR = 0.006) than F-model. Taken together, these model fit indices indicated that H model is suitable for the data sample in current study, and the results of model comparisons supported our hypothesis that the participants’ characteristics influence depression via stress. Table 3 The results of the model comparison. Model χ^2 df p RMSEA CFI TLI SRMR AIC BIC F-model 521.889† 200 .000 .044 .961† .953 .029† 25541.196 25800.542 H-model 533.847 206 .000 .044† .960 .954† .036 25541.154† 25772.208† C-model 549.682 212 .000 .044 .959 .954 .039 25544.989 25747.750† Notes. χ^2: Chi-Square; df: Degree of Freedom; RMESA: Root Mean Square Error of Approximation; CFI: Comparative Fit Index; TLI: Tucker-Lewis Index; SRMR: Standardized Root Mean-squared Residual; AIC: Akaike Information Criterion; BIC: Bayesian Information Criterion. †: statistically significant The path diagram of best-fitted H-model was showed in Figure 1. As expected based on the plot data, it was shown that age, sex, and marital status have an effect on the latent factors of perceived stress (f_S1, f_S2). Specifically, age can affect depression severity by negatively influencing f_S2 (self-efficacy [42, 45] ), with an effect was – 0.055 (Total effect = -0.09*(0.36+0.32*0.78)), indicating older participants feel lower self-efficacy, and thus reported more depressed than younger individuals. Sex can affect depression severity by positively influencing f_S1 (distress [42] ), with an effect was 0.062 (Total effect = 0.08*0.78), indicating females feel more distress and subsequently report more depressed than males. Marital status can affect the severity of depression by positively influencing f_S1, with an effect was 0.085 (Total effect = 0.11*0.78), indicating the changes in marital status may lead participants to feel more distress, thereby increasing depression severity. In line with the results from the moderated effect check, these findings support our hypothesis that participants’ characteristics alter depression severity by modifying their subjective feeling of stress. Discussion This study aimed to investigate how socio-environmental factors influence perceived stress and ultimately contribute to the onset of depression by examining the effects of demographic characteristics that may reflect chronic stress on perceived stress or/and depression. The results of model comparisons (Table 3) indicated that the model (H-model), in which demographic characteristics were solely related to perceived stress, was the most parsimonious and adequately explained the observed data, suggesting that demographic characteristics contribute to the severity of depression by influencing perceived stress. Next, the SEM results of H-model (Figure 1) indicated that age was associated with the latent factor, self-efficacy, while sex and marital status were associated with the latent factor distress, suggesting that self-efficacy declines with aging, whereas distress increases among individuals whom are tagged the special characteristics “female” and “having marital experience”. These findings support our hypothesis that chronic stress related demographic characteristics can affect perceived stress, thereby exerting an indirect impact on the severity of depression. The impact of occupational specificity on the incidence of depression has received much attention [46] (e.g., burnout), and data from specialized occupational groups have been sought to better understand why service-based occupational roles are at higher risk for depression [47] . Compared to large-sample data (N = 6028) of Chinese in Hong kong [48] , hospital staff exhibited a similar prevalence of severe depression (scores over 20) as the general population (0.36% vs 0.5%). Whereas the distribution of scores in the current sample showed a significantly smaller proportion of individuals entirely free from depression (67.39% vs 82.1%), and a higher proportion of people who felt slightly depressed and had already experienced low-grade depressive symptoms (scores less than 9: 24.85% vs 13.7%, less than 14: 5.82% vs 3.0%, less than 19: 1.58% vs 0.8%). This trend in hospital employees aligns with findings from a group of nurses [49] (N = 442, 57.69%, 28.28%, 8.6%, 3.6%, 1.83%, respectively). The reason this study focuses on depression among all staff members, not just nurses, because—although it cannot be denied that nurses are exposed to more stress than doctors due to their position, it should be considered that stress caused by conflicts in the doctor-patient relationship affects all staff members. The current results indeed support this notion, suggesting that working in a hospital (at least in China) is a stressful environment that significantly impacts on the mental state of staff. Individual differences in perceived stress were suggested as an important factor in the onset of depression. However, because the effects of internal (e.g., gene expression) or external factors (e.g., gender) on the variations in perceived stress and depression are similar, it has not been easy to clarify whether a causal relationship between perceived stress and depression exists. Previous studies have reported that demographic characteristics influence both perceived stress and depression [13, 23] . In that case, it is more reasonable to consider that perceived stress and depression are likely covariates and that their correlation can be reasoned by the influence of demographic characteristics. This pattern (which was represented in the current study as F-model) can does little to clarify the causal relationship between perceived stress and depression. To extend it, the current study used a model comparison approach, considering the explanatory power of the data as the criterion for evaluating “which relationships are reasonable”. The results showed H-model (i.e., a high correlation between perceived stress and depression, but demographic characteristics only affect perceived stress) was more suitable than F-model, suggesting that a causal relationship between stress perception and depression provides a better explanation for the current data than a non-causal relationship. In addition, the explanatory power clearly dropped when C-model was used, indicating that the influence of demographic characteristics on stress perception makes an indispensable and important contribution to explaining the data. These findings suggest a clear causal relationship in which demographic characteristics alter perceived stress, which in turn is associated with changes in depression severity. Previous studies have shown that demographic characteristics may affect perceived stress and that perceived stress is highly correlated with depression. Another aim of this study is to explore how to interpret the relationship between these findings. Given that individual differences in perceived stress can be attributed to variations in socio-environmental condition difference, we hypothesized that if demographic characteristics clearly indicate that a person is exposed to chronic stress, then people with these characteristics should be more likely to feel stress in similar stressful environments than those without them, and that this, in turn, should affect depression severity We also investigated how demographic characteristics affect specific psychological components, such as the latent factors of perceived stress (self-efficacy and distress), rather than simply examining whether they increase or decrease overall perceived stress. The results showed that self-efficacy decreased with age and that individuals tagged by “female” or “marital experience” reported higher levels of distress. Since the effect of marital experience was slightly higher than that of gender, it is thought that the increased distress associated with marital experience reflects the impact of changes in social relationships, rather than purely physiological differences between the sexes [6] . Commonly, after a change in marital status, social relationships become more complex and interpersonal conflicts that individuals must navigate increase. People in such situations can be considered as being exposed to chronic stress. In this sense, the finding that married individuals experience more distress than unmarried individuals can be interpreted as the result of exposure to chronic stress from managing more complex social relationships over time, rather than simply the impact of a one-time stressful life event. However, this influence was not obviously enough to overcome the noise arising from individual differences because demographic characteristics had little direct effect on the fit of the model (Table S1 & S2). This may also help explain why previous studies have not reported a similar effect. Nevertheless, it can be suggested that the current findings highlight the relationship between chronic stress and perceived stress, revealing that the chronic stress related demographic characteristics can increase perceived stress under similar socio-environmental conditions (e.g., the same occupation). These findings are consistent with related studies suggesting that stress should be a causal factor in the development of depression, and extend the understanding of that marital experience may generate a chronic stress situation which increases perceived stress and ultimately promotes the development of depression. There are several limitations that should be acknowledged. First, due to privacy concerns regarding the open collection of staff data at the hospital, we did not collect demographic information such as income or working hours (as doing so may have violated hospital regulations). Although these workplace-related characteristics may also affect perceived stress and depression, the current analysis could not eliminate their influence. This is considered one of the limitations of this study. Second, because the data in this study were collected from participants in a natural setting within a specific organization, we were unable to use experimental manipulation to control for differences in the number of categories sampled for a given characteristic. This limitation may have led to either overestimation or underestimation of the effects of certain demographic characteristics. Future research should explore whether the results can be replicated by addressing this factor. Third, due to the fact that a strong positive correlation is not sufficient to conclusively a causal relationship. Hence, even if demographic characteristics influence perceived stress, this does not necessarily mean they lead to more severe depressive symptoms. It is possible that depression can affect perceived stress in conjunction with demographic characteristics, particularly if depression is treated as an internal stressor [50] . The current findings cannot rule out this possibility and therefore it should be tested in further investigation. Conclusion Since Selye proposed the psychological concept of "stressor," many researchers have examined how stress caused by environmental stressors is related to depression. While it is widely accepted that stress is an important trigger for the onset of depression, relatively little research has focused on individual differences in how stressors affect different individuals. In this study, the variation in perceived stress reported by individuals working in similar environments, depending on demographic characteristics, can be interpreted as reflecting such individual differences. Moreover, the results indicated that gender and changes in marital status make individuals more susceptible to stress, and correspondingly, their depressive symptoms more pronounced. These findings support the notion that perceived stress plays a more critical role in the onset of depression than stressful life events themselves. Declarations Acknowledgements The authors would like to thank Google translate and ChatGPT for the English language review. Authors’ contributions WL, HD, and HL contributed to the study’s conception and design, conducted statistical analysis, acquired data, and wrote the initial draft of the manuscript. WL was responsible for revising and addressing questions during the peer review process. All authors approved the final version of the manuscript for submission. Funding This work was supported by the Funding for Scientific Research Projects from Wuhan Municipal Health Commission under Grant WX23Q35 & WX23Z64, Science Foundation of the Hubei Province, China under Grant 2025AFC007; and Scientific Research Foundation of Wuhan Hankou Hospital under Grant HKYY2025005 & HKYY2025012. The funder had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. Data availability statement The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request. Ethics approval and consent to participate The studies involving human participants were reviewed and approved by Ethics Committee of Wuhan Hankou Hospital (hyll2023060). 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Frequent interpersonal stress and inflammatory reactivity predict depressive-symptom increases: two tests of the social-signal-transduction theory of depression. Psychol Sci. 2022;33(1):152–64. Additional Declarations No competing interests reported. Supplementary Files Supplementfile.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 08 Oct, 2025 Reviewers agreed at journal 08 Oct, 2025 Reviewers agreed at journal 28 Sep, 2025 Reviewers invited by journal 26 Sep, 2025 Editor invited by journal 08 Sep, 2025 Editor assigned by journal 05 Sep, 2025 Submission checks completed at journal 05 Sep, 2025 First submitted to journal 04 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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1","display":"","copyAsset":false,"role":"figure","size":402631,"visible":true,"origin":"","legend":"\u003cp\u003eThe SEM result of H-model.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7532487/v1/5c92d535a8839f7a7c7e3fa4.jpeg"},{"id":93027581,"identity":"fd35de55-2437-4051-aaaf-8507449e82ca","added_by":"auto","created_at":"2025-10-08 09:40:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1312739,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7532487/v1/a11fb64f-7d7a-4f1e-8570-c739e9e969fc.pdf"},{"id":93025857,"identity":"cd0e362c-455d-456f-aeac-1f8e378a72a9","added_by":"auto","created_at":"2025-10-08 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In China, depression has become prevalent across generations and socio-economic statuses\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e, and it generates a variety of public health issues\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. In response to this situation, a substantial body of research has been conducted to identify key predictors of depression and to examine the impact of critical factors on its progression, thereby contributing to early detection and prevention. However, due to the complex pathogenesis of depression, even some fundamental questions remain unresolved.\u003c/p\u003e\u003cp\u003eIt is well known that stress and depression are closely related. In the Selye\u0026rsquo;s theoretical framework of Stress\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e, when a stressful life event occurs, subject is exposed to stress, which causes both mental and physical responses. If stress accumulates and cannot be relieved, subject\u0026rsquo;s mind and body may break down and become unable to adapt to environmental stressors. For this reason, depression is considered to be the result of maladaptation to environmental stressors\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. The question of how stress leads to the onset of depression is somewhat old, but in fact there has been little direct evidence showing that there is a causal relationship between them. In addition, in recent years, it has been pointed out that stress and depression affect each other \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Therefore, in this study, we focused on the relationship between stress and depression and aimed to examine how demographic characteristics affect their relationships.\u003c/p\u003e\n\u003ch3\u003eUnidirectional or bidirectional? The still-debated link between stress and depression\u003c/h3\u003e\n\u003cp\u003eA strong relationship between stress and depression has been well-established by numerous studies. Evidence indicates that the occurrence of stressful life events\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e, chronic stress induced by exposure to stressors\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e, the experience of early life stress\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e are powerful predictors of the impending onset of depression. Similarly, it has been found that depressed individuals report more psychological stress than those who do not\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. This relationship is robust, with consistent correlations observed no matter in the general population\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e or in specific occupational groups\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. These correlational studies demonstrate stress appears to be associated with depression; however, there is still an insufficient understanding of the pathway linking environmental stressors to the onset of depression. This gap might be filled by related works that explore how the internal physiological processes related to depression are mediated by external socio-environmental stress\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Many, but not all, findings support the inference that stress-induced alterations in neurotrophic factor expression may contribute to the development of depression. Additionally, psychological stress can trigger a significant increase in inflammatory activity\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e (i.e., in the absence of physical injury; Glaser \u0026amp; Kiecolt-Glaser, 2005), and is negatively associated with susceptibility to the immune-related disease(e.g., common cold) \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. These findings suggest that stress can have effects on the regulation of immune and inflammatory processes, offering a plausible pathway through which stress affects depression. This body of evidence undoubtedly points to stress as a contributing factor to depression, and researchers believe that stress should be a causal factor in the development of depression.\u003c/p\u003e\u003cp\u003eDespite the fact that stress-related factors do predict the risk of becoming depressed, not all individuals develop depressive disorders after encountering stressful life events. This compels researchers to realize the necessary of explaining the relationship between stress and depression in a more nuanced manner. The influence of gene phenotypes was first considered a potential explanation for this paradoxical phenomenon\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. It was supported by the finding that individuals with SS alleles (vs individuals with SL, LL alleles) were more sensitive to the depressogenic effects of low-threat stressful life events, suggesting heightened sensitivity to stress may be a causal factor in the depressive onset\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Similarly, a substantial overlap between the genetic influences on stress sensitivity and depressive symptoms has been evidenced\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e, leading to the assumption that abnormalities in stress sensitivity may be one of the early manifestations of depression. This gives rise to a further adventurous assumption: that a transient negative mood state induces a decrease in the threshold for stress sensitivity, thereby causing intense stress responses, and ultimately leading to the onset of depression. Indeed, it has been found that depressive individuals (i.e., unipolar women) are more sensitive to interpersonal conflict and consequently experience more stressful life events\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Moreover, the cortisol levels of depressive patients were found to recover more slowly after exposure to a stress stimulus compared to those of healthy individuals\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Although these findings alone cannot provide definitive evidence of an inverse relationship between stress and depression, they do suggest that the relationship is more bidirectional than unidirectional. In this context, individual differences in stress sensitivity may play a more crucial role than the stressful life events themselves on depression\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eThe influence of socio-demographic characteristics on perceived psychological stress\u003c/h2\u003e\u003cp\u003ePerceived psychological stress refers to the extent to which an individual consciously experiences stress and is most often assessed using subjective scales\u003csup\u003e[\u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Given that the more vivid and intense the perceived stress is \u0026mdash; to the same stressor or to different stressors of the same intensity \u0026mdash; the greater the risk of developing depression, understanding the factors that influence perceived stress is of particular importance. Socio-demographic characteristics (e.g., age, sex, marital status, educational level, occupation, socio-economic status, and even race in some cases), which reflect a person\u0026rsquo;s broader social context, have attracted considerable attention due to their potential to provide insight into how perceived stress changes when certain characteristics are attached. Among these, gender has been suggested to be the most salient and robust factor in perceiving stress\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Evidence indicates that females report greater psychological stress than males in a large sample of general university students\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e (n\u0026thinsp;=\u0026thinsp;1617) as well as in a smaller sample of medical students\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.47, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.03, n\u0026thinsp;=\u0026thinsp;80). This gender difference has remained consistent over time\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. However, although gender can reflect aspects of the social environment (e.g., sexism), it has been proposed that gender differences in perceived stress may stem from genetic rather than social influences. A recently published paper suggested that greater sensitivity to stress among women may be attributed to differences in physiological structure between the sexes\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. The authors highlighted a growing body of evidence indicating depression may be caused by dysfunction in the hormonal systems that normally protect the body from damage resulting from excessive inflammatory responses\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Additionally, it has been found that women report greater social stress (e.g., loneliness, social disconnection) and more pronounced emotional changes in response to experimentally induced inflammatory responses\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. Accordingly, it has been suggested that gender differences in both perceived stress and depression may stem from a reduced ability of cortisol to suppress inflammatory responses associated with regular fluctuations in female-specific sex hormones\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. Therefore, the authors suggested that female-specific physiological changes in response to inflammation may underlie gender disparities in perceived stress and the higher prevalence of depression among women.\u003c/p\u003e\u003cp\u003eOn the other hand, a smaller body of evidence suggests that other demographic characteristics may increase susceptibility to stress. It has been found that job function (i.e., type of ward, type of shift) is associated with elevated psychological stress among nurses\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. In a sample of medical undergraduate students, factors related to interpersonal stress and social rejection\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e (e.g., personal income, student score, social activity and transportation to faculty) were linked to increased scores in depression, anxiety, and stress, whereas marital status was associated only with increased stress scores\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. However, the influence of demographic characteristics was less pronounced when examining the general population. One study found the directs effect of demographic characteristics on depression, anxiety, stress (as assessed by the DASS-21) was very modest(\u003cem\u003er\u003c/em\u003es\u0026thinsp;\u0026lt;\u0026thinsp;0.2) \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Indeed, demographic characteristics are often considered merely one facet of individual differences and are treated as control factors in some studies\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. Although a broader explanation for these discrepancies may lie in subject-specific variations, these findings nonetheless suggest a potential pathway by which environmental stressors, as reflected in demographic characteristics, affect the development of depression\u0026mdash;an issue that has received limited analytical attention and debate regarding its validity.\u003c/p\u003e\u003cp\u003eIn fact, an alternative explanation for how demographic characteristics can influence perceived stress has already been proposed\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. It posits that demographic characteristics may predict the risk of depression due to their association with chronic stress and allostatic load. In support of this, evidence indicates a low level of education can predict the risk of depression, which is attributed to the strong causal link between lower educational level and low SES in developed countries\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. More direct evidence was provided by a study focusing on variations in stress according to socio-demographic characteristics among individuals from low SES backgrounds (i.e., residents of deprived neighborhoods), which found that certain demographic characteristics (e.g., age, gender, ethnicity, loneliness) can influence the perception of stress\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. Considering that the influence of these characteristics is likely to be long-term and persistent, the authors concluded that chronic stress may be the latent yet central factor embedded within socio-demographic characteristics. Thus, one can speculate that demographic characteristics, at least in part, may reflect chronic stress and therefore contribute to heightened sensitivity to stress, though they may not directly promote the development of depression.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eThe objective of current study\u003c/h3\u003e\n\u003cp\u003eAlthough stressful experiences primarily contribute directly to the onset of depression, they are not a prerequisite for its development\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e; recent studies have suggested that perceived stress\u0026mdash;traditionally treated as an individual difference\u0026mdash;is also an important factor in the onset of depression. However, questions such as how social and environmental conditions give rise to chronic stress, and how demographic characteristics reflecting these conditions moderate the relationship between perceived stress and depression have yet to be fully clarified. The current study sought to address these questions by investigating perceived stress and depression in a specific sample: hospital employees.\u003c/p\u003e\u003cp\u003eAlthough still limited, some studies have reported that demographic characteristics (e.g., sex, age, occupational function, and family composition status) influence perceived stress. Therefore, we expected these characteristics to also be associated with perceived stress in the current sample. Furthermore, if characteristics more closely associated with chronic stress, such as occupational function and marital status, are found to be related only to an increase in perceived stress but not to an increase in depression, this would support the hypothesis that increased perceived stress due to chronic stress caused by socio-environmental factors contributes to the onset of depression. Accordingly, if our hypothesis is correct, it should be supported by the model comparisons, whereby a structural equation model indicating this causal relationship would demonstrate a better fit than one indicating alternative causal relationships.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthical Statement\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe studies involving human participants were reviewed and approved by Ethics Committee of Wuhan Hankou Hospital (hyll2023060). Written Informed consent was obtained from all patients/participants after a full explanation of the nature of the study and possible risks, and all methods were performed according to the relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMeasurements\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePatient Health Questionnaire 9-Items\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe Patient Health Questionnaire 9 Items(PHQ-9)\u003csup\u003e[38]\u003c/sup\u003e is a well-validated, self-report questionnaire used to assess depression in the variety of settings, particularly in primary care. Its validity and reliability have also been confirmed in the general population\u003csup\u003e[39]\u003c/sup\u003e. Each item (e.g., Little interest of pleasure in doing things) reflects a typical manifestation of depression and is rated on a 4-point Likert scale (i.e., 0 = not at all, 1 = several days, 2 = more than half the days, 3 = nearly every day). Participants were instructed to recall whether they had experienced these symptoms over the last two weeks. The total PHQ-9 score ranges from 0 to 27, and is typically divided into five categories of increasing severity (0\u0026ndash;4, 5\u0026ndash;9, 10\u0026ndash;14,15\u0026ndash;19, 20 and greater). However, these cut-ff scores were not employed due to the objectives of this study. The obtained scores indicated that the internal reliability of the PHQ-9 was high: Cronbach\u0026rsquo;s \u0026alpha; = 0.89.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePerceived Stress Scale 14-Items\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe 14-item Perceived Stress Scale(PSS-14)\u003csup\u003e\u0026nbsp;[24, 25]\u003c/sup\u003e is widely used to assess perceived stress levels over a period of time, typically in general populations. The PSS-14 includes seven positive items, each rated on a 5-point Likert scale (e.g., 0 = never, 1 = almost never, 2 = sometimes, 3 = fairly often, 4 = very often). Participants were instructed to recall how often they had experienced these situations over the past month. An example of positive item is \u0026ldquo;In the last month, how often have you been upset because of something that happened unexpectedly?\u0026rdquo;. Item 4, 5, 6, 7, 9, 10, and 13 are scored in the reverse direction. The total PSS-14 score ranges from 0 to 56, with higher scores indicating a higher level of perceived stress. The obtained scores indicated that the internal reliability of the PSS-14 was high: Cronbach\u0026rsquo;s \u0026alpha; = 0.88.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStudy samples and Procedures\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHospital employees were invited to participate in this study from December 9, 2023 to February 28, 2024. They attended the mental health lecture and received an invitation to complete the questionnaire at the end of the lecture. The questionnaire included three sections: 1) Participant information (informed consent form and demographic information including sex, age, education, marital status, number of children, job function type); 2) PHQ-9; 3) PSS-14. Participants were informed that completing the questionnaire would take approximately 5 mins, and that participation was entirely voluntary. Data were collected via a hospital-specific platform. In total, 825 employees agreed to participate and completed the questionnaires. The descriptive statistics of participants\u0026rsquo; characteristics are summarized in Table 1.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"544\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\" style=\"width: 515px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e The descriptive statistics of participants\u0026rsquo; characteristics.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePHQ-9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePSS-14\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 222px;\"\u003e\n \u003cp\u003ePariticipants, \u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e3.49(4.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e17.96(8.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 222px;\"\u003e\n \u003cp\u003eMean age, y \u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e36.86\u0026plusmn;12.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 222px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 191px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e3.91(4.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e18.38(9.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 191px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e3.37(3.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e17.83(8.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 222px;\"\u003e\n \u003cp\u003eMarital status, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 44px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e75.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e3.46(4.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e18.12(8.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 44px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eUnmarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e22.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e3.37(3.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e17.08(8.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 44px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eDivorced/separated/widowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e6.00(5.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e21.64(11.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 222px;\"\u003e\n \u003cp\u003eChildren, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 42px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 180px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e31.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e3.32(3.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e17.05(8.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 42px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 180px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e56.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e3.48(4.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e18.08(8.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 42px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026ge;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e11.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e4.28(4.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e19.80(8.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 222px;\"\u003e\n \u003cp\u003eEducation, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 42px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 180px;\"\u003e\n \u003cp\u003e12 year, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e3.00(3.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e18.69(7.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 42px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 180px;\"\u003e\n \u003cp\u003e15 year, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e16.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e3.08(3.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e17.99(8.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 42px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 180px;\"\u003e\n \u003cp\u003e16 year, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e68.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e3.57(4.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e17.93(8.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 42px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 180px;\"\u003e\n \u003cp\u003e18 year, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e12.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e3.45(4.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e17.84(9.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 42px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 180px;\"\u003e\n \u003cp\u003e21 year, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.50(0.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e14.00(1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 42px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 180px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e9.40(10.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e21.80(14.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 222px;\"\u003e\n \u003cp\u003eFunction type, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 42px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 180px;\"\u003e\n \u003cp\u003eDoctor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e29.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e3.63(4.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e18.26(9.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 42px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 180px;\"\u003e\n \u003cp\u003eNurse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e42.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e3.46(3.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e18.30(8.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 42px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 180px;\"\u003e\n \u003cp\u003eMedical technology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e14.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e3.63(4.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e16.85(8.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 42px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 180px;\"\u003e\n \u003cp\u003eAdministration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e14.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e3.19(4.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e17.38(9.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 42px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 180px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 30px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStatistical analyses\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe direct influence of each participants\u0026rsquo; characteristic on the PHQ-9 and PSS-14 scores were assessed by using a Generalized Linear Mixed Model (GLMM) fitted for gamma distribution with an identity link. The GLMM was implemented in Rstudio software (Version 2024.12.1+56) using the function \u0026ldquo;glmer\u0026rdquo; in the package\u0026ldquo;lmerTest\u0026rdquo; (Ver. 3.1\u0026ndash;2). Step-by-step evaluation of the influence was conducted by using the function\u0026nbsp;\u0026ldquo;screenreg\u0026rdquo; in package \u0026ldquo;texreg\u0026rdquo; (Ver. 1.39.4). If the observed data follow a gamma distribution, the values should be positive, therefore, the\u0026nbsp;data were processed as follow to avoid errors in the GLMM analyses.: 1) If the value of an item was 0, it was replaced with 0.01; 2) To make the number of children an ordinal scale, the values (i.e., 0, 1, \u0026ge; 2) were changed to 1, 2, and 3, respectively.\u003c/p\u003e\n\u003cp\u003eCorrelations were calculated by the function \u0026ldquo;cor.test\u0026rdquo; (default method: Pearson correlation). Cronbach\u0026rsquo;s \u0026alpha; was calculated by alpha() in the package \u0026ldquo;psych\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003eConfirmatory factor analysis (CFA) was conducted by using the function \u0026ldquo;cfa\u0026rdquo; in package \u0026ldquo;lavaan\u0026rdquo; (Ver. 0.6\u0026ndash;19). The adequacy of the factor structure was evaluated based on the following criteria : GFI \u0026ge; .95, AGFI \u0026ge; .90, NFI \u0026ge; .95, CFI \u0026ge; .95, RMSEA \u0026le; .05, SRMR \u0026le; .05.\u003c/p\u003e\n\u003cp\u003eStructural equation modeling (SEM) was implemented by using the function \u0026ldquo;sem\u0026rdquo; in the package \u0026ldquo;sem\u0026rdquo; (Ver. 3.1\u0026ndash;16). Model comparison was conducted by using the function compareFit () in package \u0026ldquo;lavaan\u0026rdquo;. The goodness of fit model was evaluated based on the following criteria: GFI \u0026ge; .95, TLI \u0026ge; .95, SRMR \u0026le; .05, RMSEA \u0026le; .05 (adequate at .05 to .08). If the model met these criteria, it was considered acceptable. Model parsimony was compared based on the AIC and BIC (small values on both indexes indicates better fitting of the observed data).\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eThe moderated effect check\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiven that a few studies have demonstrated that participants\u0026rsquo; characteristics can have an observable effect on the subjective assessment of depression and stress, one might consider that the direct effects of participants\u0026rsquo; characteristics on total scores of the PHQ-9 and PSS-14 should not be the case in the current study. The histogram of total score distributions showed that the PHQ-9 score follows a monotonically decreasing curve in the first quadrant, while the PSS-14 score follows a positively skewed curve in the first quadrant, indicating a gamma distribution is suitable for fitting these data (Figure S1). GLMM analysis was conducted for both total scores, and the effects of participants\u0026rsquo; characteristics were checked step by step (see Table. S1 \u0026amp; S2). It was shown that nor the factors was significantly increased\u0026nbsp;r\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e, suggesting that all the participants\u0026rsquo; characteristics had little direct effect on the scores of the PHQ-9 as well as the PSS-14.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eThe correlation relationship between PHQ and PSS\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe PHQ-9 and PSS-14 scores showed a relatively strong correlation (\u003cem\u003eM\u003c/em\u003e\u003csub\u003ePHQ\u003c/sub\u003e = 3.49, \u003cem\u003eSD\u003c/em\u003e\u003csub\u003ePHQ\u003c/sub\u003e = 4.03, \u003cem\u003eM\u003c/em\u003e\u003csub\u003ePSS\u003c/sub\u003e = 17.96, \u003cem\u003eSD\u003c/em\u003e\u003csub\u003ePSS\u003c/sub\u003e = 8.95, \u003cem\u003er\u003c/em\u003e \u0026gt; .67, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). The distribution of PHQ-9 scores in each category (0\u0026ndash;4, 5\u0026ndash;9, 10\u0026ndash;14, 15\u0026ndash;19, 20\u0026ndash;27) was 67.39%, 24.85%, 5.82%, 1.58%, 0.36%, respectively. To test our hypothesis\u003csup\u003e\u0026mdash;\u003c/sup\u003ewhether the participant\u0026rsquo;s characteristics can influence depression via stress\u0026mdash;we first visualized the statistical data and used violin plots to preliminarily determine which characteristics might alter the correlation. Intergroup differences in score distributions are summarized in Figure S2. The simple linear model fitted to plotted data suggests a tendency that age, sex, and number of children may increase PHQ-9 scores through greater unit increases in PSS-14 scores, especially when participants\u0026rsquo; scores exceed the mean values of both the PHQ-9 and PSS-14. Furthermore, PHQ-9 scores were higher among participants in other group (e.g., separated/widowed) than among those in married/unmarried groups, even when PSS-14 scores were the same. This information suggests that participants\u0026rsquo; characteristics may alter the correlation between perceived stress and depression, however, no significant influence of these characteristics on PHQ-9 and PSS-14 scores was observed in the direct effect comparison, which indicates that participants\u0026rsquo; characteristics may manifest their effects in a more complex way. Nevertheless, these findings provide insight for this study to focus on specific participants\u0026rsquo; characteristics and to further investigate which ones can influence perceived stress as well as depression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConfirmatory factor analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlthough the PHQ-9 and PSS-14 are well-validated questionnaires, as demonstrated by numerous previous studies, confirmatory factor analysis (CFA) was conducted as a manipulation check (Table 2). First, we fitted a one-factor model of PHQ-9 and found that GFI was acceptable but it fell to 0.852 after adjustment while the RMSEA also exceeded 0.1, indicating that the one-factor model did not fit the observed data as expected. Considering the possibility that an acceptable two-factor model existed, we discarded items one by one until all indices changed to acceptable. It was found that when Item 2 and Item 4 were discarded, the one-factor model was acceptable, whereas the full-item two-factor model still could not meet the criteria. It indicated that the 7-Item one-factor model adequately explained the observed data in the current study, ensuring that the 7-Item score can reflect the participant\u0026rsquo;s depression severity well.\u003c/p\u003e\n\u003cp\u003eSecond, we fitted both one- and two-factor models of the PSS-14 scores and found neither was acceptable in explaining the observed data. It seems to be a common issue when using PSS-14 \u003csup\u003e[40, 41]\u003c/sup\u003e, and the PSS-10 formed by discarding Items 4, 5, 12, 13 from the PSS-14, was evidenced better performance in two-factor CFA model\u003csup\u003e[42-44]\u003c/sup\u003e. We then fitted a two-factor model of PSS-10 and it showed this model is acceptable. Thus, 10-Item two-factor model can adequately explain the observed data in the current study, ensuring that the PSS-10 score can reflect the participant\u0026rsquo;s stress level well.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"559\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" style=\"width: 558px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003eThe results of the\u0026nbsp;confirmatory factor analysis with varying numbers of factors.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026chi;^2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e(\u0026chi;^2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003eGFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003eAGFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003eNFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003eCFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003eRMSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003eSRMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003ePHQ-9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eOne-factor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e330.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.908\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eOne-factor\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e65.906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eTwo-factor\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e330.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.908\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003ePSS-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eOne-factor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e4772.398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eTwo-factor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e739.658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003ePSS-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eTwo-factor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e213.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\" style=\"width: 559px;\"\u003e\n \u003cp\u003eOne-factor\u003csup\u003ea\u003c/sup\u003e: Item 2 and Item 4 was discarded.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTwo-factor\u003csup\u003eb\u003c/sup\u003e: Item 2 and Item 4 was loading to the second factor.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStructural Equation Modeling\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe employed structural equation modeling (SEM) to clarify the influence pathways of participants\u0026rsquo; characteristics on depression. In order to test our hypothesis, the hypothesized structural model (H-model) was compared with the full structural model (F-model) and a comparison model (C-model). F-model includes three latent factors (f_D, f_S1, f_S2), and assumes that the influence of participants\u0026rsquo; characteristics is identical to all latent factors, which regards the characteristics as individual differences that equally affect subjective assessment. H-model includes the same latent factors as F-model but assumes that participants\u0026rsquo; characteristics only manifest their effects on the latent factors associated with perceived stress. C-model assumes that participants\u0026rsquo; characteristics only manifest their effects on the latent factor associated with depression. Model comparisons were conducted to determine which model had the highest explanatory power for the observed data. The results showed that all models were acceptable (Table 3). Since H-model and C-model are nested within F-model, nested model comparisons were conducted. The results showed that although 6 parameters were reduced between each model, the likelihood (\u003cem\u003eL\u003c/em\u003e\u003csub\u003eh\u003c/sub\u003e) of H-model was marginally worse than that of F-model (r\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 11.958, \u003cem\u003ep\u003c/em\u003e = .063),while \u003cem\u003eL\u003c/em\u003e\u003csub\u003eh\u003c/sub\u003e of C-mode was significantly worse than \u003cem\u003eL\u003c/em\u003e\u003csub\u003eh\u003c/sub\u003e of F-model (r\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 15.835, \u003cem\u003ep\u003c/em\u003e = .015). It indicated that removing the estimated parameters of participants\u0026rsquo; characteristics on the latent factor f_D had minimal influence on the fit of structural model, suggesting that H-model is more concise than F-model while maintaining explanatory power of the observed data. Other fit indices also supported that H-model did not fall below acceptable benchmark: AIC and BIC in the H-model decreased (rAIC= -0.042, rBIC = -28.334) and RMSEA was slightly lower (rRMSEA = 0.00, H-model CI [0.039 \u0026ndash; 0.049]; F-model CI [0.040 \u0026ndash; 0.049]), but SRMR was slight higher (rSRMR = 0.006) than F-model. Taken together, these model fit indices indicated that H model is suitable for the data sample in current study, and the results of model comparisons supported our hypothesis that the participants\u0026rsquo; characteristics influence depression via stress.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"562\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" style=\"width: 559px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e The results of the model comparison.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026chi;^2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003eRMSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eCFI\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eTLI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003eSRMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eBIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eF-model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e521.889\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.961\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e.953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e.029\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e25541.196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e25800.542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eH-model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e533.847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e.044\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e.954\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e25541.154\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e25772.208\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eC-model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e549.682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e.954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e25544.989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e25747.750\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\" style=\"width: 562px;\"\u003e\n \u003cp\u003eNotes. \u0026chi;^2: Chi-Square; df: Degree of Freedom; RMESA: Root Mean Square Error of Approximation; CFI: Comparative Fit Index; TLI: Tucker-Lewis Index; SRMR: Standardized Root Mean-squared Residual; AIC: Akaike Information Criterion; BIC: Bayesian Information Criterion. \u0026dagger;: statistically significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe path diagram of best-fitted H-model was showed in Figure 1. As expected based on the plot data, it was shown that age, sex, and marital status have an effect on the latent factors of perceived stress (f_S1, f_S2). Specifically, age can affect depression severity by negatively influencing f_S2 (self-efficacy\u003csup\u003e[42, 45]\u003c/sup\u003e), with an effect was \u0026ndash; 0.055 (Total effect = -0.09*(0.36+0.32*0.78)), indicating older participants feel lower self-efficacy, and thus reported more depressed than younger individuals. Sex can affect depression severity by positively influencing f_S1 (distress\u003csup\u003e[42]\u003c/sup\u003e), with an effect was 0.062 (Total effect = 0.08*0.78), indicating females feel more distress and subsequently report more depressed than males. Marital status can affect the severity of depression by positively influencing f_S1, with an effect was 0.085 (Total effect = 0.11*0.78), indicating the changes in marital status may lead participants to feel more distress, thereby increasing depression severity. In line with the results from the moderated effect check, these findings support our hypothesis that participants\u0026rsquo; characteristics alter depression severity by modifying their subjective feeling of stress.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to investigate how socio-environmental factors influence perceived stress and ultimately contribute to the onset of depression by examining the effects of \u0026nbsp;demographic characteristics that may reflect chronic stress on perceived stress or/and depression. The results of model comparisons (Table 3) indicated that the\u0026nbsp;model (H-model), in which demographic characteristics were solely related to perceived stress, was the most parsimonious and adequately explained the observed data, suggesting that demographic characteristics contribute to the severity of depression by influencing perceived stress. Next, the SEM results of H-model (Figure 1) indicated that age was associated with the latent factor, self-efficacy, while sex and marital status were associated with the latent factor distress, suggesting that self-efficacy declines with aging, whereas distress increases among individuals whom are tagged the special characteristics \u0026ldquo;female\u0026rdquo; and \u0026ldquo;having marital experience\u0026rdquo;. These findings support our hypothesis that chronic stress related demographic characteristics can affect perceived stress, thereby exerting an indirect impact on the severity of depression.\u003c/p\u003e\n\u003cp\u003eThe impact of occupational specificity on the incidence of depression has received much attention\u003csup\u003e[46]\u003c/sup\u003e (e.g., burnout), and data from specialized occupational groups have been sought to better understand why service-based occupational roles are at higher risk for depression\u003csup\u003e[47]\u003c/sup\u003e. Compared to large-sample data (N = 6028) of Chinese in Hong kong\u003csup\u003e[48]\u003c/sup\u003e, hospital staff exhibited a similar prevalence of severe depression (scores over 20) as the general population (0.36% vs 0.5%). Whereas the distribution of scores in the current sample showed a significantly smaller proportion of individuals entirely free from depression (67.39% vs 82.1%), and a higher proportion of people who felt slightly depressed and had already experienced low-grade depressive symptoms (scores less than 9: 24.85% vs 13.7%, less than 14: 5.82% vs 3.0%, less than 19: 1.58% vs 0.8%). This trend in hospital employees aligns with findings from a group of nurses\u003csup\u003e[49]\u003c/sup\u003e (N = 442, 57.69%, 28.28%, 8.6%, 3.6%, 1.83%, respectively). The reason this study focuses on depression among all staff members, not just nurses, because\u0026mdash;although it cannot be denied that nurses are exposed to more stress than doctors due to their position, it should be considered that stress caused by conflicts in the doctor-patient relationship affects all staff members. The current results indeed support this notion, suggesting that working in a hospital (at least in China) is a stressful environment that significantly impacts on the mental state of staff.\u003c/p\u003e\n\u003cp\u003eIndividual differences in perceived stress were suggested as an important factor in the onset of depression. However, because the effects of internal (e.g., gene expression) or external factors (e.g., gender) on the variations in perceived stress and depression are similar, it has not been easy to clarify whether a causal relationship between perceived stress and depression exists. Previous studies have reported that demographic characteristics influence both perceived stress and depression\u003csup\u003e[13, 23]\u003c/sup\u003e. In that case, it is more reasonable to consider that perceived stress and depression are likely covariates and that their correlation can be reasoned by the influence of demographic characteristics. This pattern (which was represented in the current study as F-model) can does little to clarify the causal relationship between perceived stress and depression. To extend it, the current study used a model comparison approach, considering the explanatory power of the data as the criterion for evaluating \u0026ldquo;which relationships are reasonable\u0026rdquo;. The results showed H-model (i.e., a high correlation between perceived stress and depression, but demographic characteristics only affect perceived stress) was more suitable than F-model, suggesting that a causal relationship between stress perception and depression provides a better explanation for the current data than a non-causal relationship. In addition, the explanatory power clearly dropped when C-model was used, indicating that the influence of demographic characteristics on stress perception makes an indispensable and important contribution to explaining the data. These findings suggest a clear causal relationship in which demographic characteristics alter perceived stress, which in turn is associated with changes in depression severity.\u003c/p\u003e\n\u003cp\u003ePrevious studies have shown that demographic characteristics may affect perceived stress and that perceived stress is highly correlated with depression. Another aim of this study is to explore how to interpret the relationship between these findings. Given that individual differences in perceived stress can be attributed to variations in socio-environmental condition difference, we hypothesized that if demographic characteristics clearly indicate that a person is exposed to chronic stress, then people with these characteristics should be more likely to feel stress in similar stressful environments than those without them, and that this, in turn, should affect depression severity We also investigated how demographic characteristics affect specific psychological components, such as the latent factors of perceived stress (self-efficacy and distress), rather than simply examining whether they increase or decrease overall perceived stress. The results showed that self-efficacy decreased with age and that individuals tagged by \u0026ldquo;female\u0026rdquo; or \u0026ldquo;marital experience\u0026rdquo; reported higher levels of distress. Since the effect of marital experience was slightly higher than that of gender, it is thought that the increased distress associated with marital experience reflects the impact of changes in social relationships, rather than purely physiological differences between the sexes\u003csup\u003e[6]\u003c/sup\u003e.\u0026nbsp;Commonly, after a change in marital status, social relationships become more complex and interpersonal conflicts that individuals must navigate increase. People in such situations can be considered as being exposed to chronic stress. In this sense, the finding that married individuals experience more distress than unmarried individuals can be interpreted as the result of exposure to chronic stress from managing more complex social relationships over time, rather than simply the impact of a one-time stressful life event.\u0026nbsp;However, this influence was not obviously enough to overcome the noise arising from individual differences because demographic characteristics had little direct effect on the fit of the model (Table S1 \u0026amp; S2). This may also help explain why previous studies have not reported a similar effect.\u0026nbsp;Nevertheless, it can be suggested that the current findings highlight the relationship between chronic stress and perceived stress, revealing that the chronic stress related demographic characteristics can increase perceived stress under similar socio-environmental conditions (e.g., the same occupation).\u0026nbsp;These findings are consistent with related studies suggesting that stress should be a causal factor in the development of depression, and extend the understanding of that marital experience may generate a chronic stress situation which increases perceived stress and ultimately promotes the development of depression.\u003c/p\u003e\n\u003cp\u003eThere are several limitations that should be acknowledged. First, due to privacy concerns regarding the open collection of staff data at the hospital, we did not collect demographic information such as income or working hours (as doing so may have violated hospital regulations). Although these workplace-related characteristics may also affect perceived stress and depression, the current analysis could not eliminate their influence. This is considered one of the limitations of this study. Second, because the data in this study were collected from participants in a natural setting within a specific organization, we were unable to use experimental manipulation to control for differences in the number of categories sampled for a given characteristic. This limitation may have led to either overestimation or underestimation of the effects of certain demographic characteristics. Future research should explore whether the results can be replicated by addressing this factor. Third, due to the fact that a strong positive correlation is not sufficient to conclusively a causal relationship. Hence, even if demographic characteristics influence perceived stress, this does not necessarily mean they lead to more severe depressive symptoms. It is possible that depression can affect perceived stress in conjunction with demographic characteristics, particularly if depression is treated as an internal stressor\u003csup\u003e[50]\u003c/sup\u003e. The current findings cannot rule out this possibility and therefore it should be tested in further investigation.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eSince Selye proposed the psychological concept of \u0026quot;stressor,\u0026quot; many researchers have examined how stress caused by environmental stressors is related to depression. While it is widely accepted that stress is an important trigger for the onset of depression, relatively little research has focused on individual differences in how stressors affect different individuals. In this study, the variation in perceived stress reported by individuals working in similar environments, depending on demographic characteristics, can be interpreted as reflecting such individual differences. Moreover, the results indicated that gender and changes in marital status make individuals more susceptible to stress, and correspondingly, their depressive symptoms more pronounced. These findings support the notion that perceived stress plays a more critical role in the onset of depression than stressful life events themselves.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank Google translate and ChatGPT for the English language review.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWL, HD, and HL contributed to the study\u0026rsquo;s conception and design, conducted statistical analysis, acquired data, and wrote the initial draft of the manuscript. WL was responsible for revising and addressing questions during the peer review process. All authors approved the final version of the manuscript for submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Funding for Scientific Research Projects from Wuhan Municipal Health Commission under Grant WX23Q35 \u0026amp; WX23Z64, Science Foundation of the Hubei Province, China under Grant 2025AFC007; and Scientific Research Foundation of Wuhan Hankou Hospital under Grant HKYY2025005 \u0026amp; HKYY2025012. The funder had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe studies involving human participants were reviewed and approved by Ethics Committee of Wuhan Hankou Hospital (hyll2023060). Written Informed consent was obtained from all patients/participants after a full explanation of the nature of the study and possible risks, and all methods were performed according to the relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLi T, Shi L, Xia Y, Shi Z, Wang D. Recent trend in the prevalence and correlates of depression among Chinese young adults from 2010 to 2018. 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Psychol Sci. 2022;33(1):152\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"perceived stress, depression, demographic characteristics, chronic stress, hospital employees","lastPublishedDoi":"10.21203/rs.3.rs-7532487/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7532487/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eIt is well known that the occurrence of stressful life events increases the incidence of depression; thus, stress has been considered an important trigger for the development of depression. However, recent research has suggested that \u003cem\u003eperceived stress\u003c/em\u003e\u0026mdash;the extent to which a person feels stressed\u0026mdash;is more closely related to depression. In line with this notion, individual differences in perceived stress are expected to be more strongly linked to depression. However, little is known about how these individual differences influence depression.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e\u003cp\u003eThis study investigated whether demographic characteristics can alter the relationship between perceived stress and depressive severity by using a sample of hospital employees.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eIt was found that perceived stress was positively related to depression (\u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.67); however, only a causal relationship from demographic characteristics to perceived stress was identified. The results of the best-fitting model (H-model) indicated that self-efficacy tends to decline with aging, whereas distress increases among individuals who are tagged by special characteristics \u0026ldquo;female\u0026rdquo; and \u0026ldquo;having marital experience\u0026rdquo;.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThese findings indicated that individual differences in perceived stress may be due to chronic stress environments and, in turn, adversely affect depression severity, highlighting the crucial role of perceived stress on the onset of depression.\u003c/p\u003e","manuscriptTitle":"The influence of chronic stress reflected in demographic characteristics on perceived stress and depression––Evidence from hospital employees","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-08 09:24:21","doi":"10.21203/rs.3.rs-7532487/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-10-08T08:31:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"188401448294703615861915967448256423815","date":"2025-10-08T06:42:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"226808939172583130702003881419005243586","date":"2025-09-28T09:21:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-26T06:39:24+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-08T09:27:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-05T06:46:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-05T06:45:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychology","date":"2025-09-04T05:41:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c48cc3a9-26d5-4d77-bfe2-32276c112cb4","owner":[],"postedDate":"October 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-08T09:24:21+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-08 09:24:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7532487","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7532487","identity":"rs-7532487","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}