Introduction
Occupational stress has been a significant focus of academic research since the 1960s, with extensive documentation of its impact on both health and job performance. For clinical nurses, who are at the frontline of patient care, the implications of occupational stress are particularly profound. Research indicated that high levels of occupational stress among clinical nurses can lead to severe consequences, including increased rates of job burnout [1] and affective disorders [2‐4]. Occupational stress not only affects the personal well-being of clinical nurses but also has significant implications for patient safety and the quality of care provided [5, 6]. The COVID-19 pandemic has exacerbated these stressors [7, 8], pushing clinical nurses to their limits and highlighting the urgent need for effective occupational stress evaluation and management strategies [3, 9‐15]. The unique challenges faced by clinical nurses—such as managing infectious disease outbreaks, dealing with workplace violence, and balancing increased research responsibilities—required a tailored approach to stress assessment and intervention.
Existing tools for measuring occupational stress, such as the Occupational Stress Inventory and the General Job Stress Questionnaire [16‐18], along with specialized scales like the Nurse Stressor Scale [19] and the Nursing Stress Scale [20] have provided valuable insights. However, these instruments often fall short in capturing the full spectrum of stressors experienced by clinical nurses, such as outbreaks of infectious diseases, workplace violence, and research stress. In addition, little attention has been paid to the impact of childhood stress on later occupational stress. Childhood stressors, such as abuse or neglect, can significantly influence an individual’s vulnerability to stress in adulthood. Research has shown that early-life adversity can heighten an individual’s sensitivity to stress, leading to an increased risk of experiencing significant stress in their professional lives [21, 22]. Individuals who endured such adverse experiences during childhood are more likely to encounter elevated stress levels in adulthood, which can predispose them to various psychiatric disorders [23, 24]. This connection underscores the necessity of incorporating considerations of childhood stress into assessments of occupational stress. Traditional instruments may overlook these underlying factors, potentially missing a critical component that affected how individuals respond to workplace stressors. As a result, the development of updated and comprehensive tools for assessing occupational stress was essential. These instruments should not only evaluate current stressors but also account for the long-term effects of early-life experiences on stress responses.
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The Medical Staff Occupational Stress Scale (MSOSS) was developed by our team with several key innovations. Firstly, item generation for the MSOSS utilized both qualitative and quantitative methodologies to capture the most prevalent stressors experienced by medical staff. Secondly, the MSOSS included a sub-questionnaire specifically addressing childhood stress. Thirdly, the assessment method differentiates occupational stressors from mental pressure. The development of the MSOSS involved data collected from 650 clinical nurses in a specific hospital setting. The final scale comprises 42 items, including a childhood stress dimension and seven work-related stress dimensions: workplace violence stress, relationship with patients stress, teaching stress, research stress, contagion stress, working environment stress, and administrative stress [25]. Psychometric analysis of the MSOSS during development revealed a high Cronbach’s alpha coefficient of 0.968, indicating strong reliability. The scale’s structure was deemed acceptable, with most items exhibiting factor loadings above 0.5 [25].
The current study advanced research on the MSOSS by examining its psychometric properties across a diverse provincial cohort of clinical nurses from various hospital levels. The results broaden the applicability of the MSOSS, enhancing its utility as a robust tool for assessing occupational stress among Chinese clinical nurses. By confirming the MSOSS’s reliability across different hospital settings and nurse demographics, this research enables more accurate and contextually relevant stress assessments. Measurement invariance ensures that the MSOSS consistently measures occupational stress in the same way, regardless of age, work duration, or job title [26, 27]. Precious assessment of occupation stress is crucial for developing targeted interventions and support programs for nurses, ultimately contributing to better mental health outcomes, enhanced job quality, and improved overall quality of nursing care.
Methods
Participants
This study was granted ethical approval by the Institutional Review Board of Hainan Medical University, with number HYLL-2021-364. The study was conducted in accordance with the tenets of the Declaration of Helsinki. The participants were asked to sign written informed consent and informed consent to participate was obtained from all of the participants in the study. The results of the survey will be used solely for scientific reporting and the promotion of nurses’ mental health. Personal information identifying any individual respondent will not be released.
This study encompassed clinical nurses employed in hospitals located in Hainan Province, specifically focusing on those working in environments characterized by multiple stressors, including those related to teaching and scientific research activities. By including nurses from hospitals with a diverse array of professional stressors, the study sought to obtain a comprehensive understanding of stress experiences encountered by this group. Nurses from community hospitals were excluded from the study due to their limited involvement in research and teaching activities. Tertiary hospitals in China are hospitals with more than 501 beds, typically larger in scale, they contain high-quality medical resources and provide high-level clinical nursing services. Secondary hospitals in China also undertaking multiple medical tasks, they generally have a lower patient volume and less complex medical conditions, with 101–500 beds. Ultimately, the study sampled nurses from 26 Tertiary hospitals and 28 Secondary hospitals hospitals, ensuring a diverse group of nurses with varied professional backgrounds and occupational stressors. This approach allowed for a comprehensive assessment of the MSOSS across different levels of clinical nurses.
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An investigation team was established, with the main investigator having majored in clinical nursing. An online cross-sectional survey was conducted from January 1st 2022 to May 31st 2022. Informed consent was obtained from participants electronically. Nurses from the sampled hospitals who provided informed consent were eligible to participate in the survey. In total, 3585 nurses participated in the survey. After the exclusion of surveys with incomplete data, a total of 2989 valid surveys were obtained, with a response rate of 83.38%. The inclusion criteria were as follows: (1) has a clinical nurse qualification certificate; (2) is a formal full-time nurse at the sampled hospital. The exclusion criteria included: (1) nursing assistants; (2) temporary workers engaged in nursing jobs. The survey had been reported [25].
Measures
Socio-demographics
The participants’ socio-demographic data were collected, including gender, age, level of hospital they are were employed in, highest level of education, marital status, occupation duration and job title.
Occupational stress status
The MSOSS, developed by our team, was used to assess participants’ occupational stress. It consisted of 42 items across eight dimensions, using a five-point Likert scale to evaluate both the frequency and mental pressure of stressors (Appendix 1). The total mental pressure score for each item was calculated by multiplying the frequency and mental pressure scores, with a maximum possible total score of 1050. The MSOSS demonstrated acceptable Cronbach’s alpha and structural validity in a sample of 650 nurses from a tertiary hospital in China [25]. The eight dimensions of the MSOSS were identified through factor analysis. Each dimension was described and titled as follows:
F1—Childhood Stress (Five Items): this dimension examined stress originating from childhood experiences. Research indicated that adverse childhood stressors, can have a long-term impact, persisting into adulthood and influencing overall stress sensitivity and health responses [28‐30].This dimension highlighted the enduring long-term impact of childhood stress on adult stress experiences.
F2—Working Environment Stress (five items): this dimension reflected occupational stress associated with the physical and organizational aspects of the work environment, such as outdated medical equipment (diagnostic, therapeutic, or assistive devices) and insufficient patient numbers. Stress related to the working environment was a serious issue and a significant concern [31]. A challenging work environment can significantly impact stress experience.
F3—Contagion Stress (Six Items): this dimension addressed stress resulting from exposure to infectious diseases or health crises. Working in environments with high contagion risks can amplify stress due to concerns about both personal and patient safety. According to Transactional Model of Stress and Coping [32, 33], stress arised from the perceived imbalance between demands and resources. In high contagion risk settings, healthcare professionals may perceive the demands (e.g., risk of infection) as surpassing their available coping resources, thereby intensifying their stress experience.
F4—Occupational Violence Stress (nine items): violence and aggression toward healthcare workers were significant global public health issues [34‐36]. Recent reports indicated a concerning troubling prevalence of such violence in medical settings, including in China [37]. This dimension of the scale addressed the stress related to workplace violence experienced by healthcare professionals, encompassing physical, psychological, and sexual violence.
F5—Teaching Stress (four items): this dimension examined the stress linked to teaching and mentoring responsibilities. In many Chinese Medical Universities, experienced healthcare professionals, including nurses, are expected to teach as part of their roles. This expectation supports the integration of practical experience with academic training but can add significant stress. This stress impacts job satisfaction and performance, as explained by Role Strain theory [38], which posits that additional roles can lead to strain when they conflict with existing duties. This challenge is intensified in high-pressure healthcare settings where job demands are already substantial.
F6—Research Stress (Four Items): this dimension addressed stress related to research activities. In China, secondary and tertiary hospitals are research-oriented institutions that integrate medical services, technology innovation, and scientific research [39]. Medical staff including clinical nurses, were required to engage in scientific research, which involved long-term stressors such as writing papers and applying for funding. These long-term stressors can accumulate, leading to burnout, reduced job satisfaction, and potentially adverse effects on patient care.
F7—Relationship with Patient Stress (six items): this dimension addressed the stress healthcare professionals experienced from their interactions with patients. The nurse–patient relationship involved work-oriented, interpersonal, and caring interactions established through nursing activities. Stress can arise from managing challenging patient behaviours and high emotional demands while maintaining empathy and professionalism, a concept explored through Emotional Labour Theory [40‐42].
F8—Administration Stress (Three Items): this dimension focused on stress arising from bureaucratic procedures within organizations, such as issues with subordinates not following work arrangements and feeling wronged by superiors. Persistent organizational administrative stress can contribute to burnout among nurses, worsening mental health and leading to reduced quality of nursing care [43].
Psychiatric symptoms
The Chinese version of the Patient Health Questionnaire Depression Module (PHQ-9) was utilized to measure each participant’s depressive symptoms [44]. Each item was measured in terms of the frequency of symptom occurrence on a rating scale from 0 (never) to 3 (daily). A higher total score (possible range 0–27) indicated a greater prevalence of depressive symptoms. The Chinese version of the PHQ-9 has been reported to have a Cronbach’s alpha of 0.86 [45].
Anxiety symptoms were assessed using the Generalized Anxiety Disorder scale (GAD-7), a seven-item self-report instrument. Participants rated how often they have been troubled by anxiety on a four-point Likert scale ranging from 0 (not at all) to 3 (nearly every day). A higher total score indicated greater intensity of anxiety symptoms. The GAD-7 showed good psychometrics [46].
Statistics
SPSS 27.0 statistical software was used to generate descriptive statistics and analyse the reliability and validity of the scale. Mplus software was used to test the confirmative factor structure and measurement invariance of the scale among nurses. The equivalence test (measurement invariance) included: ① configural equivalence, to check whether the structure of the latent variables was the same in different groups; ② metric invariance (weak invariance), to test whether the factor loadings were equal across groups; ③ scalar invariance, also known as strong invariance, to test whether the intercept of the observed variables was equivalent across groups; ④ residual invariance, to test whether the error variances were equal across different groups.
Results
Socio-demographic characteristics of the sample
Among the 2989 nurses who completed the survey, 1639 nurses worked in secondary hospitals (54.8%) and 1350 worked in tertiary hospitals (45.2%). The detailed socio-demographic data were shown in Table 1.
Table 1
Participants’ socio-demographic characteristics
Variable | Category | Number | Percentage |
|---|---|---|---|
Hospital level | Secondary hospital | 1639 | 54.8% |
Tertiary hospital | 1350 | 45.2% | |
Gender | Male | 78 | 2.6% |
Female | 2911 | 97.4 | |
Age | 20–29 years | 1361 | 45.5% |
30–39 years | 1149 | 38.4% | |
40–49 years | 411 | 13.8 | |
50 years and above | 68 | 2.3% | |
Highest education degree | College | 1637 | 54.8% |
Bachelors | 1348 | 45.1% | |
Masters | 4 | 0.1% | |
Marital status | Married/De Facto | 2259 | 75.6% |
Single | 730 | 24.4% | |
Working duration | 0–10 years | 1838 | 61.5% |
More than 10 years | 1151 | 38.5% | |
Job title | Junior | 2023 | 67.7% |
Senior or above | 966 | 32.3% |
Reliability
Internal consistency represented the extent to which different items were correlated. It was assessed using the Cronbach’s alpha coefficient, omega coefficient and Spearman-Brown coefficient. In this study, the Cronbach’s alpha coefficient was calculated for different cohorts of participants. All coefficients presented in Table 2 indicated that the scale had good internal consistency, with values greater than 0.7 reflecting reliable measurement [32].
Table 2
Reliability of MSOSS
Category | Cronbach’s alpha coefficient | Omega coefficient | Spearman-Brown coeffecient | |
|---|---|---|---|---|
Hospital level | Secondary hospital | 0.963 | 0.957 | 0.888 |
Tertiary hospital | 0.955 | 0.967 | 0.905 | |
Age | 20–29 years old | 0.965 | 0.959 | 0.898 |
30–39 years old | 0.957 | 0.953 | 0.883 | |
≥ 40 years old | 0.951 | 0.966 | 0.901 | |
Working duration | 1–10 years | 0.964 | 0.961 | 0.898 |
> 10 years | 0.959 | 0.965 | 0.901 | |
Job titles | Junior | 0.963 | 0.956 | 0.894 |
Senior and above | 0.954 | 0.957 | 0.888 |
Composite reliability and convergent validity
Composite reliability (CR), also known as construct reliability, measured the internal consistency of scale items [47]. It represented the proportion of true score variance relative to the total scale score variance. Convergent validity required that multiple measures of a construct related highly to each other and less highly to measures of other constructs. The convergent validity of the MSOSS was assessed by the average variance extracted (AVE) value. The convergent validity of a scale was considered acceptable when AVE was above 0.50 [48]. As shown in Tables 3, 4 and 5, the CR in different cohorts were acceptable.
Table 3
Composite reliability of MSOSS in different level hospitals
Path | Hospital level | CR |
|---|---|---|
F1 | Secondary hospital | 0.4267 |
Tertiary hospital | 0.4385 | |
F2 | Secondary hospital | 0.7915 |
Tertiary hospital | 0.744 | |
F3 | Secondary hospital | 0.8699 |
Tertiary hospital | 0.8445 | |
F4 | Secondary hospital | 0.931 |
Tertiary hospital | 0.9272 | |
F5 | Secondary hospital | 0.772 |
Tertiary hospital | 0.7815 | |
F6 | Secondary hospital | 0.8662 |
Tertiary hospital | 0.8355 | |
F7 | Secondary hospital | 0.8993 |
Tertiary hospital | 0.8974 | |
F8 | Secondary hospital | 0.7888 |
Tertiary hospital | 0.7891 | |
AVE (Secondary hospital) | 0.6512 | |
AVE (Tertiary hospital) | 0.6319 | |
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Table 4
Composite reliability of MSOSS in different age cohort
Path | Age cohort | CR |
|---|---|---|
F1 | 20–29 years old | 0.4236 |
30–39 years old | 0.4359 | |
≥ 40 years old | 0.4413 | |
F2 | 20–29 years old | 0.7884 |
30–39 years old | 0.7464 | |
≥ 40 years old | 0.74529 | |
F3 | 20–29 years old | 0.8960 |
30–39 years old | 0.8454 | |
≥ 40 years old | 0.8478 | |
F4 | 20–29 years old | 0.9445 |
30–39 years old | 0.9275 | |
≥ 40 years old | 0.8968 | |
F5 | 20–29 years old | 0.7820 |
30–39 years old | 0.7897 | |
≥ 40 years old | 0.7173 | |
F6 | 20–29 years old | 0.8782 |
30–39 years old | 0.8282 | |
≥ 40 years old | 0.8353 | |
F7 | 20–29 years old | 0.9170 |
30–39 years old | 0.9066 | |
≥ 40 years old | 0.8844 | |
F8 | 20–29 years old | 0.8158 |
30–39 years old | 0.7827 | |
≥ 40 years old | 0.7084 | |
AVE(20–29 years old) | 0.6731 | |
AVE(30–39 years old) | 0.6333 | |
AVE(≥ 40 years old) | 0.5957 | |
Table 5
Composite reliability of MSOSS in different working duration cohort
Path | Working duration | CR |
|---|---|---|
F1 | 1–10 years | 0.4103 |
> 10 years | 0.4367 | |
F2 | 1–10 years | 0.7838 |
> 10 years | 0.7617 | |
F3 | 1–10 years | 0.8719 |
> 10 years | 0.8561 | |
F4 | 1–10 years | 0.9389 |
> 10 years | 0.9277 | |
F5 | 1–10 years | 0.7898 |
> 10 years | 0.7708 | |
F6 | 1–10 years | 0.8739 |
> 10 years | 0.8441 | |
F7 | 1–10 years | 0.9132 |
> 10 years | 0.9068 | |
F8 | 1–10 years | 0.8059 |
> 10 years | 0.7826 | |
AVE(1–10 years) | 0.6619 | |
AVE(> 10 years) | 0.6382 | |
Construct validity
In psychometrics, validity referred to the extent to which evidence and theory support the interpretations of the test scores as derived from the proposed use of the test [4]. Construct validity referred to the extent to which a construct (e.g., a practical test developed from a theory) accurately measured the defined theory. It was generally recognized that CFI and TLI values above or equal to 0.9 indicated a satisfactory model fit; the closer the values are to 1, the better the model ideal [49]. As shown in Table 6, the construct validity of MSOSS in different cohorts was acceptable.
Table 6
Construct validity of MSOSS in different cohorts
Category | Model fit indices | |||||
|---|---|---|---|---|---|---|
χ2/df | RMSEA | CFI | IFI | TLI | ||
Hospital level | Secondary hospitals | 2.804 | 0.054 | 0.905 | 0.905 | 0.896 |
Tertiary hospitals | 2.904 | 0.054 | 0.896 | 0.896 | 0.887 | |
Age | 20–29 years old | 5.686 | 0.059 | 0.898 | 0.899 | 0.890 |
30–39 years old | 4.128 | 0.052 | 0.902 | 0.903 | 0.894 | |
≥ 40 years old | 2.324 | 0.053 | 0.884 | 0.885 | 0.874 | |
Working duration | 1–10 years | 3.615 | 0.056 | 0.903 | 0.903 | 0.894 |
> 10 years | 6.97 | 0.053 | 0.903 | 0.903 | 0.894 | |
Job titles | Junior | 6.879 | 0.054 | 0.907 | 0.907 | 0.899 |
Senior and above | 3.709 | 0.053 | 0.892 | 0.892 | 0.882 | |
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Empirical validity was also known as predictive validity or criterion-related validity. The empirical validity of the MSOSS was assessed by examining the correlations between the MSOSS and the depression and anxiety scores obtained from the PHQ-9 and GAD-7. All correlations were significant as shown in Table 7.
Table 7
Occupational stress and affective disorders among clinical nurses
Hospital level | Psychiatric symptoms | Correlation | P |
|---|---|---|---|
Secondary hospital | PHQ-9 | 0.431** | 0.000 |
GAD-7 | 0.430** | 0.000 | |
Tertiary hospital | PHQ-9 | 0.442** | 0.000 |
GAD-7 | 0.462** | 0.000 |
Structural invariance of the MSOSS
Next, multiple-group analysis was performed to examine the structural invariance of the scale across hospital levels. Hospital level was divided into two groups: Level-3 and Level-2. The model fit indices were as follows: CFI = 0.959, TLI = 0.951, RMSEA = 0.032. Each fit index was shown in Table 8.
Table 8
Measurement invariance across hospital level
Model | χ2 | CFI | TLI | RMSEA(90%CI) | SRMR | ∆CFI | ∆TLI | ∆RMSEA | Decision | |
|---|---|---|---|---|---|---|---|---|---|---|
M1 | Configural invariance | 3557.160 | 0.959 | 0.951 | 0.032 (0.030–0.033) | 0.040 | - | - | - | Accept |
M2 | Metric invariance | 3620.622 | 0.959 | 0.952 | 0.031 (0.030–0.033) | 0.045 | 0.000 | 0.001 | 0.001 | Accept |
M3 | Scalar invariance | 3752.142 | 0.957 | 0.951 | 0.031 (0.030–0.033) | 0.046 | 0.002 | 0.001 | 0.000 | Accept |
M4 | Residual invariance | 3859.075 | 0.956 | 0.951 | 0.031 (0.030–0.033) | 0.048 | 0.001 | 0.000 | 0.000 | Accept |
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Multiple-group analysis was then performed to examine the structural invariance of the scale across age. Age was divided into three groups: 20–29 years, 30–39 years and ≥ 40 years. The model fit indices were shown in Table 9. The following fit indices were obtained for the baseline model: TLI = 0.950, CFI = 0.959, RMSEA = 0.032. The results indicated that the metric invariance, scalar invariance and residual invariance were all acceptable.
Table 9
Measurement invariance across age
Model | χ2 | CFI | TLI | RMSEA(90%CI) | SRMR | ∆CFI | ∆TLI | ∆RMSEA | Decision | |
|---|---|---|---|---|---|---|---|---|---|---|
M1 | Configural invariance | 4430.004 | 0.959 | 0.950 | 0.032 (0.031–0.034) | 0.041 | - | - | - | Accept |
M2 | Metric invariance | 4577.403 | 0.957 | 0.951 | 0.032 (0.031–0.034) | 0.052 | 0.002 | 0.001 | 0.000 | Accept |
M3 | Scalar invariance | 4840.486 | 0.954 | 0.949 | 0.033 (0.032–0.034) | 0.053 | 0.003 | 0.002 | 0.001 | Accept |
M4 | Residual invariance | 5122.207 | 0.950 | 0.947 | 0.034 (0.032–0.035) | 0.056 | 0.004 | 0.002 | 0.001 | Accept |
Next, multiple-group analysis was performed to examine the structural equivalence of the scale across working duration. Working duration was divided into two groups: 1–10 years and > 10 years. The model fit indices for the baseline model were as follows: CFI = 0.965, TLI = 0.957, RMSEA = 0.030. Each fit index was shown in Table 10.
Table 10
Measurement invariance across work duration
Model | χ2 | CFI | TLI | RMSEA(90%CI) | SRMR | ∆CFI | ∆TLI | ∆RMSEA | Decision | |
|---|---|---|---|---|---|---|---|---|---|---|
M1 | Configural invariance | 3272.725 | 0.965 | 0.957 | 0.030 (0.028–0.031) | 0.038 | - | - | - | Accept |
M2 | Metric invariance | 3348.605 | 0.964 | 0.957 | 0.030 (0.028–0.031) | 0.044 | 0.001 | 0.000 | 0.000 | Accept |
M3 | Scalar invariance | 3545.809 | 0.961 | 0.955 | 0.030 (0.029–0.032) | 0.045 | 0.003 | 0.002 | 0.000 | Accept |
M4 | Residual invariance | 3812.844 | 0.957 | 0.951 | 0.032 (0.030–0.031) | 0.049 | 0.004 | 0.004 | 0.002 | Accept |
Multiple-group analysis was also performed to examine the structural invariance of the scale across job titles. Job title was divided into two groups: junior and senior (or above). As shown in Table 11, the model fit indices were as follows: CFI = 0.959, TLI = 0.951, RMSEA = 0.032. Each fit index met the requirements for configural invariance. The metric invariance, scalar invariance and residual invariance of the scale across job titles were acceptable.
Table 11
Measurement invariance across job titles
Model | χ2 | CFI | TLI | RMSEA(90%CI) | SRMR | ∆CFI | ∆TLI | ∆RMSEA | Decision | |
|---|---|---|---|---|---|---|---|---|---|---|
M1 | Configural invariance | 3185.765 | 0.967 | 0.959 | 0.029 (0.028–0.031) | 0.038 | - | - | - | Accept |
M2 | Metric invariance | 3285.829 | 0.965 | 0.958 | 0.029 (0.028–0.031) | 0.045 | 0.002 | 0.001 | 0.000 | Accept |
M3 | Scalar invariance | 3520.215 | 0.962 | 0.955 | 0.030 (0.029–0.032) | 0.047 | 0.003 | 0.003 | 0.001 | Accept |
M4 | Residual invariance | 3866.527 | 0.956 | 0.950 | 0.032 (0.031–0.033) | 0.051 | 0.006 | 0.005 | 0.002 | Accept |
Sensitivity and specificity of the MSOSS as a depression screening tool
The Chinese version of the PHQ-9 had good reliability and validity [11] and based on previous studies, a cut-off of 10 was considered positive for depression disorder [50]. As shown in Fig. 1, the area under the receiver operating characteristic curve (ROC curve) of the PHQ-9 was 0.712 (95%CI: 0.690–0.733). The maximum Youden index (sensitivity + specificity − 1) was used as the criterion to determine the cut-off value. According to the sensitivity values in Tables 12 and 13, a cut-off value of 102.5 was selected to identify depression. Therefore, a total MSOSS score > 102 was defined as positive for possible depression.
Fig. 1
Sensitivity and specificity of the MSOSS as a depression screening-tool
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Table 12
Area under the curve
Area | Std. Errora | Asymptotic Sig.b | Asymptotic 95% Confidence Interval | |
|---|---|---|---|---|
Lower Bound | Upper Bound | |||
0.712 | 0.011 | 0.000 | 0.690 | 0.733 |
Table 13
Sensitivity of the MSOSS as a depression screening-tool
Positive if Greater Than or Equal Toa | Sensitivity | 1 - Specificity | |
|---|---|---|---|
102.5 | 0.801 | 0.473 | 0.328 |
104.5 | 0.794 | 0.466 | 0.328 |
136.5 | 0.682 | 0.354 | 0.328 |
Sensitivity and specificity of the MSOSS as an anxiety screening tool
GAD-7 score greater than 10 was considered indicative of generalized anxiety disorder [51]. As shown in Table 14, the AUC of the GAD-7 was 0.772 (95%CI: 0.746–0.798); see Fig. 2. The maximum Youden index was 0.438, and 207.5 (Table 15) was identified as the critical value. Therefore, a total MSOSS score > 207 was considered positive for possible anxiety.
Fig. 2
Sensitivity and specificity of the MSOSS as an anxiety screening-tool
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Table 14
Area under the curve
Area | Std. Errora | Asymptotic Sig.b | Asymptotic 95% Confidence Interval | |
|---|---|---|---|---|
Lower Bound | Upper Bound | |||
0.772 | 0.013 | 0.000 | 0.746 | 0.798 |
Table 15
Sensitivity of the MSOSS as an anxiety screening-tool
Positive if Greater Than or Equal Toa | Sensitivity | 1 - Specificity | |
|---|---|---|---|
207.5 | 0.761 | 0.323 |
Discussion
The COVID-19 pandemic and its aftermath have imposed unprecedented challenges on clinical nurses, intensifying their workload and exposing them to numerous stressors [15]. Today, clinical nurses are tasked with managing a broad range of responsibilities, including combating the pandemic, performing routine clinical tasks, teaching students, managing patient relationships, and conducting scientific research. Despite these extensive demands, there remains a significant gap in thoroughly assessing their occupational stress. This study aimed to address this gap by evaluating the psychometric properties and measurement invariance of the MSOSS, a novel instrument designed to measure the multidimensional occupational stress experienced by medical professionals. The study examined the psychometric properties and measurement invariance of MSOSS in a large cohort of clinical nurses. The results demonstrated the MSOSS’s strong reliability, with Cronbach’s alpha coefficients exceeding 0.90 for both secondary and tertiary hospitals [52, 53]. Additionally, the composite reliability estimates were found to be acceptable.
Beyond reliability, the study also demonstrated the validities of the MSOSS. The results of the current study indicated that the convergent validity of the MSOSS was acceptable, due to the AVEs were higher than 0.50 [48]. CFI and TLI values approximately 0.9 of the Multi-group CFA indicated a satisfactory structural validity of the MSOSS [49]. Additionally, the empirical validity of the MSOSS was confirmed, with significant correlations found between MSOSS scores and scores on the PHQ-9 and GAD-7. Measurement invariance ensures consistent interpretation of an instrument across groups [54]. When the factor structure, factor loadings and intercepts are equal across groups, latent means can be meaningfully compared across the groups [55]. For the MSOSS, this was tested across sub-groups of nurses based on age, work duration, job title, and hospital levels [56, 57]. The indices indicated that the MSOSS scores accurately reflect respondents’ occupational stress, independent of age, work duration, job title, or hospital level, as the equivalence bounds for ∆CFI and ∆TLI were both less than 0.01 [58]. Therefore, comparisons across different sub-groups are valid.
Current research demonstrated that childhood stress, particularly negative experiences, can have a profound impact on an adult’s mental health [59, 60] and mortality [61]. Adverse childhood stress may alter the habituation of the HPA axis to repeated stress later in life [62]. Given these findings, nurses with a history of childhood stress may be particularly vulnerable to experiencing occupational stress [63, 64], which can adversely affect their well-being, job performance, and patient care which could adversely affect their well-being, job performance, and patient care. To address and prevent this risk, the MSOSS can be utilized to identify clinical nurses who are at high risk for occupational stress. Interventions such as cognitive-behavioral therapy (CBT) [65], and strategies aimed at reducing exposure to social stress [66] can help these individuals manage past adverse stress, develop effective coping strategies, and build emotional resilience. Furthermore, support groups and psycho-education can support recovery and personal growth.
Depressive disorders account for 40.5% of disability-adjusted life years (DALYs), and anxiety disorders account for 14.6% of DALYs [55]. To effectively address these conditions, early warning models should incorporate timely assessment and monitoring of upstream factors, such as occupational stress, which significantly contribute to their development [67]. Identifying a cut-off for occupational stress could lead to more effective and cost-efficient public prevention and clinical treatment strategies for the early warning of depression and anxiety disorders [4, 68]. ROC curve, was used to identify the cut-off of MSOSS on the true positive rate of affective disorders (depression and anxiety) against the false positive rate at threshold setting. This study found that MSOSS cut-off values of 102 and 207 could indicate depression and anxiety, respectively.
If clinical nurses’ occupational stress was not effectively evaluated and addressed through psychological assessment and intervention, it can lead to a range of adverse consequences affecting both the nurses’ well-being and the quality of care they provide. Prolonged stress can significantly impact nurses’ mental health, resulting in issues such as anxiety, depression, and physical diseases [69]. This stress can also contribute to job burnout [70], characterized by emotional exhaustion and reduced job satisfaction. Furthermore, occupational stress can impair concentration and judgment, increasing the risk of nursing errors and accidents. Thus, addressing occupational stress is crucial for maintaining both nurses’ health and high standards of patient care. The MSOSS could be administered to clinical nurses to screen for occupational stress in order to minimize the potential risks of occupational stress and provide timely and precise psychological support and clinical interventions.
Strengths and limitations
The findings of this study provided ample support for the use of the MSOSS as a screening tool to assess occupational stress among clinical nurses. The study demonstrated that the newly developed MSOSS has a stable structure and measurement equivalence across different cohorts. However, several limitations should be noted. First, to establish causal relationships between childhood stress, occupational stress, and affective disorders, further prospective longitudinal studies are needed. Second, the survey participants were from Hainan Province, which may not fully represent nurses across China or globally.
Conclusion
Nursing is the backbone of any healthcare system and is essential to the health and well-being of all nations. Addressing occupational stress in nursing is crucial for preventing psychosomatic diseases and maintaining job efficiency. This study indicated that the 42-item MSOSS was a reliable and valid tool for assessing occupational stress among clinical nurses. It effectively measured and compared stress across different ages, job durations, titles, and hospital levels.
Acknowledgements
Thanks for all the participants.
Declarations
Ethics approval and consent to participate
The ethics approval was obtained from the Institutional Review Board of Hainan Medical University (the ethical approval number: HYLL-2021-364).The study was conducted in accordance with the tenets of the Declaration of Helsinki. The participants were asked to sign written informed consent and informed consent to participate was obtained from all of the participants in the study.
Consent for publication
Yes.
Competing interests
The authors declare no competing interests.
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