The Effect of Mental Workload on Nurses' Caring Behaviors: The Mediating Role of Secondary Traumatic Stress - A Cross-Sectional Correlational Study

The Effect of Mental Workload on Nurses' Caring Behaviors: The Mediating Role of Secondary Traumatic Stress - A Cross-Sectional Correlational Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Effect of Mental Workload on Nurses' Caring Behaviors: The Mediating Role of Secondary Traumatic Stress - A Cross-Sectional Correlational Study Rahim Baghaei, Elham Abgarmi, Yaser Moradi, Shila Hasanzadeh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6756826/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Introduction: Nurses' caring behaviors play a critical role in communicating value, safety, and compassion to patients. Mental workload is a significant factor influencing these behaviors, with up to 93% of nurses reporting exposure to occupational stressors. Previous studies have shown that Secondary Traumatic Stress (STS), a condition resulting from indirect exposure to trauma, may serve as a mediator in the relationship between mental workload and caring behaviors. Therefore, this study aimed to examine the effect of mental workload on nurses' caring behaviors, with a particular focus on the mediating role of STS. Methodology: This cross-sectional correlational study employed Structural Equation Modeling (SEM) to analyze data collected from a total of 340 nurses working in teaching hospitals in Urmia, Northwest Iran. Data were collected using a demographic questionnaire, the NASA Task Load Index (NASA-TLX), the Secondary Traumatic Stress Scale (STSS), and the Caring Behaviors Inventory-24 (CBI-24). Statistical analysis was conducted using SPSS version 26, while SEM was conducted using SmartPLS 3 and Mplus 7.4 software packages. Results The majority of participants were female nurses (70.9%), with a mean age of 32.13 ± 6.30 years. SEM analysis showed that mental workload had a statistically significant positive effect on caring behaviors ( β = 0.167), while STS had a significant negative effect ( β = − 0.174). Furthermore, the indirect effect of mental workload on caring behaviors through STS was significant ( p 0.9, CFI > 0.9, RMSEA < 0.08, SRMR < 0.05). Conclusion Nurses are subject to high levels of both mental workload and STS, which have opposing effects on their caring behaviors. While mental workload may enhance certain aspects of care delivery, STS negatively impacts the quality of patient care. These findings further highlight the need for targeted interventions to manage STS and optimize mental workload to maintain and improve nursing care quality. Secondary traumatic stress Mental workload Caring behaviors Nurse Figures Figure 1 Figure 2 Introduction Nursing is a caregiving profession( 1 ) and is considered a demanding job with high and complex requirements( 2 ). Nurses constitute the largest segment of the healthcare workforce( 3 ) and they perform most of the tasks within the Iranian healthcare system( 4 ). Furthermore, nurses play a vital role in delivering healthcare services due to their direct interaction with patients( 5 ). In fact, nursing care is fundamental to patient recovery( 6 ). Care is the central and unifying aspect of nursing practice, and as an ethical ideal, it encourages nurses to focus on maintaining integrity, promoting healing, and upholding dignity( 7 ). The nursing profession has long been recognized for its caring behaviors( 8 ). Caring behaviors are defined as actions and behaviors carried out by nurses that convey a sense of importance, safety, and attention to patients( 9 ). Nurses’ caring behaviors play a vital role in ensuring the quality of patient care and significantly influence the overall performance of healthcare institutions. These behaviors are also recognized as a major contributor to patient satisfaction( 10 , 11 ). Given the complex nature of the healthcare environment, numerous psychological and environmental factors affect the delivery of nursing care. This highlights the critical need to support the mental health and well-being of nursing staff. Such support is not only essential for the individual caregiver but is equally crucial for sustaining a competent and resilient healthcare workforce( 12 , 13 ). Therefore, the psychological well-being of nurses deserves focused attention and the implementation of effective, evidence-based interventions( 14 ). One factor that indirectly contributes to a reduction in the quality of nursing services is the mental workload of nurses( 15 ). Mental workload refers to the cognitive effort required to perform a task( 16 ). Nursing workload can be influenced by various factors( 17 ). Mental workload has dynamic and complex relationships with performance( 18 ) and often manifests through neurophysiological symptoms( 19 ). In a study by Ardestani-Rostami et al. (2019), the results indicated an inverse relationship between mental workload and nursing performance, with 39% of performance variability being influenced by mental workload( 20 ). Therefore, the mental workload experienced by nurses can significantly impact their occupational outcomes( 21 ). On the other hand, some studies report conflicting results. Maghsoud et al. (2022) found no significant relationship between mental workload and the quality of nursing care( 22 ). High mental workload may reduce attentional capacity and ultimately contribute to workplace errors( 23 ). Accordingly, mental workload is a crucial factor in determining the quality and extent of nursing care provided( 24 ). Additionally, excessive mental workload is a critical factor that generates anxiety, fatigue, and fear among healthcare staff. However, Hassanie et al. (2022) unexpectedly indicated that workload may enhance occupational adjustment and mental health in healthcare workers( 25 ). Other contradictory findings suggest that high mental workload serves as a primary source of stress for nurses and can negatively impact their behavior, performance, and quality of professional life( 4 ). Rostami et al. (2021) also showed that higher mental workload is associated with lower job satisfaction, meaning that as work pressures on nurses increase, their satisfaction with their job decreases( 26 ). Almost 93% of nurses are exposed to occupational stressors( 27 ). Excessive psychological stress among nursing staff negatively impacts the quality of nursing services( 14 ). It appears that nurses' exposure to stress-related factors may predict their caring behaviors( 2 ). Secondary Traumatic Stress (STS) is one of the stress-related conditions to which nurses are particularly susceptible( 28 ). STS is defined as a pattern of psychological symptoms experienced by caregivers and characterized by distressing or painful feelings that occur during or after helping others( 29 ), without directly experiencing the traumatic event themselves( 14 ). These symptoms resemble those of Post-Traumatic Stress Disorder (PTSD)( 30 ) and include hyperarousal, avoidance, sleep disturbances, and intrusive thoughts during patient care( 31 ). Generally, symptoms manifest as intrusive thoughts or the re-experiencing of trauma through nightmares and flashbacks( 32 ). Before the COVID-19 pandemic, the prevalence of STS was estimated to be between 4% and 13%, but this rate increased to 40% afterward. The STS prevalence also rose to 47.5% among frontline healthcare workers to 67.1% among those witnessing patient deaths from infection( 33 ). When nurses are regularly exposed to unpredictable changes and stressful situations, they may experience anxiety and fatigue, and this may decrease the quality of care they provide( 34 ). Moreover, nurses experiencing STS often become emotionally distressed and suffer from recurrent negative thoughts and sleep disturbances( 28 ). Occupational stress resulting from diminished compassion toward patients is negatively associated with care quality and affects care delivery and patient outcomes both directly and indirectly( 2 ). Bock et al. (2020) reported that 23% of nurses experiencing STS symptoms exhibited a significant decline in workflow efficiency, which indirectly affected the quality of patient care( 35 ). Considering the indirect impact of STS on nurses' caring behaviors, identifying mediating related variables seems to be essential. Based on previous studies( 17 , 25 , 35 ), it appears that STS may amplify the indirect effects of mental workload, acting as an influential mediator. Consequently, there is a clear need to further elucidate this relationship. Today, hospitals are the largest consumers of resources in healthcare sectors. In this context, conducting studies to assess nurses' caring behaviors and implementing measures based on research findings for efficient resource management and improved healthcare quality is crucial. Given the importance of nurses' caring behaviors in ensuring safe care and considering previous research as well, it is evident that the mediating role of STS has been rarely investigated. Therefore, the present study aimed to examine the effect of mental workload on nurses' caring behaviors considering the mediating role of STS. Hypotheses H1: STS mediates the relationship between mental workload and nurses’ caring behaviors (Fig. 1). Methods Study Design & Sampling This cross-sectional correlational study was conducted following the approval of the study protocol by the Research Ethics Committee of Urmia University of Medical Sciences. Data collection was conducted at the university’s teaching (public) hospitals located in Urmia, northwest Iran. Sampling was first conducted using stratification, with quotas allocated to each hospital based on the number of employed nurses. Convenience sampling was then used to recruit a total of 340 nurses. The minimum required sample size was estimated to be 305 using the Structural Equation Modeling (SEM) formula of " number of questionnaire items × 5 ". Considering a 20% attrition rate, the final sample size was adjusted to 340( 36 ). To ensure equal representation across the three work shifts (morning, evening, and night), the sample size was divided equally, with at least 102 nurses recruited for each shift. Eligibility Criteria The inclusion criteria were as follows: (i) having at least a bachelor’s degree in nursing; (ii) having a minimum of six months of work experience; (iii) holding an active bedside position; (iv) working rotating shifts; (v) being employed in wards with morning, evening, and night rotations; (vi) no bereavement of an immediate family member within the past six months; and (vii) having no self-reported psychiatric disorders. The sole exclusion criterion was failure to complete the questionnaires correctly and in full. Data Collection After signing an informed consent form and being assured the anonymity and confidentiality of personal data, participants completed an electronic survey that included a demographic questionnaire, the Caring Behaviors Inventory-24 (CBI-24), the Secondary Traumatic Stress Scale (STSS), and the NASA Task Load Index (NASA-TLX). Nurses were instructed to complete the instruments within 20 minutes, either at work—after handing over their shifts to a colleague—or at home if the ward was busy. To clarify potential ambiguities in some NASA-TLX items, a pamphlet was provided to explain each subscale with examples. Demographic Questionnaire The demographic questionnaire included items on age, gender, marital status, and work shift. Caring Behaviors Inventory-24 (CBI-24) The CBI-24 is a 24-item instrument designed to assess nurses' caring behaviors across four subscales: Assurance (8 items), which focuses on the nurse's availability to meet patients' needs and ensure their security; Knowledge and Skill (5 items), which evaluates the nurse's competence and conscientiousness in providing care; Respect (6 items), which assesses the nurse's attention to the dignity of the patient; and Connectedness (5 items), which measures the nurse's provision of continuous assistance and readiness to support patients. Each item is rated on a 6-point Likert scale from " 1 = Never " to " 6 = Always ". The total scale score and sub-scale scores are obtained by dividing the sum of the item scores by the number of items, with higher scores indicating better caring behavior. In our study, we used the Persian version of CBI-24 translated by Khaletabad et al. (2023). The construct validity of this tool was confirmed using the exploratory factor analysis, and all items achieved a Content Validity Index (CVI) of ≥ 0.80. The inventory’s reliability was shown to be high with a Cronbach’s alpha of > 0.90( 37 ). Secondary Traumatic Stress Scale (STSS) The STSS is a 17-item tool developed by bride et al. (2004) to measure symptoms of traumatic stress in individuals who are indirectly exposed to trauma through their professional work with trauma survivors. Based on the DSM-IV PTSD criteria, the scale assesses symptoms in three dimensions of intrusion, avoidance, and arousal. Originally created from 65 items, it was refined through expert reviews and pilot testing that resulted in the final 17-item version. Participants rate each item on a 5-point Likert scale from " 1 = Never " to " 5 = Very Often " based on their experiences in the past week. Higher total scores indicate more severe STS. Developers of the scale reported a Cronbach’s alpha coefficient of 0.87, which shows good reliability( 38 ). Sabzianpoor et al. (2021) found an alpha of 0.76, which further confirms the scale’s reliability across different populations and contexts( 29 ). NASA Task Load Index (NASA-TLX) The NASA TLX is a tool used to assess subjective mental workload by evaluating six dimensions of performance during task execution. The dimensions include mental demand (thinking, deciding, or calculating required), physical demand (intensity of physical activity), temporal demand (time pressure), effort (hard work required to maintain performance), performance (task success level), and frustration level (emotional state during the task). Participants rate each dimension on a scale from 1 (low) to 20 (high) either during or after the task. This tool also includes a paired comparison procedure, where participants choose the dimension that most affects their workload from 15 pairwise combinations, helping to address variability between raters and tasks( 39 ). Safari et al. (2013) reported a Cronbach’s alpha coefficient of 0.84, which confirms the tool’s reliability( 40 ). Data Analysis Data were analyzed using IBM SPSS Statistics for Windows, version 26 (IBM Corp., Armonk, NY, USA). Descriptive statistics (mean ± standard deviation for continuous variables and frequency and percentage for categorical variables) were presented in tables. SEM was conducted using PLS-SEM procedures in SmartPLS 3 and Mplus 7.4. Composite reliability was evaluated using Cronbach's α . A p -value of < 0.05 was considered statistically significant. Results Of the total participants, 70.9% were female nurses and 29.1% were male. The overall mean age was 32.13 ± 6.30 years. Regarding age distribution, 51.8% of the participants were in the 21–30 age group, 36.8% were aged 31–40, and 11.5% were ≥ 41 years old. In terms of marital status, 40.3% of the nurses were single, while 59.7% were married (Table 1). Table 1: Frequency Distribution of Participants' Demographic Characteristics The mean ± Standard Deviation (SD) of the total STSS score was 44.10 ± 98.98. Based on the scale’s cutoff point of > 38 for clinically significant STS symptoms, 76. % of nurses were classified as having high levels of STS. For mental workload, the mean ± SD of the total NASATLX score was 75.49 ± 14.70. To estimate the prevalence of high workload, one standard deviation above the mean was used as the cutoff point (scores > 90.19). Based on this criterion, 8.1 % of nurses experienced high mental workload. The mean ± SD of the total CBI score was 109.16 ± 71.14. The internal consistency of all instruments was confirmed using Cronbach’s alpha(Table 2). Table 2: Means and Standard Deviations of Total Scores for Study Variables and Reliability Confirmation To examine the effect of mental workload on nurses' caring behaviors, with STS as a mediator, Partial Least Squares Structural Equation Modeling (PLSSEM) was conducted using the SmartPLS 3 and Mplus 7.4 software packages. The structural model comprised 13 observed indicators across three latent variables of mental workload (6 reflective items), STS (3 reflective items), and caring behaviors (4 reflective items). Evaluation of the measurement model showed that all reliability and validity criteria met the required thresholds. Cronbach’s alpha values were > 0.70, Average Variance Extracted (AVE) values exceeded 0.50, and composite reliability scores were > 0.70, all confirming internal consistency and convergent validity (Table 3). Variance Inflation Factor (VIF) values were < 5 for all variables, which indicated no multicollinearity concerns. STS was modeled as the mediating variable. The model explained 14. % of the variance in STS ( R² = 0.147), indicating that mental workload moderately predicts changes in STS (Table 3). The overall GoodnessofFit (GOF) index was 0.26, suggesting an acceptable model fit. Table 3: Discriminant Validity, Composite Reliability, Variable Reliability, and Explained Variance for Study Variables The structural model results showed that all standardized path coefficients were statistically significant at the 95% confidence level, with t values > 1.96 (Table 4). Table 4: Discriminant Validity Assessment Using the Fornell-Larcker Criterion The path coefficient represents the direct effect of one construct on another. If the path coefficient between variables is greater than 0.60, it indicates a strong predictive effect of the latent variable on the dependent variable. If the value is between 0.30 and 0.60, the effect is considered moderate, and if it is less than 0.30, the effect is considered weak. The direct effect of mental workload on caring behaviors was positive but weak ( B = 0.167); the effect of mental workload on STS was positive and moderate ( B = 0.383); and the effect of STS on caring behaviors was negative and weak ( B = − 0.174). These findings support the hypothesized relationships between the variables (Table 5, Fig. 2). Table 5: Estimates of Standardized Coefficients, Means, Standard Deviations, and Statistical Significance of Variables Discriminant validity was confirmed through acceptable factor loadings for all items, where each indicator loaded strongly on its respective latent variable (Fig. 2). Items with loadings > 0.70 were retained, reinforcing the robustness of the measurement model (Table 3). The mediating role of STS was tested with the Sobel test. The resulting z value exceeded the critical value of 1.96, indicating that the indirect effect of mental workload on caring behaviors via STS was statistically significant, thereby confirming the mediating role of STS (Table 6). Table 6: Multiple Mediation Analysis of Indirect Effects Using the Sobel Test (Full Sample) Modelfit indices provided further support for the adequacy of the structural model. In this regard, CFI, NFI, and TLI were all > 0.90; RMSEA was < 0.08; and SRMR was < 0.05. Additionally, the D_ULS, D_G, and chisquare values were within acceptable limits ( p < 0.001), collectively confirming the overall fit and strength of the conceptual model (Table 7). Table 7: Model Evaluation & Fit Indices Discussion The present study aimed to examine the effect of mental workload on nurses' caring behaviors using the mediating role of STS. Regarding the mediating role and the indirect effect of STS, which have not been explored in prior studies, this study found that STS serves as a significant mediator. Additionally, based on the SEM indices of GOF, the model demonstrated adequate fit in this regard. In other words, STS can act as a mediating variable in the causal relationship between mental workload and nurses' caring behaviors. The results of this study clearly indicate a high prevalence of STS among nurses, with 76.8% of the total study sample experiencing high levels of STS. In line with these results, Xu et al. (2024) reported a 65% combined prevalence of STS among Emergency Room (ER) nurses. ER nurses were shown to have a higher prevalence of STS during the COVID-19 pandemic( 41 ). Furthermore, Bock et al. (2020) found that more than a quarter of nurses reported symptoms of STS. They also indicated that nurses with STS reported a significant decline in their ability to manage their work, increased emotional stress, and a reduced sense of control over their tasks( 35 ). This high prevalence of STS suggests a significant and widespread psychological burden, likely stemming from the highly stressful nature of the nursing profession, particularly in clinical settings where nurses are continuously exposed to patients' critical and painful conditions( 42 ). The high prevalence of STS can lead to negative outcomes, including reduced productivity, increased job absenteeism, and psychological issues such as anxiety and depression. It can also adversely affect the quality of care provided for patients( 35 , 43 ). This study also showed that nurses experience a high mental workload, with 82.1% of the total study sample reporting high mental workload. Consistent with these findings, Pourteimour et al. (2021) indicated that a high percentage of nurses experience mental workload( 44 ). Moreover, Bakhshi et al. (2019) showed that while the level of mental workload was high among nurses, it did not have a significant correlation with their mental fatigue( 45 ). Mohammadi Ali Abadi et al. (2022) reported that the mental workload of nurses providing care for COVID-19 patients had significantly increased, which consequently led to a deterioration in their mental health( 46 ). Overall, our study revealed a high level of mental workload among the participating nurses, with a mean score of 109.71 ± 16.14. This indicates that nurses are likely to uphold their commitment to professional duties despite the demanding nature of their roles and the considerable responsibilities they carry. The results further revealed that mental workload and STS have different impacts on nurses' caring behaviors. Mental workload has a positive effect, while STS has a negative effect on caring behaviors. Mental workload, which involves pressures arising from the high volume of tasks and the need to simultaneously manage multiple activities, is positively associated with an improvement in caring behaviors. This may be due to the motivation that a high workload provides for nurses to enhance their skills and adopt more effective methods for delivering care. In these conditions, nurses might seek to improve the quality of their interactions and enhance their caring behaviors to effectively cope with additional challenges( 47 ). In contrast, in meta-analysis conducted by Jun et al. (2021), a moderate negative correlation ( r = − 0.42) was found between mental workload and caring behaviors( 48 ). Unlike mental workload, STS, which results from exposure to patients' pain and suffering, is significantly associated with a decrease in the quality of caring behaviors. This type of stress can lead to diminished psychological and emotional capabilities, reduced motivation and job satisfaction, and an increased likelihood of burnout. When nurses are affected by STS, they may not be able to respond effectively to patients' needs, thereby reducing the quality of their caring behaviors. These findings emphasize the importance of effective workload management and STS reduction in maintaining and improving the quality of nurses' caring behaviors( 47 , 49 ). While mental workload can improve caring behaviors, STS leads to a reduction in this quality. Therefore, designing and implementing strategies to reduce stress and optimize workload can enhance the quality of caring behaviors and improve overall nursing performance. Fikri et al. (2024) demonstrated a positive relationship between mental workload and nurses' stress levels. These findings showed that high mental workload can influence job-related stress in nurses. They concluded that effective stress management skills can help individuals mitigate this impact. Mental workload arises from various nursing tasks, such as managing patient care, addressing anxiety, handling complaints, and dealing with patients' defense mechanisms, all of which can contribute to increased stress( 49 ). Conclusion The results of this study clearly indicated that a significant proportion of nurses experience high levels of STS. A large number of nurses in this study reported experiencing high mental workload, which shows that they endured considerable psychological pressure. These findings emphasize the importance of managing mental workload and stress among nurses to improve healthcare quality and support the mental health of healthcare workers. According to the results of SEM, mental workload has a positive effect, while STS has a negative effect on nurses' caring behaviors. The study also demonstrated that STS can act as a mediating variable between mental workload and caring behaviors among nurses. This finding underscores the necessity of designing intervention and experimental studies (e.g., therapy groups for nurses to cope with STS) to reduce mental workload and STS among this important group of healthcare workers. Study Limitations The use of stratified sampling followed by convenience sampling may not fully represent all nurses. Therefore, the study results might be limited to the nurses working in the study setting and may not be generalizable to other hospitals or geographic regions. Another potential limitation of this study is recall bias, as the questionnaires were completed by participants based on their work performance over the previous several months. Looking forward, collaboration between universities, hospitals, and health organizations across different cities and countries may significantly advance research and development in this area. Abbreviations CB Caring Behaviors MWL Mental Workload STS Secondary Traumatic Stress CBI Caring Behaviors Inventory NASA-TLX NASA-Task Load Index STSS Secondary Traumatic Stress Scale ICU intensive care unit Declarations Acknowledgements This article is based on a master's thesis in psychiatric nursing that received approval from Urmia University of Medical Sciences. The authors extend their sincere appreciation to the Vice-Chancellor for Research and Technology at Urmia University of Medical Sciences, as well as to the participants who contributed to the successful completion of this research project. Furthermore, we wish to acknowledge the esteemed faculty members of the Nursing and Midwifery Department at Urmia University of Medical Sciences, along with all individuals who provided support in the conduct of this research. Authors’ contributions R.B contributed to the conception and design of this study and engaged in the revision of this manuscript. Y.M contributed to the study design, oversaw the entirety of the study process. E.A was responsible for data collection and analysis, and led the drafting of this manuscript. SH.H conducted the statistical analysis and participated in the revision of this manuscript. Funding This study was not financially funded. Availability of data and materials The datasets generated and/or analyzed during the current study are not publicly available due [reason why data are not public] but are available from the corresponding author on reasonable request. Ethics approval and consent to participate Prior to the initiation of the study, ethical approval was secured from the Research Ethics Committees of Urmia University of Medical Sciences (Ethics No. IR.UMSU.REC.1402.228). Furthermore, requisite permissions were obtained from the principal developer of the inventory. All methodologies employed in this study was conducted in accordance with the pertinent laws and guidelines. 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Development and validation of the secondary traumatic stress scale. Research on social work practice. 2004;14(1):27-35. Hart SG, editor NASA-task load index (NASA-TLX); 20 years later. Proceedings of the human factors and ergonomics society annual meeting; 2006: Sage publications Sage CA: Los Angeles, CA. Safari S, Mohammadi-Bolbanabad H, Kazemi M. Evaluation mental work load in nursing critical care unit with National Aeronautics and Space Administration Task Load Index (NASA-TLX). Journal of Health System Research. 2013;9(6):613-9. Xu Z, Zhao B, Zhang Z, Wang X, Jiang Y, Zhang M, et al. Prevalence and associated factors of secondary traumatic stress in emergency nurses: a systematic review and meta-analysis. European Journal of Psychotraumatology. 2024;15(1):2321761. Beck CT. Secondary traumatic stress in nurses: A systematic review. Archives of psychiatric nursing. 2011;25(1):1-10. Bride BE. Prevalence of secondary traumatic stress among social workers. Social work. 2007;52(1):63-70. Pourteimour S, Yaghmaei S, Babamohamadi H. The relationship between mental workload and job performance among Iranian nurses providing care to COVID‐19 patients: A cross‐sectional study. Journal of Nursing Management. 2021;29(6):1723-32. Bakhshi E, Mazloumi A, Hoseini SM. Relationship between mental fatigue and mental workload among nurses. Zahedan Journal of Research in Medical Sciences. 2019;21(1). Mohammadi Ali Abadi F, Shamsaei F, Tapak L. Relationship between mental workload and mental health of nurses caring for patients with Covid-19. Scientific Journal of Nursing, Midwifery and Paramedical Faculty. 2022;8(2):15-30. Sardo PMG, Macedo RPA, Alvarelhão JJM, Simões JFL, Guedes JAD, Simões CJ, et al. Nursing workload assessment in an intensive care unit: A retrospective observational study using the Nursing Activities Score. Nursing in critical care. 2023;28(2):288-97. Jun J, Ojemeni MM, Kalamani R, Tong J, Crecelius ML. Relationship between nurse burnout, patient and organizational outcomes: Systematic review. International journal of nursing studies. 2021;119:103933. Fikri Z, Bellarifanda A, Sunardi S, Rosyidul‘Ibad M, Mu’jizah K. The relationship between mental workload and nurse stress levels in hospitals. Healthcare in Low-resource Settings. 2024;12(1). Tables Table 1: Frequency Distribution of Participants' Demographic Characteristics Variable Numerical Value Percentage Age (Years) ≤30 177 51.9% 31 - 40 125 36.7% 41 ≤ 38 11.4% Gender Female 241 70.9% Male 99 29.1% Marital Status Single 137 40.3% Married 203 59.7% Work Shift Morning 112 32.9% Evening 112 32.9% Night (12 hours) 116 34.1% Table 2: Means and Standard Deviations of Total Scores for Study Variables and Reliability Confirmation Variable Range of Total Score Mean ± SD of the Total Score α NASA-TLX 0-100 75.49 ± 14.70 0.83 STSS 17-85 44.10 ± 98.98 0.883 CBI 23-138 109.16 ± 71.14 0.944 Table 3: Discriminant Validity, Composite Reliability, Variable Reliability, and Explained Variance for Study Variables Variable AVE Composite Reliability R Square Cronbach’s Alpha rho_A Communality Redundancy STS 0.783 0.915 0.147 0.861 0.872 0.515 0.106 Mental Workload 0.502 0.796 0.721 0.706 0.176 Caring Behavior 0.770 0.931 0.036 0.900 0.900 0.569 0.023 AVE = Average Variance Extracted; Composite Reliability = Internal consistency; rho_A = Cronbach's alpha; Communality = Shared variance; Redundancy = Redundancy index; R Square = Explained variance Table 4: Discriminant Validity Assessment Using the Fornell-Larcker Criterion Variable STS Mental Workload Caring Behavior STS 0.885 Mental Workload 0.383 0.634 Caring Behavior - 0.110 0.101 0.878 Table 5: Estimates of Standardized Coefficients, Means, Standard Deviations, and Statistical Significance of Variables Original Sample Mean Standard Deviation p -values STS Caring Behavior - 0.174 - 0.171 0.063 0.006 Mental Workload STS 0.383 0.376 0.101 < 0.001 Mental Workload Caring Behavior 0.167 0.265 0.109 0.038 Table 6: Multiple Mediation Analysis of Indirect Effects Using the Sobel Test (Full Sample) Path z -value Sig. Mental workload to Caring Behaviors through STS 2.183 < 0.05 Table 7: Model Evaluation & Fit Indices Goodness-of-Fit Indices SRMR 0.042 D_ULS 0.979 D_G 0.224 Chi-Square 454.888 NFI 0.91 TLI 0.94 CFI 0.96 RMSEA 0.064 SRMR = Standardized Root Mean Square Residual; D_ULS = Euclidean Distance Squared; D_G = Geodesic Distance; Chi-Square = Chi-square test; NFI = Bentler-Bonett Normed Fit Index; TLI = Tucker-Lewis Index; CFI = Comparative Fit Index; RMSEA = Root Mean Square Error of Approximation Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6756826","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":468219912,"identity":"2f50e416-bd45-4cf3-a9f7-f61e505c7e5d","order_by":0,"name":"Rahim Baghaei","email":"","orcid":"","institution":"Urmia University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Rahim","middleName":"","lastName":"Baghaei","suffix":""},{"id":468219913,"identity":"64b8fed6-fc52-4100-b8c2-b4e614993075","order_by":1,"name":"Elham Abgarmi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYFACHgjJxt58/McHIIuNnVgtfDzHEiRngLQwE6mFQU4ix0AazCakhZ/97OEXP/dsk2EDajG2+bVNno+ZgfHDxxzcWiR78tIse57d5mHjeVaQnNt327CNmYFZcuY23FoMDuSYGfAcAGphT95wOLfnNiNQCxszLx4t9uffmBn+AWlhSDBstuy5bU9Qi4FEjvFjsC0cKcbMDD9uJxLUInHjXRqzDEgLz7E0xt6G28ltzIzNeP3C3597+OObA7ft5dubjzH8+HPbdn5788EPH/FoAQI2CTiTsQ1MNuBVDwTMHxDsP4QUj4JRMApGwUgEAF+kUN1sUI5mAAAAAElFTkSuQmCC","orcid":"","institution":"Urmia University of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Elham","middleName":"","lastName":"Abgarmi","suffix":""},{"id":468219914,"identity":"b219c21d-5511-4bd6-83f9-edf6d0f692ae","order_by":2,"name":"Yaser Moradi","email":"","orcid":"","institution":"Urmia University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yaser","middleName":"","lastName":"Moradi","suffix":""},{"id":468219915,"identity":"812e3889-f303-4209-b524-fc75284a9d00","order_by":3,"name":"Shila Hasanzadeh","email":"","orcid":"","institution":"Urmia University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Shila","middleName":"","lastName":"Hasanzadeh","suffix":""}],"badges":[],"createdAt":"2025-05-27 07:53:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6756826/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6756826/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84306104,"identity":"e3c871f0-fc75-4a65-bcaa-e442c6d07395","added_by":"auto","created_at":"2025-06-10 11:24:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":188172,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHypothesis model\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6756826/v1/42ee0d1e61fda7b4755591f3.png"},{"id":84305868,"identity":"7b166304-ee3c-442e-ba0c-2f9befd5917b","added_by":"auto","created_at":"2025-06-10 11:24:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":680733,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePath Coefficients Between Variables in the Conceptual Model\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6756826/v1/fb1c80ef697034cd453a0d4e.png"},{"id":84306910,"identity":"696cc800-9649-4b09-855a-3097425fe8da","added_by":"auto","created_at":"2025-06-10 11:32:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2592732,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6756826/v1/604c46ad-d385-407b-9f74-995f20dc2767.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eThe Effect of Mental Workload on Nurses' Caring Behaviors: The Mediating Role of Secondary Traumatic Stress - A Cross-Sectional Correlational Study\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNursing is a caregiving profession(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) and is considered a demanding job with high and complex requirements(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Nurses constitute the largest segment of the healthcare workforce(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) and they perform most of the tasks within the Iranian healthcare system(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Furthermore, nurses play a vital role in delivering healthcare services due to their direct interaction with patients(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). In fact, nursing care is fundamental to patient recovery(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Care is the central and unifying aspect of nursing practice, and as an ethical ideal, it encourages nurses to focus on maintaining integrity, promoting healing, and upholding dignity(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). The nursing profession has long been recognized for its caring behaviors(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Caring behaviors are defined as actions and behaviors carried out by nurses that convey a sense of importance, safety, and attention to patients(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Nurses’ caring behaviors play a vital role in ensuring the quality of patient care and significantly influence the overall performance of healthcare institutions. These behaviors are also recognized as a major contributor to patient satisfaction(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Given the complex nature of the healthcare environment, numerous psychological and environmental factors affect the delivery of nursing care. This highlights the critical need to support the mental health and well-being of nursing staff. Such support is not only essential for the individual caregiver but is equally crucial for sustaining a competent and resilient healthcare workforce(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Therefore, the psychological well-being of nurses deserves focused attention and the implementation of effective, evidence-based interventions(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOne factor that indirectly contributes to a reduction in the quality of nursing services is the mental workload of nurses(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Mental workload refers to the cognitive effort required to perform a task(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Nursing workload can be influenced by various factors(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Mental workload has dynamic and complex relationships with performance(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) and often manifests through neurophysiological symptoms(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). In a study by Ardestani-Rostami et al. (2019), the results indicated an inverse relationship between mental workload and nursing performance, with 39% of performance variability being influenced by mental workload(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Therefore, the mental workload experienced by nurses can significantly impact their occupational outcomes(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). On the other hand, some studies report conflicting results. Maghsoud et al. (2022) found no significant relationship between mental workload and the quality of nursing care(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). High mental workload may reduce attentional capacity and ultimately contribute to workplace errors(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Accordingly, mental workload is a crucial factor in determining the quality and extent of nursing care provided(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Additionally, excessive mental workload is a critical factor that generates anxiety, fatigue, and fear among healthcare staff. However, Hassanie et al. (2022) unexpectedly indicated that workload may enhance occupational adjustment and mental health in healthcare workers(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Other contradictory findings suggest that high mental workload serves as a primary source of stress for nurses and can negatively impact their behavior, performance, and quality of professional life(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Rostami et al. (2021) also showed that higher mental workload is associated with lower job satisfaction, meaning that as work pressures on nurses increase, their satisfaction with their job decreases(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlmost 93% of nurses are exposed to occupational stressors(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Excessive psychological stress among nursing staff negatively impacts the quality of nursing services(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). It appears that nurses' exposure to stress-related factors may predict their caring behaviors(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Secondary Traumatic Stress (STS) is one of the stress-related conditions to which nurses are particularly susceptible(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). STS is defined as a pattern of psychological symptoms experienced by caregivers and characterized by distressing or painful feelings that occur during or after helping others(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e), without directly experiencing the traumatic event themselves(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). These symptoms resemble those of Post-Traumatic Stress Disorder (PTSD)(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) and include hyperarousal, avoidance, sleep disturbances, and intrusive thoughts during patient care(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Generally, symptoms manifest as intrusive thoughts or the re-experiencing of trauma through nightmares and flashbacks(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Before the COVID-19 pandemic, the prevalence of STS was estimated to be between 4% and 13%, but this rate increased to 40% afterward. The STS prevalence also rose to 47.5% among frontline healthcare workers to 67.1% among those witnessing patient deaths from infection(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). When nurses are regularly exposed to unpredictable changes and stressful situations, they may experience anxiety and fatigue, and this may decrease the quality of care they provide(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Moreover, nurses experiencing STS often become emotionally distressed and suffer from recurrent negative thoughts and sleep disturbances(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOccupational stress resulting from diminished compassion toward patients is negatively associated with care quality and affects care delivery and patient outcomes both directly and indirectly(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Bock et al. (2020) reported that 23% of nurses experiencing STS symptoms exhibited a significant decline in workflow efficiency, which indirectly affected the quality of patient care(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Considering the indirect impact of STS on nurses' caring behaviors, identifying mediating related variables seems to be essential. Based on previous studies(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), it appears that STS may amplify the indirect effects of mental workload, acting as an influential mediator. Consequently, there is a clear need to further elucidate this relationship.\u003c/p\u003e \u003cp\u003eToday, hospitals are the largest consumers of resources in healthcare sectors. In this context, conducting studies to assess nurses' caring behaviors and implementing measures based on research findings for efficient resource management and improved healthcare quality is crucial. Given the importance of nurses' caring behaviors in ensuring safe care and considering previous research as well, it is evident that the mediating role of STS has been rarely investigated. Therefore, the present study aimed to examine the effect of mental workload on nurses' caring behaviors considering the mediating role of STS.\u003c/p\u003e\n\u003ch3\u003eHypotheses\u003c/h3\u003e\n\u003cp\u003eH1: STS mediates the relationship between mental workload and nurses’ caring behaviors (Fig.\u0026nbsp;1).\u003c/p\u003e \u003c/div\u003e"},{"header":"Methods","content":"\u003ch2\u003eStudy Design \u0026amp; Sampling\u003c/h2\u003e\u003cp\u003e This cross-sectional correlational study was conducted following the approval of the study protocol by the Research Ethics Committee of Urmia University of Medical Sciences. Data collection was conducted at the university’s teaching (public) hospitals located in Urmia, northwest Iran.\u003c/p\u003e\u003cp\u003eSampling was first conducted using stratification, with quotas allocated to each hospital based on the number of employed nurses. Convenience sampling was then used to recruit a total of 340 nurses. The minimum required sample size was estimated to be 305 using the Structural Equation Modeling (SEM) formula of \"\u003cem\u003enumber of questionnaire items × 5\u003c/em\u003e\". Considering a 20% attrition rate, the final sample size was adjusted to 340(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). To ensure equal representation across the three work shifts (morning, evening, and night), the sample size was divided equally, with at least 102 nurses recruited for each shift.\u003c/p\u003e\n\u003ch3\u003eEligibility Criteria\u003c/h3\u003e\n\u003cp\u003eThe inclusion criteria were as follows: (i) having at least a bachelor\u0026rsquo;s degree in nursing; (ii) having a minimum of six months of work experience; (iii) holding an active bedside position; (iv) working rotating shifts; (v) being employed in wards with morning, evening, and night rotations; (vi) no bereavement of an immediate family member within the past six months; and (vii) having no self-reported psychiatric disorders. The sole exclusion criterion was failure to complete the questionnaires correctly and in full.\u003c/p\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eAfter signing an informed consent form and being assured the anonymity and confidentiality of personal data, participants completed an electronic survey that included a demographic questionnaire, the Caring Behaviors Inventory-24 (CBI-24), the Secondary Traumatic Stress Scale (STSS), and the NASA Task Load Index (NASA-TLX). Nurses were instructed to complete the instruments within 20 minutes, either at work\u0026mdash;after handing over their shifts to a colleague\u0026mdash;or at home if the ward was busy. To clarify potential ambiguities in some NASA-TLX items, a pamphlet was provided to explain each subscale with examples.\u003c/p\u003e\n\u003ch3\u003eDemographic Questionnaire\u003c/h3\u003e\n\u003cp\u003eThe demographic questionnaire included items on age, gender, marital status, and work shift.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCaring Behaviors Inventory-24 (CBI-24)\u003c/h2\u003e \u003cp\u003eThe CBI-24 is a 24-item instrument designed to assess nurses' caring behaviors across four subscales: Assurance (8 items), which focuses on the nurse's availability to meet patients' needs and ensure their security; Knowledge and Skill (5 items), which evaluates the nurse's competence and conscientiousness in providing care; Respect (6 items), which assesses the nurse's attention to the dignity of the patient; and Connectedness (5 items), which measures the nurse's provision of continuous assistance and readiness to support patients. Each item is rated on a 6-point Likert scale from \"\u003cem\u003e1\u0026thinsp;=\u0026thinsp;Never\u003c/em\u003e\" to \"\u003cem\u003e6\u0026thinsp;=\u0026thinsp;Always\u003c/em\u003e\". The total scale score and sub-scale scores are obtained by dividing the sum of the item scores by the number of items, with higher scores indicating better caring behavior. In our study, we used the Persian version of CBI-24 translated by Khaletabad et al. (2023). The construct validity of this tool was confirmed using the exploratory factor analysis, and all items achieved a Content Validity Index (CVI) of \u0026ge;\u0026thinsp;0.80. The inventory\u0026rsquo;s reliability was shown to be high with a Cronbach\u0026rsquo;s alpha of \u0026gt;\u0026thinsp;0.90(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSecondary Traumatic Stress Scale (STSS)\u003c/h3\u003e\n\u003cp\u003eThe STSS is a 17-item tool developed by bride et al. (2004) to measure symptoms of traumatic stress in individuals who are indirectly exposed to trauma through their professional work with trauma survivors. Based on the DSM-IV PTSD criteria, the scale assesses symptoms in three dimensions of intrusion, avoidance, and arousal. Originally created from 65 items, it was refined through expert reviews and pilot testing that resulted in the final 17-item version. Participants rate each item on a 5-point Likert scale from \"\u003cem\u003e1\u0026thinsp;=\u0026thinsp;Never\u003c/em\u003e\" to \"\u003cem\u003e5\u0026thinsp;=\u0026thinsp;Very Often\u003c/em\u003e\" based on their experiences in the past week. Higher total scores indicate more severe STS. Developers of the scale reported a Cronbach\u0026rsquo;s alpha coefficient of 0.87, which shows good reliability(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Sabzianpoor et al. (2021) found an alpha of 0.76, which further confirms the scale\u0026rsquo;s reliability across different populations and contexts(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eNASA Task Load Index (NASA-TLX)\u003c/h3\u003e\n\u003cp\u003eThe NASA TLX is a tool used to assess subjective mental workload by evaluating six dimensions of performance during task execution. The dimensions include mental demand (thinking, deciding, or calculating required), physical demand (intensity of physical activity), temporal demand (time pressure), effort (hard work required to maintain performance), performance (task success level), and frustration level (emotional state during the task). Participants rate each dimension on a scale from 1 (low) to 20 (high) either during or after the task. This tool also includes a paired comparison procedure, where participants choose the dimension that most affects their workload from 15 pairwise combinations, helping to address variability between raters and tasks(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Safari et al. (2013) reported a Cronbach\u0026rsquo;s alpha coefficient of 0.84, which confirms the tool\u0026rsquo;s reliability(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eData were analyzed using IBM SPSS Statistics for Windows, version 26 (IBM Corp., Armonk, NY, USA). Descriptive statistics (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation for continuous variables and frequency and percentage for categorical variables) were presented in tables. SEM was conducted using PLS-SEM procedures in SmartPLS 3 and Mplus 7.4. Composite reliability was evaluated using Cronbach's \u003cem\u003eα\u003c/em\u003e. A \u003cem\u003ep\u003c/em\u003e-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eOf the total participants, 70.9% were female nurses and 29.1% were male. The overall mean age was 32.13\u0026thinsp;\u0026plusmn;\u0026thinsp;6.30 years. Regarding age distribution, 51.8% of the participants were in the 21\u0026ndash;30 age group, 36.8% were aged 31\u0026ndash;40, and 11.5% were \u0026ge;\u0026thinsp;41 years old. In terms of marital status, 40.3% of the nurses were single, while 59.7% were married (Table\u0026nbsp;1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;1: Frequency Distribution of Participants\u0026apos; Demographic Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mean\u0026thinsp;\u0026plusmn;\u0026thinsp;Standard Deviation (SD) of the total STSS score was 44.10\u0026thinsp;\u0026plusmn;\u0026thinsp;98.98. Based on the scale\u0026rsquo;s cutoff point of \u0026gt;\u0026thinsp;38 for clinically significant STS symptoms, 76. % of nurses were classified as having high levels of STS. For mental workload, the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD of the total NASATLX score was 75.49\u0026thinsp;\u0026plusmn;\u0026thinsp;14.70. To estimate the prevalence of high workload, one standard deviation above the mean was used as the cutoff point (scores\u0026thinsp;\u0026gt;\u0026thinsp;90.19). Based on this criterion, 8.1 % of nurses experienced high mental workload. The mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD of the total CBI score was 109.16\u0026thinsp;\u0026plusmn;\u0026thinsp;71.14. The internal consistency of all instruments was confirmed using Cronbach\u0026rsquo;s alpha(Table\u0026nbsp;2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;2: Means and Standard Deviations of Total Scores for Study Variables and Reliability Confirmation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo examine the effect of mental workload on nurses\u0026apos; caring behaviors, with STS as a mediator, Partial Least Squares Structural Equation Modeling (PLSSEM) was conducted using the SmartPLS 3 and Mplus 7.4 software packages. The structural model comprised 13 observed indicators across three latent variables of mental workload (6 reflective items), STS (3 reflective items), and caring behaviors (4 reflective items). Evaluation of the measurement model showed that all reliability and validity criteria met the required thresholds. Cronbach\u0026rsquo;s alpha values were \u0026gt;\u0026thinsp;0.70, Average Variance Extracted (AVE) values exceeded 0.50, and composite reliability scores were \u0026gt;\u0026thinsp;0.70, all confirming internal consistency and convergent validity (Table\u0026nbsp;3). Variance Inflation Factor (VIF) values were \u0026lt;\u0026thinsp;5 for all variables, which indicated no multicollinearity concerns. STS was modeled as the mediating variable. The model explained 14. % of the variance in STS (\u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.147), indicating that mental workload moderately predicts changes in STS (Table 3). The overall GoodnessofFit (GOF) index was 0.26, suggesting an acceptable model fit.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;3: Discriminant Validity, Composite Reliability, Variable Reliability, and Explained Variance for Study Variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe structural model results showed that all standardized path coefficients were statistically significant at the 95% confidence level, with \u003cem\u003et\u003c/em\u003evalues\u0026thinsp;\u0026gt;\u0026thinsp;1.96 (Table\u0026nbsp;4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;4: Discriminant Validity Assessment Using the Fornell-Larcker Criterion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe path coefficient represents the direct effect of one construct on another. If the path coefficient between variables is greater than 0.60, it indicates a strong predictive effect of the latent variable on the dependent variable. If the value is between 0.30 and 0.60, the effect is considered moderate, and if it is less than 0.30, the effect is considered weak. The direct effect of mental workload on caring behaviors was positive but weak (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.167); the effect of mental workload on STS was positive and moderate (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.383); and the effect of STS on caring behaviors was negative and weak (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.174). These findings support the hypothesized relationships between the variables (Table\u0026nbsp;5, Fig.\u0026nbsp;2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;5: Estimates of Standardized Coefficients, Means, Standard Deviations, and Statistical Significance of Variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDiscriminant validity was confirmed through acceptable factor loadings for all items, where each indicator loaded strongly on its respective latent variable (Fig. 2). Items with loadings\u0026thinsp;\u0026gt;\u0026thinsp;0.70 were retained, reinforcing the robustness of the measurement model (Table 3).\u003c/p\u003e\n\u003cp\u003eThe mediating role of STS was tested with the Sobel test. The resulting \u003cem\u003ez\u003c/em\u003evalue exceeded the critical value of 1.96, indicating that the indirect effect of mental workload on caring behaviors via STS was statistically significant, thereby confirming the mediating role of STS (Table\u0026nbsp;6).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;6: Multiple Mediation Analysis of Indirect Effects Using the Sobel Test (Full Sample)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eModelfit indices provided further support for the adequacy of the structural model. In this regard, CFI, NFI, and TLI were all \u0026gt;\u0026thinsp;0.90; RMSEA was \u0026lt;\u0026thinsp;0.08; and SRMR was \u0026lt;\u0026thinsp;0.05. Additionally, the D_ULS, D_G, and chisquare values were within acceptable limits (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), collectively confirming the overall fit and strength of the conceptual model (Table\u0026nbsp;7).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;7: Model Evaluation \u0026amp; Fit Indices\u003c/strong\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study aimed to examine the effect of mental workload on nurses' caring behaviors using the mediating role of STS. Regarding the mediating role and the indirect effect of STS, which have not been explored in prior studies, this study found that STS serves as a significant mediator. Additionally, based on the SEM indices of GOF, the model demonstrated adequate fit in this regard. In other words, STS can act as a mediating variable in the causal relationship between mental workload and nurses' caring behaviors.\u003c/p\u003e \u003cp\u003eThe results of this study clearly indicate a high prevalence of STS among nurses, with 76.8% of the total study sample experiencing high levels of STS. In line with these results, Xu et al. (2024) reported a 65% combined prevalence of STS among Emergency Room (ER) nurses. ER nurses were shown to have a higher prevalence of STS during the COVID-19 pandemic(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Furthermore, Bock et al. (2020) found that more than a quarter of nurses reported symptoms of STS. They also indicated that nurses with STS reported a significant decline in their ability to manage their work, increased emotional stress, and a reduced sense of control over their tasks(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). This high prevalence of STS suggests a significant and widespread psychological burden, likely stemming from the highly stressful nature of the nursing profession, particularly in clinical settings where nurses are continuously exposed to patients' critical and painful conditions(\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). The high prevalence of STS can lead to negative outcomes, including reduced productivity, increased job absenteeism, and psychological issues such as anxiety and depression. It can also adversely affect the quality of care provided for patients(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study also showed that nurses experience a high mental workload, with 82.1% of the total study sample reporting high mental workload. Consistent with these findings, Pourteimour et al. (2021) indicated that a high percentage of nurses experience mental workload(\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). Moreover, Bakhshi et al. (2019) showed that while the level of mental workload was high among nurses, it did not have a significant correlation with their mental fatigue(\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Mohammadi Ali Abadi et al. (2022) reported that the mental workload of nurses providing care for COVID-19 patients had significantly increased, which consequently led to a deterioration in their mental health(\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Overall, our study revealed a high level of mental workload among the participating nurses, with a mean score of 109.71\u0026thinsp;\u0026plusmn;\u0026thinsp;16.14. This indicates that nurses are likely to uphold their commitment to professional duties despite the demanding nature of their roles and the considerable responsibilities they carry.\u003c/p\u003e \u003cp\u003eThe results further revealed that mental workload and STS have different impacts on nurses' caring behaviors. Mental workload has a positive effect, while STS has a negative effect on caring behaviors. Mental workload, which involves pressures arising from the high volume of tasks and the need to simultaneously manage multiple activities, is positively associated with an improvement in caring behaviors. This may be due to the motivation that a high workload provides for nurses to enhance their skills and adopt more effective methods for delivering care. In these conditions, nurses might seek to improve the quality of their interactions and enhance their caring behaviors to effectively cope with additional challenges(\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). In contrast, in meta-analysis conducted by Jun et al. (2021), a moderate negative correlation (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.42) was found between mental workload and caring behaviors(\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). Unlike mental workload, STS, which results from exposure to patients' pain and suffering, is significantly associated with a decrease in the quality of caring behaviors. This type of stress can lead to diminished psychological and emotional capabilities, reduced motivation and job satisfaction, and an increased likelihood of burnout. When nurses are affected by STS, they may not be able to respond effectively to patients' needs, thereby reducing the quality of their caring behaviors. These findings emphasize the importance of effective workload management and STS reduction in maintaining and improving the quality of nurses' caring behaviors(\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). While mental workload can improve caring behaviors, STS leads to a reduction in this quality. Therefore, designing and implementing strategies to reduce stress and optimize workload can enhance the quality of caring behaviors and improve overall nursing performance. Fikri et al. (2024) demonstrated a positive relationship between mental workload and nurses' stress levels. These findings showed that high mental workload can influence job-related stress in nurses. They concluded that effective stress management skills can help individuals mitigate this impact. Mental workload arises from various nursing tasks, such as managing patient care, addressing anxiety, handling complaints, and dealing with patients' defense mechanisms, all of which can contribute to increased stress(\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe results of this study clearly indicated that a significant proportion of nurses experience high levels of STS. A large number of nurses in this study reported experiencing high mental workload, which shows that they endured considerable psychological pressure. These findings emphasize the importance of managing mental workload and stress among nurses to improve healthcare quality and support the mental health of healthcare workers. According to the results of SEM, mental workload has a positive effect, while STS has a negative effect on nurses' caring behaviors. The study also demonstrated that STS can act as a mediating variable between mental workload and caring behaviors among nurses. This finding underscores the necessity of designing intervention and experimental studies (e.g., therapy groups for nurses to cope with STS) to reduce mental workload and STS among this important group of healthcare workers.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStudy Limitations\u003c/h2\u003e \u003cp\u003eThe use of stratified sampling followed by convenience sampling may not fully represent all nurses. Therefore, the study results might be limited to the nurses working in the study setting and may not be generalizable to other hospitals or geographic regions. Another potential limitation of this study is recall bias, as the questionnaires were completed by participants based on their work performance over the previous several months. Looking forward, collaboration between universities, hospitals, and health organizations across different cities and countries may significantly advance research and development in this area.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCB Caring Behaviors\u003c/p\u003e\n\u003cp\u003eMWL Mental Workload\u003c/p\u003e\n\u003cp\u003eSTS Secondary Traumatic Stress\u003c/p\u003e\n\u003cp\u003eCBI Caring Behaviors Inventory\u003c/p\u003e\n\u003cp\u003eNASA-TLX NASA-Task Load Index\u003c/p\u003e\n\u003cp\u003eSTSS Secondary Traumatic Stress Scale\u003c/p\u003e\n\u003cp\u003eICU intensive care unit\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article is based on a master\u0026apos;s thesis in psychiatric nursing that received approval from Urmia University of Medical Sciences. The authors extend their sincere appreciation to the Vice-Chancellor for Research and Technology at Urmia University of Medical Sciences, as well as to the participants who contributed to the successful completion of this research project. Furthermore, we wish to acknowledge the esteemed faculty members of the Nursing and Midwifery Department at Urmia University of Medical Sciences, along with all individuals who provided support in the conduct of this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eR.B contributed to the conception and design of this study and engaged in the revision of this manuscript. Y.M contributed to the study design, oversaw the entirety of the study process. E.A was responsible for data collection and analysis, and led the drafting of this manuscript. SH.H conducted the statistical analysis and participated in the revision of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was not financially funded.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available due [reason why data are not public] but are 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\u003ePrior to the initiation of the study, ethical approval was secured from the Research Ethics Committees of Urmia University of Medical Sciences (Ethics No. IR.UMSU.REC.1402.228). Furthermore, requisite permissions were obtained from the principal developer of the inventory. All methodologies employed in this study was conducted in accordance with the\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003epertinent laws and guidelines. Prior to the\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003ecommencement of the study, all participants\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003ecompleted an informed consent form.\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003eParticipants were apprised of the anonymity\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003eand confidentiality pertaining to their personal\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003edata.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBabaei S, Haratian M. Compassion satisfaction and fatigue in cardiovascular nurses: A cross-sectional descriptive study. 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Sustainability. 2022;14(19):12010.\u003c/li\u003e\n\u003cli\u003eRostami F, Babaei-Pouya A, Teimori-Boghsani G, Jahangirimehr A, Mehri Z, Feiz-Arefi M. Mental workload and job satisfaction in healthcare workers: the moderating role of job control. Frontiers in public health. 2021;9:683388.\u003c/li\u003e\n\u003cli\u003eJalal E, Mohammadi F, Fatemi N, Haghani H. Secondary traumatic stress and resilience of the nurses at the Psychiatric Centers in Tehran City, Iran. Iran J Nurs. 2019;32:36-49.\u003c/li\u003e\n\u003cli\u003eBahari G, Asiri K, Nouh N, Alqahtani N. Professional quality of life among nurses: compassion satisfaction, burnout, and secondary traumatic stress: a multisite study. SAGE Open Nursing. 2022;8:23779608221112329.\u003c/li\u003e\n\u003cli\u003eSabzianpoor B, AMINI N, Deyreh E, AZADI S. Effectiveness of Lazarus-Based Multidimensional Therapy on Reducing Secondary Traumatic Stress and Generalized Anxiety in Nurses. 2021.\u003c/li\u003e\n\u003cli\u003eEpstein EG, Haizlip J, Liaschenko J, Zhao D, Bennett R, Marshall MF. Moral distress, mattering, and secondary traumatic stress in provider burnout: A call for moral community. AACN Advanced Critical Care. 2020;31(2):146-57.\u003c/li\u003e\n\u003cli\u003eLee MS, Shin S, Hong E. Factors affecting secondary traumatic stress of nurses caring for COVID-19 patients in South Korea. International Journal of Environmental Research and Public Health. 2021;18(13):6843.\u003c/li\u003e\n\u003cli\u003eJobe JA, Gillespie GL, Schwytzer D. A national survey of secondary traumatic stress and work productivity of emergency nurses following trauma patient care. Journal of Trauma Nursing| JTN. 2021;28(4):243-9.\u003c/li\u003e\n\u003cli\u003eOrr\u0026ugrave; G, Marzetti F, Conversano C, Vagheggini G, Miccoli M, Ciacchini R, et al. Secondary traumatic stress and burnout in healthcare workers during COVID-19 outbreak. International journal of environmental research and public health. 2021;18(1):337.\u003c/li\u003e\n\u003cli\u003eLopez J, Bindler RJ, Lee J. Cross-sectional analysis of burnout, secondary traumatic stress, and compassion satisfaction among emergency nurses in Southern California working through the COVID-19 pandemic. Journal of emergency nursing. 2022;48(4):366-75. e2.\u003c/li\u003e\n\u003cli\u003eBock C, Heitland I, Zimmermann T, Winter L, Kahl KG. Secondary traumatic stress, mental state, and work ability in nurses\u0026mdash;Results of a psychological risk assessment at a university hospital. Frontiers in psychiatry. 2020;11:298.\u003c/li\u003e\n\u003cli\u003eWeston R, Gore Jr PA. A brief guide to structural equation modeling. The counseling psychologist. 2006;34(5):719-51.\u003c/li\u003e\n\u003cli\u003eKhaletabad NA, Radfar M, Khademi M, Khalkhali H. Caring Behaviors Inventory-24: translation, cross-cultural adaptation, and psychometric testing for using in nurses and patients. BMC nursing. 2023;22(1):82.\u003c/li\u003e\n\u003cli\u003eBride BE, Robinson MM, Yegidis B, Figley CR. Development and validation of the secondary traumatic stress scale. Research on social work practice. 2004;14(1):27-35.\u003c/li\u003e\n\u003cli\u003eHart SG, editor NASA-task load index (NASA-TLX); 20 years later. Proceedings of the human factors and ergonomics society annual meeting; 2006: Sage publications Sage CA: Los Angeles, CA.\u003c/li\u003e\n\u003cli\u003eSafari S, Mohammadi-Bolbanabad H, Kazemi M. Evaluation mental work load in nursing critical care unit with National Aeronautics and Space Administration Task Load Index (NASA-TLX). Journal of Health System Research. 2013;9(6):613-9.\u003c/li\u003e\n\u003cli\u003eXu Z, Zhao B, Zhang Z, Wang X, Jiang Y, Zhang M, et al. Prevalence and associated factors of secondary traumatic stress in emergency nurses: a systematic review and meta-analysis. European Journal of Psychotraumatology. 2024;15(1):2321761.\u003c/li\u003e\n\u003cli\u003eBeck CT. Secondary traumatic stress in nurses: A systematic review. Archives of psychiatric nursing. 2011;25(1):1-10.\u003c/li\u003e\n\u003cli\u003eBride BE. Prevalence of secondary traumatic stress among social workers. Social work. 2007;52(1):63-70.\u003c/li\u003e\n\u003cli\u003ePourteimour S, Yaghmaei S, Babamohamadi H. The relationship between mental workload and job performance among Iranian nurses providing care to COVID‐19 patients: A cross‐sectional study. Journal of Nursing Management. 2021;29(6):1723-32.\u003c/li\u003e\n\u003cli\u003eBakhshi E, Mazloumi A, Hoseini SM. Relationship between mental fatigue and mental workload among nurses. Zahedan Journal of Research in Medical Sciences. 2019;21(1).\u003c/li\u003e\n\u003cli\u003eMohammadi Ali Abadi F, Shamsaei F, Tapak L. Relationship between mental workload and mental health of nurses caring for patients with Covid-19. Scientific Journal of Nursing, Midwifery and Paramedical Faculty. 2022;8(2):15-30.\u003c/li\u003e\n\u003cli\u003eSardo PMG, Macedo RPA, Alvarelh\u0026atilde;o JJM, Sim\u0026otilde;es JFL, Guedes JAD, Sim\u0026otilde;es CJ, et al. Nursing workload assessment in an intensive care unit: A retrospective observational study using the Nursing Activities Score. Nursing in critical care. 2023;28(2):288-97.\u003c/li\u003e\n\u003cli\u003eJun J, Ojemeni MM, Kalamani R, Tong J, Crecelius ML. Relationship between nurse burnout, patient and organizational outcomes: Systematic review. International journal of nursing studies. 2021;119:103933.\u003c/li\u003e\n\u003cli\u003eFikri Z, Bellarifanda A, Sunardi S, Rosyidul\u0026lsquo;Ibad M, Mu\u0026rsquo;jizah K. The relationship between mental workload and nurse stress levels in hospitals. Healthcare in Low-resource Settings. 2024;12(1).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1: Frequency Distribution of Participants\u0026apos; Demographic Characteristics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 288px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumerical Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (Years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026le;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e51.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e31 - 40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e36.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e41 \u0026le;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e11.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e70.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e29.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e40.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e59.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWork Shift\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003eMorning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e32.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003eEvening\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e32.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003eNight\u003c/p\u003e\n \u003cp\u003e(12 hours)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e34.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Means and Standard Deviations of Total Scores for Study Variables and Reliability Confirmation\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"564\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRange of Total Score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026plusmn;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eSD of the Total Score\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026alpha;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNASA-TLX\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e0-100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e75.49 \u0026plusmn; 14.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSTSS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e17-85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e44.10 \u0026plusmn; 98.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.883\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCBI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e23-138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e109.16 \u0026plusmn; 71.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.944\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003cstrong\u003eTable 3: Discriminant Validity, Composite Reliability, Variable Reliability, and Explained Variance for Study Variables\u003c/strong\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"672\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAVE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComposite Reliability\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR Square\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCronbach\u0026rsquo;s Alpha\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003erho_A\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCommunality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRedundancy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSTS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMental Workload\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCaring Behavior\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.023\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\u003eAVE\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;= Average Variance Extracted; \u003cstrong\u003eComposite Reliability\u003c/strong\u003e = Internal consistency; \u003cstrong\u003erho_A\u003c/strong\u003e = Cronbach\u0026apos;s alpha; \u003cstrong\u003eCommunality\u003c/strong\u003e = Shared variance; \u003cstrong\u003eRedundancy\u003c/strong\u003e = Redundancy index; \u003cstrong\u003eR Square\u003c/strong\u003e = Explained variance\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eTable 4: Discriminant Validity Assessment Using the Fornell-Larcker Criterion\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"592\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 158px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSTS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMental Workload\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 158px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCaring Behavior\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 158px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSTS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e0.885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 158px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 158px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMental Workload\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e0.383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e0.634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 158px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 158px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCaring Behavior\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e- 0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 158px;\"\u003e\n \u003cp\u003e0.878\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003cstrong\u003eTable 5: Estimates of Standardized Coefficients, Means, Standard Deviations, and Statistical Significance of Variables\u003c/strong\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"598\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 252px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOriginal Sample\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard Deviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-values\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 252px;\"\u003e\n \u003cp\u003eSTS \u003cimg width=\"20\" height=\"8\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAAICAYAAAD5nd/tAAAAAXNSR0ICQMB9xQAAAAlwSFlzAAAOxAAADsQBlSsOGwAAABl0RVh0U29mdHdhcmUATWljcm9zb2Z0IE9mZmljZX/tNXEAAACuSURBVCjPY2hsbGQgBQMBOxAHArE0VnkyDGQD4mNA/AaI84CYFU2e4biUlNRxNTW146qqqngxUM1RFRWVE7Kysu+zs7P/x8bG/gfqPwrEbsgG/l+7du3/79+////27RtR+OvXr/9hYP/+/f9tbGxABq8FYkmKDbx27dp/V1dXkIFHgFiBbC+Xlpb+Ly4u/s/Ozn4TaEY0JZHCCg23H0DcBMSClMYyJxCnALEWNnkAGUcZrLznhRUAAAAASUVORK5CYII=\" alt=\"image\"\u003e\u0026nbsp;Caring Behavior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e- 0.174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e- 0.171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 252px;\"\u003e\n \u003cp\u003eMental Workload \u003cimg width=\"20\" height=\"7\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAAHCAYAAAAIy204AAAAAXNSR0ICQMB9xQAAAAlwSFlzAAAOxAAADsQBlSsOGwAAABl0RVh0U29mdHdhcmUATWljcm9zb2Z0IE9mZmljZX/tNXEAAADBSURBVCjPY2hsbGQgBQMBIxBHA7EODnkGZRKwEhBrAvEFZmbmP0B6FoiPbuB3X1/f7/n5+d/z8vLwYqCab7m5ud+zsrL+rFu37v+KFSv+m5ubf2ZnZ58MNEcd6nqG/2fOnPlPLvj9+/f/sLCw/0BzXkINZfjm5+f3raCg4BvIBQTwV6BLv4FcuHnz5v+7d+/+7+np+ZePj2850BxLIGZlgIYLsVgRiDWA+BwXFxfIVWuB2AolDMmIZSYgzgZia2zyAG5A1ywUZzxAAAAAAElFTkSuQmCC\" alt=\"image\"\u003e\u0026nbsp;STS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0.383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 252px;\"\u003e\n \u003cp\u003eMental Workload \u0026nbsp;\u003cimg width=\"20\" height=\"7\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAAHCAYAAAAIy204AAAAAXNSR0ICQMB9xQAAAAlwSFlzAAAOxAAADsQBlSsOGwAAABl0RVh0U29mdHdhcmUATWljcm9zb2Z0IE9mZmljZX/tNXEAAADBSURBVCjPY2hsbGQgBQMBIxBHA7EODnkGZRKwEhBrAvEFZmbmP0B6FoiPbuB3X1/f7/n5+d/z8vLwYqCab7m5ud+zsrL+rFu37v+KFSv+m5ubf2ZnZ58MNEcd6nqG/2fOnPlPLvj9+/f/sLCw/0BzXkINZfjm5+f3raCg4BvIBQTwV6BLv4FcuHnz5v+7d+/+7+np+ZePj2850BxLIGZlgIYLsVgRiDWA+BwXFxfIVWuB2AolDMmIZSYgzgZia2zyAG5A1ywUZzxAAAAAAElFTkSuQmCC\" alt=\"image\"\u003e\u0026nbsp; Caring Behavior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6: Multiple Mediation Analysis of Indirect Effects Using the Sobel Test (Full Sample)\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"605\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 396px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePath\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ez\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSig.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 396px;\"\u003e\n \u003cp\u003eMental workload to Caring Behaviors through STS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e2.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\u003cstrong\u003e\u003c/strong\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7: Model Evaluation \u0026amp; Fit Indices\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"622\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 622px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGoodness-of-Fit Indices\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSRMR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eD_ULS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003e0.979\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eD_G\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003e0.224\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChi-Square\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003e454.888\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNFI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTLI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCFI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRMSEA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003e0.064\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\u003eSRMR =\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cem\u003eStandardized Root Mean Square Residual;\u003cstrong\u003e\u0026nbsp;D_ULS =\u0026nbsp;\u003c/strong\u003eEuclidean Distance Squared;\u003cstrong\u003e\u0026nbsp;D_G =\u0026nbsp;\u003c/strong\u003eGeodesic Distance;\u003cstrong\u003e\u0026nbsp;Chi-Square =\u0026nbsp;\u003c/strong\u003eChi-square test;\u003cstrong\u003e\u0026nbsp;NFI =\u0026nbsp;\u003c/strong\u003eBentler-Bonett Normed Fit Index;\u003cstrong\u003e\u0026nbsp;TLI =\u0026nbsp;\u003c/strong\u003eTucker-Lewis Index;\u003cstrong\u003e\u0026nbsp;CFI =\u0026nbsp;\u003c/strong\u003eComparative Fit Index;\u003cstrong\u003e\u0026nbsp;RMSEA =\u0026nbsp;\u003c/strong\u003eRoot Mean Square Error of Approximation\u003c/em\u003e\u003c/p\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-nursing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurs","sideBox":"Learn more about [BMC Nursing](http://bmcnurs.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurs/default.aspx","title":"BMC Nursing","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Secondary traumatic stress, Mental workload, Caring behaviors, Nurse","lastPublishedDoi":"10.21203/rs.3.rs-6756826/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6756826/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction:\u003c/h2\u003e \u003cp\u003eNurses' caring behaviors play a critical role in communicating value, safety, and compassion to patients. Mental workload is a significant factor influencing these behaviors, with up to 93% of nurses reporting exposure to occupational stressors. Previous studies have shown that Secondary Traumatic Stress (STS), a condition resulting from indirect exposure to trauma, may serve as a mediator in the relationship between mental workload and caring behaviors. Therefore, this study aimed to examine the effect of mental workload on nurses' caring behaviors, with a particular focus on the mediating role of STS.\u003c/p\u003e\u003ch2\u003eMethodology:\u003c/h2\u003e \u003cp\u003eThis cross-sectional correlational study employed Structural Equation Modeling (SEM) to analyze data collected from a total of 340 nurses working in teaching hospitals in Urmia, Northwest Iran. Data were collected using a demographic questionnaire, the NASA Task Load Index (NASA-TLX), the Secondary Traumatic Stress Scale (STSS), and the Caring Behaviors Inventory-24 (CBI-24). Statistical analysis was conducted using SPSS version 26, while SEM was conducted using SmartPLS 3 and Mplus 7.4 software packages.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe majority of participants were female nurses (70.9%), with a mean age of 32.13\u0026thinsp;\u0026plusmn;\u0026thinsp;6.30 years. SEM analysis showed that mental workload had a statistically significant positive effect on caring behaviors (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.167), while STS had a significant negative effect (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.174). Furthermore, the indirect effect of mental workload on caring behaviors through STS was significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), which confirmed the mediating role of STS (\u003cem\u003eTLI\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.9, \u003cem\u003eCFI\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.9, \u003cem\u003eRMSEA\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.08, \u003cem\u003eSRMR\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eNurses are subject to high levels of both mental workload and STS, which have opposing effects on their caring behaviors. While mental workload may enhance certain aspects of care delivery, STS negatively impacts the quality of patient care. These findings further highlight the need for targeted interventions to manage STS and optimize mental workload to maintain and improve nursing care quality.\u003c/p\u003e","manuscriptTitle":"The Effect of Mental Workload on Nurses' Caring Behaviors: The Mediating Role of Secondary Traumatic Stress - A Cross-Sectional Correlational Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-10 11:23:59","doi":"10.21203/rs.3.rs-6756826/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-07-18T16:10:24+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-18T12:04:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"323224106932306237395562817300895147167","date":"2025-07-16T04:21:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"303612973547458616486151219729251696595","date":"2025-06-08T19:26:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-06T09:20:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"44080549235872523677007016119361068112","date":"2025-06-06T09:13:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-05T18:24:21+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-28T08:07:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-28T02:30:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-28T02:30:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nursing","date":"2025-05-27T07:45:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nursing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurs","sideBox":"Learn more about [BMC Nursing](http://bmcnurs.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurs/default.aspx","title":"BMC Nursing","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"aea4dc90-d0fc-4e2c-b31a-c75c05d4ec17","owner":[],"postedDate":"June 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-06-10T11:23:59+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-10 11:23:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6756826","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6756826","identity":"rs-6756826","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}