key: cord-0944725-14p3mod3 authors: Shan, Yawei; Shang, Jing; Yan, Yan; Lu, Gendi; Hu, Deying; Ye, Xuchun title: Mental workload of frontline nurses aiding in the COVID‐19 pandemic: A latent profile analysis date: 2021-02-16 journal: J Adv Nurs DOI: 10.1111/jan.14769 sha: 3c578b812fe3200381a9717aa45845df5d6cd44b doc_id: 944725 cord_uid: 14p3mod3 AIMS: To investigate the mental workload level of nurses aiding the most affected area during the Coronavirus disease 2019 (COVID‐19) pandemic and explore the subtypes of nurses regarding their mental workload. DESIGN: Cross‐sectional study. METHODS: A sample of 446 frontline nurses participated from March 8 to 19, 2020. A latent profile analysis was performed to identify clusters based on the six subscales of the Chinese version of the National Aeronautics and Space Administration Task Load Index. The differences among the classes and the variables including sociodemographic characteristics, psychological capital and coping style were explored. RESULTS: The level of mental workload indicates that the nurses had high self‐evaluations of their performance while under extremely intensive task loads. The following three latent subtypes were identified: ‘low workload & low self‐evaluation’ (8.6%); ‘medium workload & medium self‐evaluation’ (35.3%) and ‘high workload & high self‐evaluation’ (56.1%) (Classes 1, 2, and 3, respectively). Nurses with shared accommodations, fewer years of practice, junior professional titles, lower incomes, nonmanagement working positions, lower psychological capital levels and negative coping styles had a higher likelihood of belonging to Class 1. In contrast, senior nurses with higher psychological capital and positive coping styles were more likely to belong to Classes 2 and 3. CONCLUSION: The characteristics of the ‘low workload & low self‐evaluation’ subtype suggest that attention should be paid to the work pressure and psychological well‐being of junior nurses. Further research on regular training program of public health emergency especially for novices is needed. Personnel management during public health events should be focused on the allocation between novice and senior frontline nurses. IMPACT: This study addresses the level of mental workload of frontline nurses who aid in the most severe area of the COVID‐19 pandemic in China and delineates the characteristics of the subtypes of these nurses. The ongoing outbreak of novel pneumonia caused by the Coronavirus disease 2019 has raised considerable concerns globally, as it is associated with high infection rates and fatal outcomes (Zhu et al., 2020) . Although a rapid response and timely detection were implemented globally, large-scale infection and death have been reported (World Health Organization, 2020a) . In mainland China, the Chinese government announced its highest-level commitment to respond to the pandemic and prevent its further spread (World Health Organization, 2020b). As the highest peak occurred on February 12, 2020. with 30,042 existing conformed cases (National Health Commission of the People's Republic of China, 2020a), more than 42,000 health care professionals (HCPs) were sent to Wuhan by February 29, 2020, which is the most affected area in China (National Health Commission of the People's Republic of China, 2020b) . With the ever-increasing number of infected cases, HCPs on the frontline might be under both physical and psychological pressure (Lai et al., 2020) . Among these HCPs, nurses aiding in the COVID-19 pandemic account for 68% (28,600) and have been considered the major workforce in pandemic control (National Health Commission of the People's Republic of China, 2020b). As reported in many public health pandemics, such as the SARS-CoV, MERS-CoV (Park et al., 2018) and 2009 influenza A (H1NI) (Nap et al., 2008) pandemics, the high workload of nurses on the frontline is a major concern for efficient health care, patient safety, and the physical and mental health of nurses (World Health Organization, 2019). As COVID-19 appears to be 10 times more contagious than SARS-CoV and MERS-CoV (Ahn et al., 2020) , this might increase the workload burden of frontline nurses. However, given the different types of workloads (Holden et al., 2011) , not all workloads result in compromised performance . Therefore, apart from the task load, a particularly interesting construct related to state of mind, namely, mental workload (MWL), warrants considerable attention (Sumwalt et al., 2019) . However, little is known about the level of MWL among nurses aiding in Wuhan. Whether there exist different MWL clusters in nurses and how to identify these clusters are worthwhile to explore. The main study objective is to investigate the level of MWL among nurses in Wuhan during the COVID-19 pandemic, and to explore the subtypes of MWL among these nurses. The specific objective is to identify the characteristics of subtypes. Mental workload is a multidimensional and multifaceted concept that explains the relationship between the nature of a task and the characteristics of the worker. This subjective factor can be defined as the amount of thinking, level of cognitive demand or thought processing effort required by the worker to meet the physical, temporal and environmental demands of the defined task (Young et al., 2015) . It is a more comprehensive variable than the simple quantity of tasks for predicting nurses' mental health and work performance, especially in some complex and dynamic situations (Byrne, 2013) . The assessment and management of MWL was recommended by the European Pact for Mental Health and Welfare to promote physical and mental well-being (Scheftlein, 2011) . Because of the urgency of managing the variety of human factors that influence the mental health of HCPs and that thus compromise pandemic control (Carayon, 2011) , researchers should examine the topic of MWL in frontline nurses, especially in pandemic regions (Ticharwa et al., 2019) . While drawing insights from previous studies that have provided a solid foundation for the present study, the researchers seek to go a step further to identify the different subtypes of MWL among nurses in the most affected area in China and investigate the characteristics of the different subtypes to, in turn, improve the mental health of frontline nurses. In this way, two technical issues should be addressed, namely, the use of a feasible statistical methodology for MWL grouping and the identification of the major characteristics of each subtype. With respect to the statistical methodology, previous studies on nurses' MWL were conducted based on a variable-centred analytical approach (Koch et al., 2012) . However, the identification of different facets of MWL among pandemic frontline nurses provides an opportunity for policy makers to take measures to prevent negative physical and psychological outcomes of nurses and improve their clinical performance. Latent profile analysis (LPA) is a person-centred statistical method that provides a methodology to group individuals with similar patterns of personal and professional characteristics, traits or behaviours into profiles according to their responses to a set of observed indicators. This statistical analysis method is rather novel in the MWL research among nurses, but it has been shown to be usable and valid for exploring the subtypes of clinical competency Oyesanya & Snedden, 2018) , work stress (Jenull & Wiedermann, 2015) and job satisfaction in HCPs. Therefore, LPA can be employed to identify the patterns of MWL among pandemic frontline nurses. According to the human-based archetype of MWL proposed by Mohammad-jabad Jafari et al., task demand, resource supply and individual characteristics are the key variables that influence psychophysiological responses and workload modification (Jafari et al., 2019) . For all pandemic frontline nurses in China, the task demands of nursing care and the external resources from the government and designated hospitals for COVID-19 treatment are generally equivalent. Therefore, the personal resources and core individual characteristics associated with the MWL of frontline nurses might be essential for identifying the subtypes. Current studies predominantly address several sociodemographic variables that influence nurses' MWL, including living conditions, financial status (Moloney et al., 2018) and work experience (Hegney et al., 2019; Kallberg et al., 2017) . Regarding the internal psychological and behavioural factors that reflect personal resources, psychological capital (PsyCap) and coping style were also a focus of this study, following a previous study (Liling, 2019) . PsyCap is recognized as a personal resource that predicts nurses' mental health and work performance (Boamah & Laschinger, 2015) . It is an individual, positive, motivational propensity that accrues through positive psychological characteristics such as self-efficacy, optimism, hope and resilience (Fred Luthans et al., 2007) . The development of PsyCap promotes psychological well-being and effective work performance (Fred Luthans & Youssef, 2004) . Therefore, exploring the features of PsyCap in relation to different facets of MWL in pandemic frontline nurses could identify target populations for precise intervention. Coping style is another internal factor that reflects personal resources related to MWL; it is defined as the set of cognitive and behavioural strategies used by an individual to manage the internal and external demands of stressful situations (Folkman & Moskowitz, 2004) . In contrast with the traditional classification of positive and negative coping, Gou et al. recommended six dimensions of coping, including avoidance or self-accusation, emotional distress alleviation, social support seeking, positive reinterpretation and behavioural disengagement (Gou et al., 2006) , which provide more specific information for describing MWL subtypes. The main hypotheses of this study were as follows: The aim of this study is to investigate the level of MWL among nurses aiding in Wuhan during the COVID-19 pandemic, identify the subtypes of MWL among nurses and explore the characteristics of different MWL clusters in terms of sociodemographic factors, PsyCap and coping style. A cross-sectional self-report study design was conducted. Frontline nurses were recruited in a tertiary hospital in Wuhan, which was redesigned to provide health care to patients infected with COVID-19. There were 1,120 frontline nurses from 12 provinces at this hospital. Of the 1,120 nurses approached, 477 were interested in this study and completed the questionnaires. Data were collected from March 8 to 19, 2020 through an online questionnaire platform called Wenjuanxing (www.wjx.cn), on which only a fully completed questionnaire can be uploaded. Initial permission was sought and obtained from various department heads and hospital administrators before the release of the recruiting information and questionnaire. The frontline nurses reported their sociodemographic characteristics, MWL, PsyCap and coping style. We discontinued data collection when the data were not uploaded in 7 days. Participants in this study was entirely voluntary. A sociodemographic questionnaire was designed to collect information on characteristics including gender, age, marital status, financial status, education, clinical experience (years of clinical practice and professional title) and practice department. MWL data were obtained using the Chinese version of the National Aeronautics and Space Administration Task Load Index (NASA-TLX) (Hart & Staveland, 1988) . The NASA-TLX is a well-validated and widely used measure in human factors and ergonomics that comprises six subscales or dimensions regarding different aspects of workload (mental demands, physical demands, temporal demands, performance, effort and frustration). The Chinese version was translated by Liang et al. (Liang et al., 2019) ; in this version, the items are rated on a 20-point bipolar scale that ranges from 0 to 100. For five of the six dimensions, i.e., mental demands, physical demands, temporal demands, effort and frustration, a score of 0 indicates the lowest task load; however, the performance dimension is reversescored, with 0 indicating the most successful performance of the task and the highest level of satisfaction with one's performance. The Cronbach's α of the total Chinese version of the scale is 0.707 (Liang et al., 2019) . In this study, we used the total (mean) MWL score rather than the weighted workload score. PsyCap was measured using the 24-item Psychological Capital Questionnaire (PCQ-24) developed by Luthans , which consists of the four subscales of self-efficacy, hope, optimism The Chinese version of the Coping Style for Nurses scale was used to assess the attitudes and behaviours of individuals during stressful events; the scale contains 30 items. All items are scored on a 5-point Likert scale where 0 is 'never' and 4 is 'always'. The scale is composed of six subscales, namely, problem solving, avoidance or self-accusation, emotional distress alleviation, social support seeking, positive reinterpretation, and behavioural disengagement. A high score on a certain subscale reflects a strong propensity to adopt the corresponding coping style. The Cronbach's α of the total scale of the Chinese version is 0.867 (Gou et al., 2006) . The psychometric properties of the measurement tools have been described above. Exploratory LPA using Mplus Software (version 7.0) was performed to identify clusters based on the six subscales. Data for the six dimensions were entered into the LPA, with one class initially and additional classes added incrementally, until a unique solution could not be determined with maximum likelihood methods. The fit indices were examined. The Akaike information criterion (AIC) (Akaike, 1978) , Bayesian information criterion (BIC) and sample-sizeadjusted BIC (aBIC) were applied, with the lowest value indicating the best fit (Stanley, 1987) . The Lo-Mendell-Rubin (LMR) adjusted likelihood ratio test and bootstrap likelihood ratio test (BLRT) were used to assess the p-values in the comparisons between models (Lo et al., 2001) . A low p-value indicates that the k-class model fits the data better than the k-1-class model. In addition, entropy values, which range from 0 to 1, were used to evaluate the separability of each LPA solution; values closer to 1 represent a better separation of the classes (Ramaswamy et al., 1993) . To test the differences between sociodemographic and occupational characteristics and to determine the psychological characteristics of the subtypes based on LPA, SPSS 21.0 was used, and all statistical tests were two-sided (α = 0.05). The statistical methods included descriptive statistical calculations (e.g., percentage, minimum, maximum, mean and standard deviation), and Student's t test, a one-way ANOVA or a chi-square (χ 2 ) test were used to compare the variables. In total, 477 nurses participated, and after non-frontline nurses (31 nurses) were excluded, the number of valid responses was 446 (93.50%) without any missing data. Overall, 90.81% of the nurses (n = 405) were female, and 50% of the participants (n = 223) ranged in age from 31-40 years. The sociodemographic characteristics of the participants are shown in Table 1 Table 2 . The best fitting LPA was the three-class model (Table 3) can be separated from one another by a relatively low, medium and high MWL level, and the nurses with a high MWL represented more than half of the total sample. Class 3, accounted for the majority of sample, had the highest task load but the best self-evaluated performance, which could be the important workforce in health care in public health emergency. Class 1 had the lowest task load level but the worst self-reported performance, which indicates a major concern that should be focused on. In this study, the total mean MWL score was 65.90 (SD 12.71), which indicates a medium level of MWL. The classes divided by the LPA showed that the total mean MWL score in Class 3 (which accounted for 56.96% of the total sample) was 73.59 (SD 8.86), which suggests a much lower level of MWL than the MWL reported not only in a study conducted by Habibi et al. (Habibi et al., 2015) with nurses in Iran (77.7 ± 12.6) but also in a study by Sönmez et al. (Sönmez et al., 2017) with nurses in Turkey (80.48 ± 11.76) and in a study data reported in recent study . However, the objective workload might be increased because personal pro- Regarding the significant differences in the sociodemographic char- -Vásquez et al., 2015) . Therefore, among the three classes, Class 1 had the lowest mean MWL score in this study. However, the mean self-reported work performance score was the highest for this class, which indicates that these nurses were the least successful in their performance or the least satisfied with their performance. One of the reasons might be that they were less experienced or inadequately prepared before aiding in the COVID-19 pandemic, which identifies the importance of training before participating in aid work (Mohamadi et al., 2019) . Another possible reason is that they may not have met their own lofty expectations for pandemic control performance. However, their level of frustration was low (30.541 ± 21.660). It can be speculated that the physical and psychological stress during COVID-19 aid work may not result in job burnout; however, job burnout in frontline nursing has been reported in other countries (Rajkumar, 2020) . Concerning the differences in the PsyCap among the groups, Class 3 showed the highest level of PsyCap, which confirms the association between MWL and PsyCap, especially in the domains of self-efficacy, hope and optimism. Previous studies have found that nurses are willing to provide care during a pandemic because of their commitment as HCPs (Wong et al., 2008) . Willingness and motivation can positively influence nurses' self-efficacy and ability to work to provide aid during a pandemic (McMullan et al., 2016) . In this study, the frontline nurses were all volunteers; apart from professional responsibility and personal dedication, government policies on extra compensation and special recognition might have been positive motivators, which has also been reported in other epidemics (Khalid et al., 2016; Simonds & Sokol, 2009 ). Moreover, a previous study has also reported that a positive attitude towards success (optimism and hope) can ease the stress of HCPs and improve their work performance (Khalid et al., 2016) , which may explain the relationship between PsyCap and MWL in this study. However, the domain of resilience did not show significant differences among the groups in this study, and the reason for this might be determined by the characteristics of resilience. Resilience is defined as a positive coping and adaptation mechanism in the face of significant risk, conflict, failure, or even positive change and progress (Luthans, 2002) ; it is recognized as a state-like variable more than a trait-like construct. Therefore, it might take time to adapt to intensive frontline care and 'bounce back' from adverse events. Consequently, Comparison of the mental workload of the classes by sociodemographic variable among the three classes. The efficient resolution of the wide array of problems encountered in pandemic health care is the main factor that helps ease MWL, which is consistent with the findings in our study that the performance dimension score predicted the overall level of MWL. In addition, as reported in previous studies on other epidemics, the anxiety and distress felt by frontline HCPs are common and can result in a compromised quality of care and long-term psychological outcomes of HCPs (Khalid et al., 2016; Lai et al., 2020; Lin et al., 2007) . Therefore, efficient and positive coping strategies such as emotional distress alleviation and positive reinterpretation can help decrease MWL. In contrast, coping styles such as avoidance or self-accusation and behavioural disengagement are negative behaviours, and they were observed to be at low levels in the three classes in this study. However, there were no differences in the domain of social support seeking among the three classes, which might be because support (especially in terms of medical supplies) from local hospitals and the government on the frontline was sufficient in March, and mental health and psychosocial support provided by psychologists was accessible. This study has several limitations. (a) We employed an online questionnaire platform to recruit participants and collect data. The number of delivered questionnaires and the differences be- In this survey study, frontline nurses reported high levels of task load but good self-evaluated performance and low frustration. Their profiles differed primarily in professional experience. The current data provide evidence to focus more on the work pressure and psychological well-being of junior nurses. Further research on regular training programs is needed to improve novices' knowledge and skills regarding public health emergency. Research on facilitating the PsyCap level and positive coping styles could be considered, and social support could be enhanced to promote work performance. As for the senior professionals, future practice could involve a major proportion of these nurses for successful frontline aid. Comparison of the different classes by mental workload, psychological capital and coping strategy This study was supported by grants from the National Natural Science and Technology Youth Cultivation Program (19QNP019). No conflict of interest has been declared by the authors. 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