The Relationship between Perceived Social Support, Psychological Capital, Job Burnout, and Work Engagement in Nurses Working On The Front Line Of The Fight Against The Coronavirus

doi:10.21203/rs.3.rs-4281322/v1

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Research Article

The Relationship between Perceived Social Support, Psychological Capital, Job Burnout, and Work Engagement in Nurses Working On The Front Line Of The Fight Against The Coronavirus

https://doi.org/10.21203/rs.3.rs-4281322/v1

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Background

Due to the coronavirus epidemic, nurses on the front line of caring for infected patients are always faced with many job stressors, and one of the most inevitable consequences is job burnout, which can cause challenges for work engagement. This study used structural equation modeling to determine the effects of psychological capital and perceived social support on job burnout and work.

Methods

This study was a cross-sectional descriptive-analytical type. 306 nurses on the frontline of the fight against the coronavirus in the public hospitals of Mazandaran University of Medical Sciences were selected by multi-stage random stratification. The data collection process lasted from September 19 to November 19, 2021. To collect data from Zimet et al. perceived social support questionnaire, Lutans psychological capital questionnaire, Utrecht work engagement scale, and Maslach burnout inventory. PLS3 and SPSS25 software were used to analyze the data. This study was approved by the Ethics Committee of the University of Social welfare and Rehabilitation under the number IR.USWR.REC.1400.105.

Results

More than half of the people were women, married, under 40 years old, with a bachelor's degree, with over 10 years of work experience, and over one year of experience in corona departments. The fit indices of the research model were favorable (GOF > 0.529) and the research model fit was confirmed. Also, based on the path coefficient there was a significant negative relationship between perceived social support with job burnout (β=-0/115) and psychological capital with job burnout (β=-0/503) and a significant positive relationship between perceived social support with work engagement (β = 0/132) and between psychological capital with work engagement (β = 0/607) (P˂0.05).

Conclusions

Considering the inevitable effect of perceived social support and psychological capital on reducing job burnout and increasing work engagement; our findings can be used To develop the occupational health of employees and then to develop the quality of health care services by health managers and policymakers. Also, interventions based on social support and psychological capital can be identified and studied as a new area for effective psychological interventions among nurses.

perceived social support

psychological capital

job burnout

work engagement

nurse

coronavirus

covid-19

Structural equation

Coronavirus disease 19 (COVID-19), started in Wuhan, China in early December 2019 and has spread rapidly around the world in a few weeks on January 30, 2020, was reported as a public health emergency by the World Health Organization (1). The outburst of the coronavirus disease has become a significant public health event worldwide, disrupting people's lives worldwide with rapid spread, high mortality, disruption of social structure, health care system casualties, and withering economic effects(2, 3). The eruption of emerging infectious diseases, such as Covid-19, has contributed to the constantly increased flow of patients and stress levels in nurses, prompting their burnout(4). Burnout is a basic nursing concern causing outcomes such as low quality of care, decreased patient safety, sickness absence, turnover, and job dissatisfaction, which irrevocably degrades the quality of nursing care(5). Maslach and Jackson conceptualized “burnout” as a prolonged reaction against interpersonal chronic, and emotional stressors at work, characterized by emotional exhaustion, depersonalization, and a diminished sense of personal accomplishment(6). Burnout researchers have examined work engagement as the opposite of burnout, maintaining that work engagement is determined by vigor, absorption, and dedication. Vigor is represented by high energy levels and mental resilience during working. Dedication is a sense of significance, inspiration, enthusiasm, pride, and challenge. Absorption is defined as entirely concentrating on and being deeply engaged in the work. Generally, rather than a specific, fleeting state, engagement is a more pervasive and persevering affective-cognitive condition, which does not focus on any special object, behavior, or event (7).

In the meantime, psychological capital is involved in job burnout development and can decrease burnout extent (8, 9). Also, as an essential and effective individual resource, psychological capital can foster work engagement (10–12). Psychological capital can be defined as positive psychological states of personal development, which consist of hope, self-efficacy, optimism, and resilience. These four components are highly changeable such that everyone can strengthen them in different ways given the right psychological conditions(9).

The lack of social support is one of the main factors in creating negative psychological consequences in high-risk working conditions. According to the stress-buffering model by Cohen, social support can protect individuals against or moderate the destructive effects of workplace stress and life on mental health (13). Social support is socioemotional support using social ties to other people,

i.e., family, friends, colleagues, and other important people who have an important place in a person's life. Perceived social support is described as assistance and feeling of support received from formal or informal social networks, accessible from both workplace and non-work-related sources (14).

Given the pivotal role of the nursing profession in providing medical services, it tremendously affects health, disease outcomes, and their indicators in the health system. It was hoped that our results would help to achieve the elevated objectives of the health system, especially in the nursing sector.

This cross-sectional descriptive-analytical research was approved by the Ethics Committee of the university of

Social Welfare and Rehabilitation Sciences (IR.USWR.REC.1400,105.) .

Participants

The research population involved all frontline nurses fighting against Covid-19 in Iran, and the study sample encompassed all frontline nurses fighting against the Covid-19 pandemic working in the hospitals of Mazandaran University of Medical Sciences in 2021. The research sample was selected using multistage stratified sampling. Thus, based on the geographical location of each hospital assigned to treating Covid-19 patients, Mazandaran province was divided into three equally-sized districts: central, western, and eastern. In each district, two hospitals were selected through simple random sampling. Then, after accessing the information on nurses working in Covid-19 sections of selected hospitals, the desired number of research samples was selected by a simple random method considering the share of the two hospitals opted out of each district. According to Cochran's formula, 306 nurses at the hospitals of Mazandaran University of Medical Sciences were included in the study. Thereby, a total of 102 nurses were equally assigned to each district group.

The inclusion criteria included all nurses working in public hospitals of Mazandaran University of Medical Sciences without management positions, all nurses working in the Covid-19 sections of hospitals, and the interest and satisfaction of nurses to participate in the research. The exclusion criteria were the participant's unwillingness to proceed with the study and incomplete completion of the questionnaires.

Data Collection Instruments

Demographic Information Questionnaire

This questionnaire comprised data regarding age, gender, marital status, work experience, hospital titles, working section, education, and income.

The Multidimensional Scale of Perceived Social Support (MSPSS)

MSPSS was designed by Zimet et al. (1998) and has 12 items to identify an individual’s perceived level of social support with friends, family, and significant others measured on a 5-point scale from Very Strongly Disagree to Very Strongly Agree. Zimet et al. reported acceptable validity and reliability for this scale(15). Moreover, Bagherian et al. in a study entitled “Psychometric features of the Persian version of Multidimensional Scale of Perceived Social Support”(16), and also Salimi et al., confirmed its validity (17). Its reliability for all three dimensions, as reported by Salimi et al. was 0.84 through Cronbach's Alpha.

Luthans' Psychological Capital Questionnaire (PCQ)

Designed by Luthans (2007), the Psychological Capital Questionnaire has 24 items about an individual's Psychological Capital (PsyCap). It assesses four dimensions of PsyCap, including hope, resiliency, efficacy, and optimism based on a 6-point Likert scale from Strongly Disagree to Strongly Agree. Rajaei, Nadi, and Jafari reported Cronbach's alpha coefficient of 0.89% for positive psychological capital. The coefficients for subscales of optimism, hope, resiliency, and efficacy were 0.70, 0.83, 0.73, and 0.87, respectively. The correlation between the total positive psychological capital and each subscale of optimism, hope, resiliency, and efficacy was 0.76,

0.87, 0.78, and 0.84, respectively, representing acceptable validity(18).

Maslach Burnout Inventory (MBI)

MBI has 22 items on a Likert scale whose items are rated using 7-level frequency ratings from "never" to "daily", from low to very high-intensity levels. The MBI has three subscales: emotional exhaustion, personal achievement, and depersonalization. Moalemi et al. showed that the weight of each item in its dimension is 0.4 or more than the other dimension. Likewise, Cronbach's alpha for the three dimensions was reported to be greater than 0.7(19).

The Utrecht Work Engagement Scale (UWES)

UWES has 9 items aiming to assess three dimensions of work engagement: vigor, absorption, and dedication. Torabian et al. showed its acceptable construct and content validity. Also, the internal consistency reliability for the whole scale and subscales was 0.76–0.89(20). This scale is rated on a 5-point Likert scale (Strongly Disagree to Strongly Agree).

Research Procedure

After receiving permission from the ethics committee in research, the researcher acquired the list and names of nurses working in the Covid-19 section of that hospital in cooperation with the nursing office. The desired number of samples was randomly selected from those lists, taking into account the sample attrition. Given the coordination of the security wards of the respective hospitals, nurses' contact information was provided to the researcher. The researcher then provided participants with questionnaires using online social network platforms, and the sampling procedure continued until the questionnaires were completed. The data collection process was carried out from September 19 to November 19, 2021.

DATA ANALYSIS

The Kolmogorov-Smirnov test was used to assess the normality of the data, and considering that the distribution of the variables did not follow the normal distribution, the non-parametric Spearman correlation test was used to check the research hypotheses. Spearman's correlation results showed a significant correlation between all research variables and it was also possible to use the structural equation method. Data analysis was done using SPSS version 25 and a structural equation test with a partial least squares approach (PLS version 3).

According to the demographic table, it can be seen that 42 respondents are male and 264 are female. 217 of these people are married and 89 are single. 114 people in the sample are less than 30 years old, 110 people are between 30 and 40 years old, 82 people are more than 40 years old, 278 people have a BSc degree and 28 people have an MSc degree. Also, most samples (48%) have over ten years of work experience, (91.8%) are bedside nurses, and (67.6%) have an income between 7 and 10 million Tomans. Also, of these people, 80 people have been working in the Corona emergency department, 134 people in the general Corona department, and 92 people in the Corona special care department, and most of these people (67.6%) have been working in the Corona department for more than a year. Demographic information is supplied in Table 1.

Table 1

Demographic Information Regarding nurses in the Covid-19 Section of Hospitals
Variables	Grouping	Frequency	%
Gender	Male	42	13/7
Gender	Female	264	86/3
marital status	married	217	70/9
marital status	Single	89	29/1
Age	< 30 years	114	37/3
	30–40 years	110	35/9
	> 40 years	82	26/8
education	bachelor	278	90/8
education	Masters	28	9/2
Work Experience	< 2 years	48	15/7
	Between 2 and 5 years	63	20/6
	Between 5 and 10 years	47	15/4
	> 10 years	148	48/4
Responsible position	Staff	281	91/8
Responsible position	head nurse	25	8/2
Monthly income	4 to 6 million tomans	53	17/3
	7 to 10 million tomans	207	67/6
	More than 10 million tomans	46	15
Hospital department	Corona emergency	80	26/1
	General Corona	134	43/8
	Corona intensive care unit	92	30/1
Duration of activity in the Covid-19 department	Less than 1 year	100	32/7
Duration of activity in the Covid-19 department	More than 1 year	206	67/3

Measurement Model

Considering Cronbach’s alpha values as well as the combined reliability presented in Table 2, all variables had

Table 2

Result of the measurement model
Constructs	Cronbach's alpha	composite reliability	Mean-variance extracted (AVE)
Perceived Social Support	0/765	0/863	0/679
Psychological capital	0/874	0/914	0/726
Job burnout	0/871	0/921	0/795
Work engagement	0/846	0/907	0/764

Cronbach’s alpha values more than 0.765, indicating the good reliability of the model domains. Convergent validity can be applied to fit the model in the PLS structural equation modeling method, which was more than 0.5 and acceptable (Table 2).

Discriminant validity refers to the extent to which items are distinguished among the constructs or measure distinct concepts. It was determined by assessing the correlations between constructs and the square root of AVE for each construct (Table 3) (the square root of the AVE was more than the correlation values in the column and the row). Hence, the model was accepted because it fits all the convergent and discriminant validity criteria.

Table 3

Discriminant validity of constructs
	Perceived Social Support	Psychological capital	Job burnout	Work engagement
Perceived Social Support	0/824
Psychological capital	0/574	0/852
Job burnout	-0/404	-0/569	0/892
Work engagement	0/48	0/682	-0/644	0/874

Structural Model

As shown in Table 4 the R² average was more than the medium which is specified and it indicates that the predictive power of the model is moderate, and it confirms the appropriate fit of the research structural model.

Table 4

Communality and R2 value
factors	R2 value	Communality values
Perceived Social Support	-	0/679
Psychological capital	0/329	0/726
Job burnout	0/333	0/795
Work engagement	0/477	0/764

The structural model indicates the causal relationship among the model constructs (the R² value and path coefficient). The R² value and path coefficient (beta and significance) indicate how the hypothesized causal relationship is supported by the data in the model. Table 5 and Fig. 1 indicate the structural model result from the PLS output.

Table 5

Summary of the structural model
path	β value	t-statistics	Decisions
Perceived social support -> psychological capital	0/574	12/186**	S
Perceived social support -> job burnout	0-/115	2/144*	S
Perceived social support -> work engagement	0/132	2/789*	S
Psychological capital -> job burnout	0-/503	9/731**	S
Psychological capital -> work engagement	0/607	13/825**	S
**P˂0/001
*P˂0/05

Overall model fit

The goodness‑of‑fit (GOF) index as a measure of the model's overall fit can predict endogenous variables.

It is the multiplied squared of two values of the R² average and the commonality average.

GOF = √average (communality) × R₂

Average (communalities) = 0.741

Average (R₂) = 0.379

GOF = 0.529 strong overall fit

The obtained value for GOF was 0.529 which was more than the strong value of 0.36 (Table 4); thus, the overall model fitting was appropriate and its structure fits the data well.

According to Table 5 and Fig. 1, the path coefficient of perceived social support on psychological capital (β = 0/574), job burnout (β=-0/115), and work engagement (β = 0/132) indicates a significant relationship. And also The path coefficient of psychological capital on job burnout (β=-0/503) and work engagement (β = 0/607) indicates a significant relationship in frontline nurses fighting against the Covid-19 pandemic working in the hospitals of Mazandaran University of Medical Sciences.

There was a significant positive relationship between perceived social support with work engagement and psychological capital and a significant negative relationship with job burnout. Our results are in line with other studies: Tian Ya Huo et al.(21), Han Xiao et al.(22), Cao et al.(23), Nasurdin et al.(24), Ashoori et al.(25)and Piri and Zeinali(26). Tian Ya Huo et al found that resilience plays a mediating role in the relationship between mental health and social support of medical staff. Also, Han Xiao et al reported in their study that the level of social support of healthcare workers was significantly related to sleep quality and self-efficacy and was negatively related to anxiety and stress levels. Cao et al. showed a direct and significant relationship between the components of perceived social support and the components of work engagement. Also, Nasurdin et al. in Malaysia showed that work engagement mediates the relationship between three forms of social support (support from the organization, supervisors, and peers) and nurses' job performance. Ashoori et al. and Piri and Zinali reported a significant negative relationship between perceived social support and nurses' job burnout.

We showed a significant positive relationship between psychological capital with work engagement and a significant negative relationship with job burnout, which is similar to other reports, such as de coning (27), Arefnejad, et al.(28), Jiaxi Peng(29), Karimi, Asgari, and Sharifian(30). In a study by De coning, it was shown that the relationships between psychological capital and work participation, work participation, and individual performance were statistically significant, and it was confirmed that work participation has an indirect effect on the relationship between psychological capital and individual performance. Arefnejad et al. reported that the rate of burnout caused by the stress of being infected with Corona was lower in people who have high psychological capital compared to nurses with low psychological capital. Also, Jiaxi Peng et al showed that psychological capital had a significant relationship with job burnout. Karimi, Asgari, and Sharifian reported a significant and inverse relationship between the resilience component of psychological capital and job burnout.

According to the findings of this research, during the time of accidents and crises, the nurses and other healthcare workers who are chosen to provide services to the people, should have high psychological capital and perceived support: because these people have less job burnout experience and it will also have a significant positive effect on their work engagement and finally the occupational health of the employees in the work environment will be maintained and with healthy and efficient forces we will achieve the comprehensive goal of providing quality services to the people. Therefore, knowledge of the internal and external factors affecting job burnout and work engagement can be used as a basis for decision-making and policy-making. Also, those in charge should have detailed and documented planning to continuously identify issues and problems that eventually cause job burnout and create professional inadequacies such as reducing work participation, and by adopting new and flexible policies and implementing supportive and psychological interventions, try to adjust and prevent the phenomenon of job burnout. Certainly, by having healthy and efficient forces, in the face of unpredictable and large crises such as the Corona pandemic, the health system can demonstrate high professional efficiency.

Acknowledgments

We thank all nurses who helped us to do this project.

Authors' contributions

A.R and M.F and F.A were contributed in conceptualization, writing original draft, writing review& editing, methodology and data analysis. M.V was contributed in methodology and data analysis and interpretation. Y.J was contributed in data collection. A.R and F.A were involved in all stages of this study. All authors have seen and approved the final, submitted version of this manuscript.

Funding

Not applicable.

Data availability

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

Ethics approval and consent to participate

The study protocol was approved by the ethic committee of the university of Social Welfare and Rehabilitation Sciences. The participants received required information about the study and informed written consent was collected from all participants prior to the start of the study as one of the criteria for them to join the study.

Consent for publication

Not applicable.

Competing Interest

The authors declare no competing interests.

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No competing interests reported.

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You are reading this latest preprint version

The Relationship between Perceived Social Support, Psychological Capital, Job Burnout, and Work Engagement in Nurses Working On The Front Line Of The Fight Against The Coronavirus

Status:

Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

INTRODUCTION

METHODOLOGY

Participants

Data Collection Instruments

Demographic Information Questionnaire

The Multidimensional Scale of Perceived Social Support (MSPSS)

Luthans' Psychological Capital Questionnaire (PCQ)

Maslach Burnout Inventory (MBI)

The Utrecht Work Engagement Scale (UWES)

Research Procedure

DATA ANALYSIS

RESULTS

Measurement Model

Structural Model

Overall model fit

DISCUSSION

CONCLUSION

Declarations

Acknowledgments

Authors' contributions

Funding

Data availability

References

Additional Declarations

Status:

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