INTRODUCTION:

COVID 19 is an epidemic that broke out in the city of Wuhan, China which leads to respiratory syndrome amongst those infected with the virus¹. It has caused an unprecedented impact on the healthcare workers around the world. It has led to, not only a substantial increase in their workload but has also instilled fear of contracting the infection themselves and thereby passing it amongst their family members, peers and common public². All of this has resulted in an increase in stress levels of the healthcare professionals^3,4. Also, several other factors such as work-life balance, disrupted sleep patterns, longer shifts and occupational hazards have added to the mental stress experienced by these healthcare professionals⁵. Researchers have also reported an increase in the number of suicide cases amongst these frontline workers^5,6.

As the effect of such an epidemic is immeasurable and very recent, the research in this area is limited and needs to be expanded. Now, such mental stress may become a cause of burnout among the healthcare professionals. In addition to the mental stress, physical stress caused due to the constant wearing of personal protective equipment (PPE), not having enough space to breath freely, frequent sanitization and long working hours accentuate the feeling of burnout¹. This psychological syndrome can be reduced or overcome through resilience, which is an individual’s ability to bounce back. Earlier studies have reported an inverse relationship between burnout and resilience, depicting it to be a factor that may lead to reduced burnout^7,8. Through this study, the researcher attempts to study the effect of stress on burnout with mediating role of resilience among the healthcare professionals in India.

LITERATURE REVIEW AND HYPOTHESES:

Stress:

Stress can be defined as “constantly changing cognitive and behavioral efforts to manage specific external and/or internal demands that are appraised as taxing or exceeding the resources of a person”.⁹ Psychological stress has increased during the COVID 19 outbreak among the healthcare workers^3,6. One of the studies reported perceived stress to be similar among physicians and nurses during the pandemic time¹⁰. There has been a significant increase in the feelings of fear, anxiety and depression among the medical staff who have been reported to be working in close proximity of the COVID 19 infected patients¹¹. Burnout has been reported to be an outcome of the perceived psychological stress, with a prominent effect during the pandemic time^2,12, while resilience has been reported to be negatively associated with stress and other mental illnesses such as trauma and adversity¹³.

H1 - There is a positive relationship between stress and burnout

H2 - There is an inverse relationship between stress and resilience

Burnout:

Extreme workload and exhaustion can lead to burnout among individuals⁸. Burnout can be defined as a “psychological syndrome that occurs in response to chronic job-related stress, with features involving emotional exhaustion, depersonalization, and a sense of reduced personal accomplishment”¹⁴. It is essential to measure the burnout among the healthcare workers as they are the driving force behind reducing the effect of COVID 19 on people¹⁵. Stress has been identified to be one of the significant predictors of burnout¹³ and a higher resilience is reported to give protection from burnout and is negatively related with burnout and psychological distress^16,17.

H3 - There is an inverse relationship between resilience and burnout

Resilience:

During a lifetime, there are many adversities that any individual has to go through, and the response to cope up with it is unique for each individual. This ability to cope up or bounce back is referred to as resilience¹⁸. In literature, the term resilience has been used in many contexts and many meanings have been associated with it. For this study, the author attempts to assess the resilience through its basic and original meaning, which is “ability to bounce or spring back”⁷. Resilience is known to have an inverse relationship with stress and burnout, implying a lower stress and burnout in highly resilient individuals. It works as a protective layer towards burnout and stress and aids in prevention or reduction of burnout among working professionals^17,8. Also, resilience has proved to play a significant mediating role in many of the previous studies and has reported to cause a partial mediating effect on the relationship between stress and burnout^19,20,13. This shows the ability of resilience to mitigate the negative effects of stress and burnout and thus has been studied as a mediating variable in this research.

H4 – The direct relationship between stress and burnout is mediated by resilience

COVID 19 Infection:

COVID 19 has instilled a fear in the minds of the people about the chance of contracting the infection. The fear is all the more prominent amongst the healthcare professionals with the further distress of passing it amongst their family members, peers and common public². So, the respondents of the study were asked to mention about their COVID 19 infection status and based on that the data was analyzed for identifying any significant difference in the stress, resilience and burnout of respondents based on them having already contracted COVID infection or not. It must be noted, that the answer to this question is based on the respondent’s awareness towards them testing positive for the COVID 19 infection and may not include those who did not get tested and were asymptomatic.

H5 – There is significant difference in stress based on COVID infection subgroups

H6 – There is a significant difference in resilience based on COVID infection subgroups

H7 - There is a significant difference in burnout based on COVID infection subgroups

METHODS:

Sample:

The data for the study was collected from healthcare professionals employed in three pharmaceutical companies working on development and production of COVID 19 vaccine in India. Convenience sampling method was used for gathering the data. Data was sought using a self-administered structured questionnaire that was developed using google forms and online link was shared with the potential participants. The google form invite was shared with 300 healthcare professionals. The response rate turned out to be 37.3%, thus, giving the researcher 112 responses which were complete in all respects and were used for analysis of this study.

Out of the 112 participants, 80.4% (90) were males and 19.6% (22) were females (Table 1). On the basis of age, 79.5% (89) were in the age group of 18-30 and 20.5% (23) reported to be in the age group of 31-59. None of the respondents were in the age group of 60 or above. 13.4% (15) of respondents reported to have been infected by COVID 19 while 86.6% of the respondents reported to not have been infected with COVID 19 yet.

Table 1 Sample characteristics

Variable	Group	n	%
Gender	Male	90	80.4
	Female	22	19.6
Age	18-30	89	79.5
	31-59	23	20.5
COVID Infection	Yes	15	13.4
COVID Infection	No	97	86.6

Measures:

Stress:

For measuring stress, the “Perceived Stress Scale (PSS)” was adapted²¹. Nine items were considered for this study, sample item being “During this COVID pandemic time, I feel as if something serious may happen unexpectedly”. The responses to the items were measured using 5-point Likert Scale where 5 = strongly agree and 1 = strongly disagree.

Resilience:

Resilience was measured using the “Brief Resilience Scale” consisting of 4 items⁷. It assesses an individual’s ability to bounce back from an adverse situation. Sample item was “I tend to bounce back from COVID stress at work”. The responses to the items were measured using 5-point Likert Scale where 5 = strongly agree and 1 = strongly disagree.

Burnout:

“Maslach Burnout Inventory (MBI)” single item scale was used for measuring burnout¹⁵. The single item was “How do you feel at work during the pandemic”. The responses to this item were measured using 5-point likert scale where 1 = “enjoy work”, 2 = “somewhat stressed, not burned out”, 3 = “definitely burning out”, 4 = “burnout symptoms won’t go away” and 5 = “completely burned out”.

Data Analysis:

The data was analyzed using Partial Least Square-Structural Equation Modeling (PLS-SEM) using the statistical software SmartPLS v3.3.3. A path model was developed which consisted of 3 latent variables – stress, resilience and burnout. Stress was studied as an independent or the predictor variable, resilience was studied as a mediating variable and burnout as the outcome or dependent variable. Measurement model assessment was carried out to test the reliability and validity of the model and structural model assessment was performed for gauging the path coefficients through bootstrapping procedure, mediation analysis and coefficient of determination (R²) and predictive relevance through blindfolding procedure. The sample characteristics, descriptive statistics, correlation and independent sample t-test for testing difference in stress, resilience and burnout based on COVID 19 infection subgroups were conducted using IBM SPPSS 25.

RESULTS:

Descriptive Statistics and Correlation:

The mean, standard deviation and correlation among stress, resilience and burnout are presented in Table 2. It can be observed that respondents ‘agree’ that they are experiencing stress. They also ‘agree’ on having ability to bounce back and are coping up with the current pandemic situation. For burnout, it is good to report that on an average, respondents feel ‘somewhat stressed, not burned out’. The possible reason for the same is their dedication towards fulfilling their duty and their high resilience perception. The correlation among all the three variables turns out to be significant at 0.01 level, with a negative correlation between stress-resilience and burnout-resilience. Also, the highest correlation (negative) comes out to be between resilience and burnout. Implying, that higher the resilience, lower is the burnout, which is also evident from the mean values of these study variables.

Table 2 Descriptive statistics and correlation

	Variable	Mean	Std. Deviation	1	2	3
1	Stress	4.20	0.48	1
2	Resilience	4.09	0.59	-0.379**	1
3	Burnout	2.14	1.03	0.604**	-0.656**	1

** Correlation is significant at 0.01 level (2-tailed)

Independent Sample t-test:

The study variables were explored for any significant difference based on the respondents who had already tested positive for COVID 19 infection versus those who had not. Independent sample t-test was used for analyzing any significant difference in the stress levels, burnout and resilience of respondents. The hypothesis H6 was accepted as sig<0.01, while H5 and H7 were rejected as sig>0.05. From this, it could be inferred that there was a significant difference in resilience of respondents based on the COVID 19 infection subgroup while no significant difference was observed in stress and burnout level of respondents, based on COVID 19 infection subgroups.

Measurement Model Assessment:

Composite Reliability and Convergent Validity

The reliability and convergent validity of the path model was tested using measurement model assessment in Smart-PLS. For composite reliability (CR), the values need to be above the cut off limit of 0.708. It can be seen in Table 3 that the CR values of all the constructs are greater than 0.708, thus, confirming the scale to be reliable for the study. The convergent validity of the model was established by analyzing the outer loading values of each item, their corresponding t-values and average variance extracted (AVE) values of each construct. The outer loadings of the items under stress construct were within the acceptable range (Table 3) except for items S5 and S7 for which outer loadings came out to be -0.14 and -0.39 respectively. Hence, the items were deleted. For resilience scale, the outer loadings were in acceptable range for three items (Table 3). For R3 item, the outer loading came out to be 0.16, so it was deleted. Lastly, the AVE value for all the constructs was above 0.5, thus confirming the convergent validity of the scale²².

Table 3 Composite Reliability and Convergent Validity

Construct	Items	Outer Loadings	t-values	Composite Reliability	AVE
Stress				0.86	0.58
	S1	0.69	14.60
	S2	0.79	17.81
	S3	0.75	17.45
	S4	0.82	25.97
	S6	0.55	5.99
	S8	0.54	7.70
	S9	0.73	15.62

Resilience				0.73	0.547
	R1	0.46	1.98
	R2	0.91	55.81
	R4	0.86	30.65
Burnout	B1	1.00	-	1.00	1.00

Discriminant Validity:

The discriminant validity tests the uniqueness of the construct, which is confirmed if the diagonal values are greater than the other values of that column²². It can be observed in table 4 that the diagonal values are greater than their counterparts in that column, thus confirming the discriminant validity of the scale.

Table 4 Discriminant Validity – Fornell Larcker Criterion

	1	2	3
1. Stress	0.69
2. Resilience	-0.67	0.74
3. Burnout	0.64	-0.71	1

Structural Model Assessment:

The structural model assessment in PLS-SEM was conducted to assess the predictive capability of the model through various criterions such as path model coefficients through bootstrapping, mediation testing, coefficient of determination (R²) and predictive relevance (Q²) using blindfolding procedure.

Path Model Coefficients:

The hypotheses were tested by analyzing the path model coefficients and the corresponding p-values by running the bootstrapping procedure for 5000 samples. Hypothesis H1, H2 and H3 are accepted as the sig<0.01 for the following paths – Stress à Burnout, Stress à Resilience and Resilience --> Burnout. Further, it can be observed (Fig. 1) that stress and burnout have a significant positive relationship between themselves with the path coefficient value of 0.64, while stressàresilience and resilience à burnout report a significant inverse relationship, with their path coefficient values being -0.77 and -0.76 respectively.

Table 5 Path Model Coefficients with p-values

Hypothesis	Path	Path Coefficient	p-value
H1	Stress --> Burnout	0.64	0.04
H2	Stress --> Resilience	-0.77	0.00
H3	Resilience --> Burnout	-0.76	0.00

Fig. 1 – Path Model Coefficients

Mediation Analysis:

The path model developed for the study (Fig. 1) observed resilience as a mediator between stress and burnout, through the hypothesis H4. The impact of resilience as a mediator was analyzed using the mediation analysis. It can be seen in table 6 that resilience is able to fully mediate the relationship between stress and burnout with a mediating effect of 91%, thus confirming the H4 hypothesis. Also, the direct effect of stress on burnout is reduced to 0.052 with the inclusion of resilience as a mediator.

Table 6 Mediating Analysis

Mediation Analysis: Resilience as a mediator
Exogenous variable	Direct Effect	Indirect effect	Total Effect	VAF Level	Mediation Level
Stress	0.052	0.59	0.64	0.91	Full

Thus, it can be inferred that resilience acts as a very strong mediator in the relationship between stress and burnout and causes a full mediation making the model very effective and robust²².

The author also studied the same model with the single item statement of burnout as “How did you feel at work before the pandemic?”. It was observed that resilience no longer acted as a significant mediator, thus highlighting the importance of resilience as a mediator between stress and burnout among the respondents during the time of pandemic.

Coefficient of Determination (R²) and Predictive

Relevance (Q²):

The effect of stress and resilience (exogenous constructs) on burnout and of stress on resilience was assessed through the coefficient of determination (R²) values. It was observed (table 7) that stress and resilience have a significant predictive capability of 0.646 on burnout, implying that they are able to explain 64.6% of variance in burnout. Similarly, stress was found to be able to explain 59.3% variance in resilience.

Predictive relevance (Q²) of the model was also tested using the blindfolding procedure with an omission distance of 9. It can be seen in table 7 that Q² values for burnout and resilience turned out to be larger than zero²², thus inferring that stress and resilience display a good predictive relevance for burnout and stress displays a strong predictive relevance for resilience.

Table 7 R² and Q²

Endogenous Variable	R²	Q²
Burnout	0.646	0.439
Resilience	0.593	0.311

DISCUSSION:

This study examined the mediating role of resilience on the relationship between perceived stress and burnout for the healthcare professionals working on COVID 19 vaccine development and production in India. For this, a self-administered questionnaire was shared with the participants of the study and data was analyzed for 112 complete responses received. Descriptive statistics revealed that highest agreement level turned out to be for stress, indicating respondent’s perceived stress levels to be high. On the other hand, respondents also exhibited a similar agreement level for resilience, which has been helping them in coping up with the pandemic stress at work resulting in a lower burnout level. All the three variables were reported to have a significant correlation amongst themselves, with the most significant and negative correlation between resilience and burnout, inferring that higher the resilience among the respondents, lower was their reported burnout level. Similar results were described in other studies, which observed a negative association between resilience and stress and also confirmed burnout to be an outcome of perceived psychological stress^2,12,13.

A significant difference was observed in resilience of the respondents who had already contracted the COVID 19 infection versus those who had not reportedly been tested positive yet. Path model analysis confirmed a strong inverse relationship between stressà resilience and resilience àburnout. Further, resilience exhibited full mediation between stress and burnout. Similar results were reported by other researchers as well who emphasized on the importance of resilience as a protective layer between stress and burnout leading to a reduction in burnout^{19,17,20,8,13}. It is interesting to note that when author studied the same model for assessing the burnout level before the pandemic, the resilience was observed to be insignificant in mediating the relationship between stress and burnout. In terms of predictive capability, stress and resilience were found to be able to explain 64.6% of variance in burnout. Similarly, stress was found to be able to explain 59.3% variance in resilience. Overall, stress and resilience displayed a good predictive relevance for burnout, demonstrating the path model to be robust.

Limitations and Scope for Future Research:

The study was restricted in terms of diverse demographic data collection and respondents were from a specific dimension only. Considering paucity of time at respondent’s end, a single item scale was used for assessing the burnout of respondents. The results may vary for other frontline workers such as nurses, police, doctors and lab technicians. The study can be further expanded by including more study variables such as anxiety, depression and other such factors. A pre and post COVID comparative analysis can be done to understand the impact of COVID 19 more profoundly. Other demographic characteristics such as marital status, education, socio-economic status, nationality can be studied to capture new insights.

CONCLUSION:

COVID 19 has been one of the most life-threatening and widespread pandemic till date resulting in unprecedented times for the world. The most affected have been the healthcare workers across the globe. This study attempted to understand the influence of such pandemic on the stress and burnout experienced by these frontline workers who have been working at war footing to curb and find a solution to this situation. A path model was developed to assess the effect of stress on burnout with the mediating role of resilience. The results of the study revealed a positive relationship between perceived stress and burnout with a strong mediating effect of resilience among the respondents during the time of pandemic. This indicated the intention of the healthcare workers to work resiliently and dedicatedly to serve the people leading to a reportedly high coping up capability resulting in a lower burnout.

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Received on 11.09.2021 Modified on 22.11.2021

Asian Journal of Management. 2022;13(1):35-40.

DOI: 10.52711/2321-5763.2022.00007