Open access peer-reviewed chapter - ONLINE FIRST
Descriptive statistics of burnout and resilience.
Abstract
Burnout is one of the phenomena that occurs in situations of increased mental and emotional exhaustion at work, so it is expected that healthcare professionals will experience it more often in a situation of increased stress amid the outbreak of COVID-19. The aim of this national cross-sectional survey study was to examine the levels of burnout and the relationship between burnout syndrome, healthcare workers’ resilience, and different sociodemographic and work-related variables during the outbreak of COVID-19 in Serbia. The sample included 398 healthcare professionals who actively worked with patients during the COVID-19 outbreak. The primary contribution of our study was that we identified moderate to high levels of burnout among healthcare professionals (doctors, nurses, psychotherapists, and pharmacists) in Serbia during the COVID-19 outbreak, but also significant negative correlations between these levels of burnout and resilience, as well as statistically significant factors related to burnout such as the number of working hours per day, age, length of service, and profession.
Keywords
- COVID-19 outbreak
- burnout
- healthcare professionals
- resilience
- Serbia
1. Introduction
The first cases of the coronavirus disease 2019 (COVID-19) were described in Wuhan, the capital of Hubei, China, in December 2019. Soon after, the number of cases increased dramatically, spreading across China and worldwide [1]. The WHO Emergency Committee declared a global health emergency on January 30th, 2020 [2]. Another country impacted was Serbia, where a state of emergency was announced on March 15, 2020, enacting some of the toughest anti-pandemic measures in Europe. The first verified case was reported on March 6, 2020 [3]. In the midst of the COVID-19 pandemic, healthcare workers in all countries of the world, including Serbia, have increased workload and protection measures, care for their own and patients’ health, strict protocols in the treatment of COVID patients, reorganization of previous work models, and implemented prevention measures. All this undoubtedly affected not only physical but also mental and emotional exhaustion and other difficulties at work, which can further seriously endanger the mental health of healthcare professionals [4, 5, 6, 7]. Healthcare personnel are predicted to suffer burnout more frequently in a setting of heightened stress due to COVID-19 because it is one of the phenomena that occurs in situations of greater mental and emotional weariness at work [8].
As a syndrome caused by ongoing stress at work that has not been effectively handled, burnout is classified as an occupational phenomenon by the International Classification of Diseases (ICD-11). It has three characteristics that define it: experiencing reduced professional efficiency, feeling worn out or depleted of energy, and growing mental detachment from one’s work [9]. Burnout studies among healthcare professionals increased during the COVID-19 epidemic. A multitude of healthcare professionals may be negatively impacted by burnout, depending on a number of aspects, including the professionals’ occupation while dealing with patients (nurses, physicians, and other allied health workers). Also, in addition to healthcare providers, burnout has negative consequences for patients [10, 11, 12]. Healthcare workers have more work to do during a pandemic, which further increases the risk of SARS-CoV-2 infection, accounting for up to 11% of cases in some countries [13], and the risk of self-infection may be another factor that can lead to burnout [14]. Depending on psychological resources, such as resilience and personality traits, there are notable individual variances in how each person responds to a stressful, event such as a pandemic.
The capacity of a person to resume regular mental functioning following traumatic or dangerous experiences without suffering long-term harm is known as resilience [15]. Resilience research in relation to the healthcare worker population is less frequent than burnout research. High resilience, which may be attained through the right medical training program, was mentioned even before the global COVID-19 epidemic as an ability that helps healthcare workers to quickly recover from a variety of challenges at work [16, 17].
Studies suggest that burnout among healthcare workers increased during the COVID-19 pandemic compared to the time before the pandemic [18, 19]. Also, the findings of research conducted around the world before and during the COVID-19 pandemic suggest that in healthcare workers, resilience and burnout are interrelated phenomena [20], so greater resilience implies less burnout, as well as greater burnout, implies weaker resilience [20, 21, 22, 23, 24]. Taking all of this into account, the aim of this study was to examine the levels and correlations of burnout and resilience in healthcare workers who actively worked with patients during the COVID-19 pandemic in Serbia, as well as the effect of demographic and work-related variables (such as gender, length of service, number of hours spent per day at work, profession, and type of institution in which they work) on the level of burnout and resilience. The following hypotheses were set: (1) Significantly higher levels of burnout will be observed among healthcare workers, (2) Resilience as a personality trait will be negatively correlated with burnout, and (3) Demographic variables, as well as work-related variables (number of working hours per day and days per week as well as profession), will have an effect on burnout and resilience.
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2. Methods
2.1 Sample and procedures
A cross-sectional correlational study design was used in this study. Inclusion criteria for the study sample were residents of Serbia being in the health profession (physicians, medical technicians or nurses, pharmacists, and psychotherapists). Exclusion criteria were residents of other countries, members of any profession outside of healthcare occupations, as well as healthcare workers who did not work actively with people during the COVID-19 pandemic in Serbia.
To minimize social interactions, as recommended by the government of Serbia, data were collected online using the Google Forms platform during April 2020 in Serbia. We used a snowball sampling method by sharing the questionnaire link on Facebook and Viber medical community groups (healthcare workers, pharmacists in practice, psychotherapists, nurses, and medical technicians). Potential participants were given an explanation of the research’s purpose in Serbian at the outset of the anonymous online survey. The respondents were assured of the confidentiality and anonymity of the data collected, and participation in the study was voluntary and done so with informed consent. The study team was the only one with access to the safeguarded data. The statistical analysis did not include survey replies that were improper or duplicated.
The Institutional Review Board (approval number: #2020–2030) of the Department of Psychology, Faculty of Philosophy, University of Belgrade, gave its approval for the study. This study’s protocols complied with the Declaration of Helsinki’s guidelines [25].
2.2 Measures
The first part of the online questionnaire included a brief study description and an invitation, demographic data such as gender, age, and country of residence of the respondents, and data related to work engagement during the pandemic such as profession, active work with people during the COVID-19 pandemic, type of institution where they work, length of service, number of hours per day at work, and number of days per week at work.
The second part of our questionnaire included questions related to resilience, defined as the ability to recover from stressful or threatening events. Respondents completed the brief resilience scale (BRS) [15], which is one-dimensional and consists of six items (e.g., “I tend to bounce back quickly after hard times.”) Reverse scoring is present on three items (e.g., “I have trouble getting through stressful events.”) From 1 (strongly disagree) to 5 (strongly agree), respondents could select one response on a five-point Likert-type scale. The mean of the six items on this scale is the overall score. According to the original authors, this scale has very good reliability; the Cronbach’s alpha coefficient was above 0.8 in previous research [15].
The third part of our questionnaire was used to assess healthcare workers’ burnout and included two scales: The Oldenburg Burnout Inventory (OLBI) [26] and the single-item burnout measure (SIBM) [27]. The Oldenburg Burnout Inventory [26] has 16 items divided into two scales: disengagement (e.g, “I talk about my work negatively more and more often”) and exhaustion (e.g, “I often feel emotionally drained during my work.”) Respondents had the option to choose one answer on a four-point Likert-type scale from one (strongly agree) up to four (strongly disagree). Demerouti et al. [26] define the basic dimensions of burnout somewhat differently compared to earlier conceptualizations. Exhaustion is defined as the result of intense physical, affective, and cognitive stress, while disengagement refers to one’s own distance from work in general [26]. Therefore, only the exhaustion scale was used for the purposes of this research. Cronbach’s alpha on this scale, according to the original authors, is 0.78 [26]. Another tool employed in this study to assess burnout was a single-item burnout scale. It asked participants to explain burnout in their own words and states, “Overall, based on your definition of burnout, how would you rate your level of burnout”? The responses are evaluated using an ordinal five-category scale, with one indicating enjoyment at work, for example, “I am not experiencing any signs of burnout.”; 3 = “I am experiencing signs of burnout, including emotional and physical tiredness.”; and 5 = “I am so exhausted that I frequently question whether I can continue. I am at the point where I might need to make certain changes or look for assistance” [27].
2.3 Statistical analysis
Statistical analysis of the gathered data was performed using SPSS statistics software (IBM SPSS Statistics for Windows, Version 22.0, Armonk, NY, USA). Descriptive statistics were measured using means, standard deviations, skewness, kurtosis, and minimum and maximum values. Using Cronbach’s alpha coefficient as a measure of internal consistency, the validity of the measures was examined. From statistical analyses, descriptive statistics were conducted to examine levels of burnout and resilience, Pearson’s correlation was used to examine the relationship between these constructs on a sample of healthcare workers, and in order to compare groups, the t-test and ANOVA were used. The statistically significant level of probability was p < 0.05.
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3. Results
3.1 Participant characteristics
The sample consisted of 398 healthcare workers, which we classified into: medical personnel with two categories — physicians and medical technicians/nurses and allied health professionals with two categories — pharmacists and psychotherapists. The age of the respondents ranged from 19 to 62 years; the average age of the sample was [mean (M) ± standard deviation (SD)] 39.12 ± 9.76 years. There were 267 (67.1%) female respondents and 131 (32.9%) male respondents. Out of a total of 398 respondents, 119 (29.9%) were physicians, 154 (38.7%) were medical technicians and nurses, 56 (14.1%) were pharmacists, and 69 (17.3%) were psychotherapists.
Table 1 shows scale reliability and descriptive statistical metrics. The α coefficient of internal consistency, also known as Cronbach’s alpha, indicated that all of the instruments utilized in this study had high reliability, as was anticipated.
| Scale | Min | Max | M | SD | Skew | Kurt | ||
|---|---|---|---|---|---|---|---|---|
| Burnout | OLBI | 1 | 4 | 2.78 | 0.64 | −0.37* | −0.31* | 0.85 |
| SIBM | 1 | 5 | 2.81 | 1.09 | 0.58 | −0.46 | / | |
| Resilience | 1 | 5 | 3.02 | 0.86 | −0.16* | −0.45* | 0.83 | |
Table 1.
<1.96.
SD – standard deviation;
It is important to note that the single-item burnout measure (SIBM) has only one question and does not have a Cronbach’s alpha. The other two instruments show good or very good reliability. Thus, the exhaustion scale from the Oldenburg Burnout Inventory (OLBI) obtained in this study (0.85) exceeds that obtained by the original authors in their study (0.78) [26]. This certainly speaks in favor of the reliability of this scale within this inventory. The average number of respondents on the exhaustion scale is 2.78, which indicates that their degree of burnout is significantly higher compared to the normative data of the OLBI, which is 2.47 [26]. Furthermore, on the second measure of burnout, the single-item burnout measure (SIBM), the largest number of respondents (41.2%) stated that they had certain symptoms (occasional stress and lack of energy) but not burnout. In addition, only 6.5% of them have no symptoms of burnout and enjoy their job. The rest of the respondents (52.3%) consider themselves to have burned out at work with different levels of symptoms (from physical and emotional exhaustion to frequent thoughts of frustration at work and the need to seek help).
When it comes to resilience, the reliability of the scale fits within the range determined by the author of this scale (0.80 to 0.91) [15]. According to the same author, averages starting with the numbers one and two are indicative of low resilience, and averages starting with four and five are high. Therefore, the sample of this study can be described as normally resilient, although the normative data for BRS is 3.53 [15].
Furthermore, based on the values of skewness and kurtosis and their standard errors, conclusions can be drawn about the normal distribution on the Oldenburg Burnout Inventory (OLBI) and the brief resilience scale (BRS). The estimated value of the 97.5 percentile point of the standard normal distribution in probability and statistics is 1.96. Because of the central limit theorem, 95% of the area under a normal curve comes within approximately 1.96 standard deviations of the mean. As a result, approximate 95% confidence intervals are constructed using this value [28].
Table 2 shows that medical technicians, nurses, and physicians spend the most time at work on a daily basis in terms of hours, while on a weekly basis, in terms of number of days, the highest engagement is among medical technicians and nurses.
| Profession | Number of work hours per day | Number of work days per week | ||||
|---|---|---|---|---|---|---|
| N | M | SD | N | M | SD | |
| Physicians | 119 | 8.13 | 8.18 | 119 | 5.56 | 4.04 |
| Medical technicians/nurses | 154 | 8.27 | 3.99 | 154 | 6.19 | 6.96 |
| Pharmacists | 56 | 7.68 | 4.68 | 56 | 5.18 | 2.69 |
| Psychotherapists | 69 | 6.75 | 2.70 | 69 | 5.17 | 2.68 |
Table 2.
Descriptive statistics for the number of hours per day and days per week that healthcare workers spend at work during a pandemic.
Table 3 shows the correlations between two burnout measures and resilience. It can be seen that all three measures are correlated and that all correlations are significant at the level of p < 0.01. The two burnout measures, the OLBI and the SIBM, are significantly correlated (r = 0.712, p < 0.01), which indicates a high agreement in the results of these measures. The correlation of burnout, as measured by both scales, with resilience (r = −0.563, p < 0.01 and r = −0.465, p < 0.01) is significantly negative, which suggests that resilience is an important factor in burnout prevention.
Table 3.
Correlation between burnout and resilience.
p < 0.01.
In order to determine whether there are differences between males and females according to the burnout and resilience variables, the independent samples t-test was performed. Significant gender differences were found only on the resilience variable, where men show significantly higher levels, as seen in Table 4.
| t | Df | Sig. (2-tailed) | Gender | Mean | ||
|---|---|---|---|---|---|---|
| Burnout | OLBI | −1.378 | 396 | 0.169 | Male | 2.72 |
| Female | 2.81 | |||||
| SIBM | −0.549 | 396 | 0.583 | Male | 2.76 | |
| Female | 2.83 | |||||
| Resilience | −2.598 | 396 | 0.013* | Male | 3.44 | |
| Female | 2.59 | |||||
Table 4.
Gender differences in burnout and resilience.
p < 0.05.
Using the ANOVA analysis, significant differences were obtained between the groups of employed healthcare workers (physicians, nurses/technicians, pharmacists, and psychotherapists) according to the levels of burnout and resilience, which can be seen in Table 5.
| Source of variation | SS | Df | MS | F | p | ||
|---|---|---|---|---|---|---|---|
| Burnout | OLBI | Between groups | 38.14 | 3 | 12.38 | 38.48 | 0.000* |
| Within groups | 93.03 | 394 | 0.39 | ||||
| Total | 131.17 | 397 | |||||
| SIBM | Between groups | 31.81 | 3 | 10.27 | 10.05 | 0.000* | |
| Within groups | 283.29 | 394 | 0.20 | ||||
| Total | 314.10 | 397 | |||||
| Resilience | Between groups | 23.27 | 3 | 7.42 | 11.91 | 0.000* | |
| Within groups | 174.25 | 394 | 0.74 | ||||
| Total | 196.52 | 397 | |||||
Table 5.
Analysis of variance of burnout and resilience according to profession.
p < 0.01.
Table 5 shows that there are differences between groups of respondents (physicians, nurses/technicians, pharmacists, and psychotherapists) in terms of burnout levels and resilience. Conducting post hoc analysis showed that psychotherapists burned significantly less than other groups, in relation to which they are also significantly more resilient.
Most of the respondents (118, or 29.6%) work in hospitals, followed by health centers (132, or 33.2%), private practice (55, or 13.8%), pharmacy (53, or 13.3%), and counseling (40, or 10.1%). An analysis of variance, shown in Table 6, showed that there are significant differences between people working in different institutions in terms of levels of burnout and resilience.
| Source of variation | SS | df | MS | F | p | ||
|---|---|---|---|---|---|---|---|
| Burnout | OLBI | Between groups | 26.89 | 4 | 5.47 | 20.17 | 0.000* |
| Within groups | 79.27 | 393 | 0.40 | ||||
| Total | 106.17 | 397 | |||||
| SIBM | Between groups | 16.53 | 4 | 3.13 | 3.51 | 0.001* | |
| Within groups | 233.57 | 393 | 0.91 | ||||
| Total | 258.10 | 397 | |||||
| Resilience | Between groups | 19.85 | 4 | 5.21 | 8.63 | 0.000* | |
| Within groups | 153.66 | 393 | 0.74 | ||||
| Total | 151.52 | 397 | |||||
Table 6.
Analysis of variance of burnout and resilience according to the institution in which the respondents work.
p < 0.01.
Significantly lower levels of burnout, as measured by OLBI, were shown by those working in private practice compared to those working in hospitals, health centers, and pharmacies. In addition, employees in counseling centers achieve significantly better results on the same test than those who work in hospitals. Significantly higher burnout, as measured by the SIBM, is present in hospital staff compared to employees in private practice. Finally, this analysis showed that those working in private practice have higher levels of resilience than employees in health centers and hospitals.
A correlation analysis of the relationship between burnout and resilience on the one hand, and the age of the respondents and their length of service on the other hand obtained the data that can be seen in Table 7.
Table 7.
Correlations of burnout and resilience with the age of the respondents and the length of their service.
p < 0.01.
From Table 7, it can be seen that age is related only to the score on the SIBM (r = 0.182, p < 0.01), but that this correlation is weak. This means that as the years go by, healthcare workers estimate their burnout to be higher, or that the pandemic has hit those older among them harder. Furthermore, both burnout measures are correlated to the length of service of healthcare workers (r = 0.148, p < 0.01; and r = 0.214, p < 0.01). The correlations are low in this case as well, suggesting that burnout is slightly higher in those who have more work experience. Finally, the data related to the amount of employment of healthcare workers during one working day and one working week with burnout, were correlated. The results of these analyses can be found in Table 8.
| Burnout | Resilience | ||
|---|---|---|---|
| OLBI | SIBM | BRS | |
| Number of work hours per day | 0.282* | 0.058 | −0.128* |
| Number of work days per week | 0.042 | 0.066 | 0.039 |
Table 8.
Correlations between burnout and resilience with the number of hours per day and days per week spent by healthcare workers at work during a pandemic.
p < 0.05.
Table 8 shows that there are no correlations between burnout and resilience and the number of days spent at work. On the other hand, along with greater work engagement in the number of hours, burnout measured by OLBI (r = 0.282, p < 0.01) is also higher, which indicates that the more hours the respondent works in 1 day, the stronger the symptoms of exhaustion as an aspect of burnout syndrome. In addition, a negative correlation was observed between the number of hours spent per day at work and resilience (r = −0.128, p < 0.05). This finding suggests that people who spend more time at work have a weaker capacity for resilience and the ability to bounce back.
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4. Discussion
Since the beginning of the spread of the coronavirus, healthcare workers have been among the most affected by the fact that they are exposed to potential infections and excessive workload on a daily basis. A review of the literature shows that healthcare professionals face challenges at work that can affect their health, emotional well-being, and efficiency at work [29]. This study examines the associations between psychological variables of resilience and burnout in light of the current COVID-19 pandemic among healthcare professionals: physicians, medical technicians/nurses, pharmacists, and psychotherapists, who actively worked with patients during April 2020, providing them with care, assistance, and support. Respondents completed four questionnaires: the OLBI, the SIBM, the BRS, and a questionnaire designed for this research to collect demographic and work-related data. The scores from these tests were later correlated and compared with the aim of confirming or disproving hypotheses.
The first hypothesis, which concerns significant levels of burnout among healthcare workers in Serbia during the COVID-19 pandemic, was confirmed by the results of the OLBI, while the single-item burnout measure showed mixed results. According to OLBI, the burnout of this population is significantly higher compared to data obtained before the pandemic and in other studies [26], while a moderate finding was obtained on SIBM. Assumptions about the existence of higher levels of burnout among health workers who actively worked with people during the pandemic have been confirmed. It is known that healthcare workers experience higher levels of stress at work than the general population, and this stress has physical and psychological consequences [30]. In a study from Switzerland in which doctors and nurses participated, similar to this study, significant levels of burnout symptoms were obtained in both groups. However, it has been noted that women and nurses are more affected than men and doctors [31], which does not coincide with the findings of this study. In a study conducted in Spain, one of the most severely affected European countries [32], at the beginning of the pandemic, no gender differences were observed on the scale of emotional exhaustion as an aspect of burnout syndrome, similar to the findings of this study. Moderate (23.1%) and severely elevated (41%) exhaustion levels were achieved by 64.1% of respondents [33].
A systematic review and meta-analysis of burnout among healthcare workers during the COVID-19 pandemic shows that burnout among healthcare workers is exacerbated by the unique characteristics of the COVID-19 pandemic. The goal of the study was to provide an extensive overview of the prevalence of burnout and its dimensions across various healthcare workers during the COVID-19 pandemic. It found that 52% of health workers had burnout, with nurses and/or doctors having the highest rate (66%), which is higher than the rates found in previous studies carried out over the previous 20 years [12]. In Italy, which was one of the most affected countries in Europe during the COVID-19 pandemic [34], high rates of exhaustion [18] were recorded in the population of healthcare workers, which is in line with this study. Higher levels of burnout compared to the periods before the pandemic were also noted by authors from Romania, so in their sample of doctors of different specializations, as many as 76% of respondents had some symptoms of burnout [35]. Research findings from Puerto Rico are in line with the moderate levels of burnout obtained in a domestic sample. In addition, nurses on average show the most severe burnout syndromes [36], which is not in line with the differences between the professions noted in this study (where only psychotherapists differ significantly from the other three groups of health professionals). All of the above indicates that burnout is becoming a “global phenomenon” that can negatively affect those providing health care [37].
The second hypothesis has been partially confirmed. Resilience has proven to be an important factor in prevention, and the strength of this effect is stronger than expected. The only group of respondents who deviated significantly were psychotherapists, who were significantly less burned and significantly more resilient. Some authors call resilience a good preventive measure in relation to burnout [38], which can be confirmed from the analysis of the results of this research, given that a significant negative correlation was found between burnout levels measured by two measures and resilience. Our finding is consistent with the results of a domestic study that also found a negative association between resilience and burnout in medical staff [20], as well as a study conducted in the United States where a negative correlation between burnout and resilience was also observed. Namely, higher resilience was found in subjects who did not show the presence of burnout. However, 29% of physicians with the highest resilience have burnout, indicating that measures should be taken to preserve their mental health [39].
The third hypothesis is only partially confirmed, as gender differences in resilience have been identified as well as a significant negative correlation between the number of hours spent at work and resilience, while the number of days per week at work does not affect the level of resilience. As also assumed, sociodemographic variables have mixed correlations with both burnout and resilience, and most relationships are not statistically significant. This coincides with the assumption that sociodemographic factors will have a variable impact on the levels of these two psychological constructs. Those that are, such as the lower resilience of female respondents and higher levels of burnout in the elderly, more experienced, and those who work more hours a day, can more precisely target psychological help efforts at those people who fit those profiles.
Healthcare workers from Spain, doctors and nurses, showed moderate levels of resilience. The correlation between burnout and resilience was significantly negative [33], as noted in this study. A negative correlation between resilience and exhaustion, both physical and mental, was obtained in a sample of healthcare workers in China during the COVID-19 pandemic [40]. Similar to an earlier study from the United Kingdom [41], the level of resilience among health professionals in Serbia is moderate. The same authors did not show a difference in resilience by gender, age, or length of service, which is in line with the data of this study when it comes to age and length of service but not by gender. Furthermore, in one Chinese study, data were obtained indicating lower resilience in female medical staff [42], which agrees with the findings of this study, where females had significantly lower resilience scores. Also, another Chinese study found, similar to this one, a negative correlation between resilience and burnout [43].
The strength of our study could be the timing of data collection related to the period of the first wave of the COVID-19 outbreak in Serbia. It is important to consider the limitations of this study when assessing the findings. The cross-sectional study methodology is the primary limitation because it is difficult to establish causal links between the variables in the study. A long-term study is required to validate our findings in order to address this issue and acquire a deeper understanding of causal relationships. Self-reporting bias may exist because the data was gathered
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5. Conclusions
The primary contribution of our study was to identify moderate to high levels of burnout present in healthcare workers in Serbia during the COVID-19 pandemic, as well as significant negative correlations between these burnout levels and resilience. The relationship in which the greater the resilience of individuals, the less their burnout, proved to be stronger than expected, which consequently increases the implications for the practice of this finding. The creation and implementation of preventive measures to encourage greater resilience among healthcare workers [44], especially doctors, nurses, and pharmacists, may prove very useful in preventing burnout and maintaining mental health during a pandemic.
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Acknowledgments
The authors would like to express their deepest gratitude to all those who, with their misdeeds, encouraged them to work even harder and achieve scientific success.
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