1. Background
Coronavirus disease 2019 (COVID-19) is a new type of coronavirus that led to a health crisis at the end of 2019 (1). By mid-2023, about seven million individuals have died due to this virus (2). The increasing mortality resulting from the spread of the viral disease and its consequences have affected the mental health of individuals and led to numerous psychological responses (3). Healthcare workers suffer more psychological burdens due to direct and continuous contact with patients, compared to the general population (4). Reports indicate that in the early stage of the outbreak of COVID-19, 29% of all hospitalized patients were healthcare workers. Additionally, healthcare workers are exposed to a significant amount of fake news and rumors, all of which increase their anxiety or other negative psychological responses (5, 6). Undiagnosed or untreated psychological problems can have long-term effects on their health and reduce the quality of healthcare services for patients (7).
In recent years, due to several problems, such as the lack of medical facilities and increased workload, burnout has increased among healthcare workers (8). Burnout is a psychological syndrome characterized by decreased energy, increased mental distance from work, and decreased professional efficiency (9). Emergency situations, such as epidemics and pandemics, can easily cause exhaustion (10). Current studies confirm the high prevalence of burnout among nurses and other healthcare workers during the outbreak of COVID-19 (11). With the increase in the level of burnout among them, the prevalence of related symptoms and/or disorders has been reported (12). In the midst of the recent pandemic, nurses played a critical role in various wards and units related to COVID-19 (13). Nurse burnout is important because, more than any other factor, it can threaten patients' improvement process (14).
Occupational burnout might be associated with some negative affective responses, such as depression, anxiety, and stress. Interestingly, there is a remarkable similarity or overlap between depression and burnout (15). Some symptoms of burnout, such as loss of interest or pleasure, fatigue, and decreased energy, are similar to symptoms of depression (16). The literature indicates that 58% of individuals with occupational burnout also suffer from a mood disorder (17). Another factor that has not been studied as much as depression is anxiety (18). According to previous studies, 59% of individuals with occupational burnout also have an anxiety disorder, although Teo et al. indicated that the level of anxiety did not show a significant increase (17, 19). In addition, the nursing profession is considered one of the most stressful jobs (13). Acute stress can severely affect the mental health of nurses who are responsible for the continuous and sometimes unprotected care of patients (6). Sarboozi et al. observed that the level of occupational stress of nurses in COVID-19 wards increased significantly, compared to nurses in other wards, and this stress was related directly to the level of occupational burnout (20).
Altogether, although studies confirm the increase in occupational burnout among Iranian nurses during the pandemic (21), to the best of our knowledge, only one study in southern Iran has investigated the relationship between affective responses and occupational burnout. The results of the aforementioned study showed that burnout did not change significantly, compared to the previous situation of it (22).
2. Objectives
This study focused on the mental health of nurses during the COVID-19 pandemic. It investigates the relationship between occupational burnout and depression, anxiety, and stress in nurses working at a major COVID-19 referral center in Guilan province, Iran. The findings of the study can be applied to prevent burnout and ensure high-quality care services during crises, such as the COVID-19 pandemic.
3. Methods
3.1. Research Design and Sample
This cross-sectional study was conducted on nurses in Rasht's Razi hospital during 2021 - 2022. The research sample consisted of 270 nurses working in COVID-19 (n = 43), emergency (n = 55), or other (n = 172) wards of the hospital, selected using convenience sampling. The inclusion criteria were working as a nurse in one of the wards of Razi hospital, an age range of 20 to 60 years, and willingness to participate in the study. The exclusion criterion was not completing the study measures. First, the participants were asked to answer questions on a checklist regarding age, gender, marital status, education, years of employment, hospital ward, working hours in the last 3 months, history of being infected with COVID-19, the severity of the disease if infected, the level of perceived social, family, and financial support during the pandemic, occupational satisfaction from the nursing profession, social respect for the nursing profession after the pandemic, the effect of vaccination in reducing the fear of contracting the coronavirus, the effect of the pandemic on the lives of individuals and their gatherings, the death history of first-degree relatives and close friends/colleagues, the severity of discomfort on hearing the news of the death of their compatriots, and having a child in primary school. Then, the participants were asked to answer the Maslach Burnout Inventory-Human Services Survey (MBI-HSS) (23) and the Depression, Anxiety, Stress Scale (24) to assess occupational burnout and three negative affective responses, namely stress, anxiety, and depression, respectively. The project was explained to all participants, and informed consent was obtained. Statistical data were analyzed using SPSS software version 22.
3.2. Instruments
A checklist developed by present researchers was used to gather the sociodemographic information. The checklist included the following variables: age, gender, marital status, education, years of employment, hospital ward, working hours in the last 3 months, COVID-19 infection, infection severity, social support, family support, financial support, occupational satisfaction, social respect, vaccination effect, COVID-19 effect on life, COVID-19 effect on parties, family member death, relative death, discomfort about family member or relative death, and having child in primary school.
3.2.1. Maslach Burnout Inventory-Human Services Survey
This 22-item inventory is a widely used inventory developed to measure the frequency and severity of burnout among human service professionals in three dimensions: emotional exhaustion (EE) (9 items; measures feelings of being emotionally overextended and exhausted by one's work), depersonalization (DP) (5 items; measures an unfeeling and impersonal response toward patients), and reduced personal accomplishment (RPA) (8 items; measures reduced feelings of competence and achievement in one's work) (23). The answers are on a seven-point Likert scale (from 0 to 6). Higher item scores in EE, DP, and RPA correspond to high levels of burnout. The total internal consistency of the MBI-HSS is 0.83, and the validity of its subscales is reported as 0.92 - 0.71.23 In Iran. Amini reported Cronbach's alpha of its subscales as 0.86 - 0.81 (25).
3.2.2. Depression, Anxiety, and Stress Scales-42
This well-known and widely used measure was designed to assess symptoms of depression, anxiety, and stress in adolescents and adults (24). The internal consistency of Depression, Anxiety, and Stress Scales-42 (DASS-42) in a clinical sample of 437 individuals was excellent (0.89 - 0.96 for three scales), and its test-retest reliability was favorable (0.71 - 0.81 for three scales) (26). Additionally, exploratory factor analysis supported its three-factor structure. In Iran, Afzali et al. reported the alpha coefficient of three scales as 0.88 - 0.85 (27).
3.3. Ethical Consideration
The present study was approved by the scientific and ethical committees of Guilan University of Medical Sciences (code: IR.GUMS.REC.1400.334). Before implementing the study assessments, sufficient information was provided to the participants, and written consent was obtained.
3.4. Statistical Analysis
To analyze the data, we conducted an evaluation of descriptive statistics and Pearson's correlation coefficient. Then, multiple analysis of variance (MANOVA) was performed to assess differences among participants based on the obtained scores. To further investigate, Levene's test was utilized to verify or refute the assumption of homogeneity of variances. Ultimately, as this assumption was confirmed for all scores (P > 0.05), Tukey's test was employed for the scores that showed differences between groups, as indicated by the MANOVA.
4. Results
The mean and standard deviation of the age of 270 participants were 35.87 and 7.33 years, respectively. The demographic information of the participants is presented in Table 1.
Table 1.
Frequency (No.) and Percentage (%) of Participants’ Demographic Characteristics
| Variables | No. (%) |
|---|---|
| Age | |
| 20 - 30 | 83 (30.7) |
| 31 - 40 | 115 (42.6) |
| 41 - 50 | 65 (24.1) |
| 51 - 60 | 7 (2.6) |
| Gender | |
| Female | 216 (80) |
| Male | 54 (20) |
| Marital status | |
| Single | 96 (35.6) |
| Married | 174 (64.4) |
| Education | |
| Bachelor of science | 214 (79.3) |
| Master of science | 56 (20.7) |
| Years of employment | |
| 1 - 10 | 125 (46.3) |
| 11 - 20 | 108 (40) |
| 21 - 30 | 37 (13.7) |
| Hospital ward | |
| COVID - 19 | 43 (15.9) |
| Emergency | 55 (20.4) |
| Other | 172 (63.7) |
| Working hours in last 3 months | |
| 100 - 250 | 13 (4.8) |
| 251 - 400 | 65 (24.1) |
| 401 - 550 | 188 (69.6) |
| 551 - 700 | 4 (1.5) |
| COVID-19 infection | |
| No | 77 (28.5) |
| Yes | 193 (71.5) |
| Infection severity | |
| Free symptom | 5 (2.5) |
| Moderate symptom | 172 (86.9) |
| Sever symptom | 21 (10.6) |
| Social support | |
| No change | 9 (3.3) |
| Poor | 109 (40.4) |
| Moderate | 126 (46.7) |
| High | 26 (9.6) |
| Family support | |
| No change | 2 (0.7) |
| Poor | 10 (3.7) |
| Moderate | 102 (37.8) |
| High | 156 (57.8) |
| Financial support | |
| No change | 10 (3.7) |
| Poor | 89 (33) |
| Moderate | 120 (44.4) |
| High | 51 (18.9) |
| Occupational satisfaction | |
| No change | 3 (1.1) |
| Poor | 92 (34.1) |
| Moderate | 142 (52.6) |
| High | 33 (12.2) |
| Social respect | |
| No change | 19 (7) |
| Poor | 46 (17) |
| Moderate | 173 (64.1) |
| High | 32 (11.9) |
| Vaccination effect | |
| Low | 8 (3) |
| Moderate | 171 (63.3) |
| High | 91 (33.7) |
| COVID-19 effect on life | |
| No change | 1 (0.4) |
| Low | 5 (1.9) |
| Moderate | 109 (440.4) |
| High | 155 (57.4) |
| COVID-19 effect on parties | |
| Low | 3 (1.1) |
| Moderate | 97 (35.9) |
| High | 170 (63) |
| Family member death | |
| No | 279 (92.2) |
| Yes | 21 (7.8) |
| Relatives death | |
| No | 207 (77) |
| Yes | 62 (23) |
| Friends death | |
| No | 166 (61.5) |
| Yes | 104 (38.5) |
| Discomfort | |
| Low | 4 (1.5) |
| Moderate | 133 (49.3) |
| High | 133 (49.3) |
| Child in primary school | |
| No | 207 (76.7) |
Abbreviation: COVID-19, coronavirus disease 2019.
Pearson's correlation coefficient was used to examine the correlation between participants' scores in MBI-HSS and DASS-42. As observed in Table 2, all MBI-HSS and DASS-42 scores have a positive correlation with each other (P < 0.0001). However, the only non-significant association was the correlation between the subscales of reduced personal accomplishment subscale and the depression (P = 0.832).
Table 2.
Correlation Matrix Between Maslach Burnout Inventory-Human Services Survey (MBI-HSS) and Depression, Anxiety, and Stress Scales-42 (DASS-42) Scores
| Variables | EE | DP | RPA | MBI | DS | AS | SS |
|---|---|---|---|---|---|---|---|
| EE | 1 | ||||||
| DP | 0.460 a | 1 | |||||
| RPA | 0.452 a | 0.337 a | 1 | ||||
| MBI | 0.869 a | 0.656 a | 0.788 a | 1 | |||
| DS | 0.385 a | 0.376 a | -0.013 | 0.299 a | 1 | ||
| AS | 0.373 a | 0.396 a | 0.174 a | 0.383 a | 0.712 a | 1 | |
| SS | 0.536 a | 0.345 a | 0.404 a | 0.565 a | 0.606 a | 0.663 a | 1 |
Abbreviations: EE, emotional exhaustion subscale; DP, depersonalization subscale; RPA, reduced personal accomplishment subscale; MBI, Maslach Burnout Inventory; DS, depression subscale; AS, anxiety subscale; SS, stress subscale.
a Correlation is significant at the 0.01 level (2-tailed).
Multiple analysis of variance was used to examine the differences in MBI-HSS and DASS-42 scores in terms of demographic variables. As shown in Table 3, several variables show statistically significant relationships with the MBI-HSS and DASS-42 scores (P < 0.05). for example, financial support, occupational satisfaction, social respect, education, years of employment, hospital ward, social support, family support, family member death, relative’s death, and having a child in primary school age all have significant relationships with either the MBI-HSS or DASS-42 scores.
Table 3.
Multiple Analysis of Variance (MANOVA) Between Demographic Variables and Maslach Burnout Inventory-Human Services Survey (MBI-HSS) and Depression, Anxiety, and Stress Scales-42 (DASS-42) Scores
| Variables | F | Sig. |
|---|---|---|
| Age | 1.368 | 0.140 |
| Gender | 2.636 | 0.017 |
| Marital status | 0.103 | 0.996 |
| Education | 3.190 | 0.005 |
| Years of employment | 0.739 | 0.713 |
| Hospital ward | 1.785 | 0.048 |
| Working hours in the last 3 months | 1.612 | 0.052 |
| COVID-19 infection | 0.937 | 0.469 |
| Infection severity | 0.918 | 0.556 |
| Social support | 2.341 | 0.001 |
| Family support | 3.152 | 0.000 |
| Financial support | 1.965 | 0.010 |
| Occupational satisfaction | 1.648 | 0.044 |
| Social respect | 3.313 | 0.000 |
| Vaccination effect | 0.924 | 0.523 |
| COVID-19 effect on life | 1.030 | 0.423 |
| COVID-19 effect on parties | 0.789 | 0.662 |
| Family member death | 3.117 | 0.006 |
| Relative death | 2.211 | 0.043 |
| Friend death | 0.712 | 0.640 |
| Discomfort | 1.457 | 0.137 |
| Child in primary school age | 2.245 | 0.040 |
Abbreviation: COVID-19, coronavirus disease 2019.
Tukey's post-hoc test was used to further analyze the data. The results showed that gender had a significant effect on reduced personal accomplishment and total occupational burnout, with women scoring higher in both categories. Education also had a significant effect on depersonalization, reduced personal accomplishment, and total occupational burnout, with participants with a bachelor's degree scoring higher than those with a master's degree. The hospital ward only had a significant effect on reduced personal accomplishment, with nurses in COVID-19 wards scoring the highest and those in emergency wards scoring the lowest. Social support had a significant effect on anxiety and stress, with the group with high support scoring higher. Family support had a significant effect on emotional exhaustion, depersonalization, total occupational burnout, depression, anxiety, and stress, with moderate to high support leading to lower scores in these categories. Financial support had a significant effect on reduced personal accomplishment and total occupational burnout, with nurses with moderate to high support scoring lower. Occupational satisfaction only had a significant effect on emotional exhaustion, with higher satisfaction resulting in lower scores. Social respect had a significant effect on emotional exhaustion, depersonalization, total occupational burnout, depression, anxiety, and stress, with moderate to high respect leading to lower scores. The history of the death of a family member due to COVID-19 also had a significant effect on emotional exhaustion, depersonalization, depression, anxiety, and stress, with those with such a history scoring higher. The death of relatives due to COVID-19 had a significant effect on emotional exhaustion, with those with a history of this scoring higher. Finally, having a primary school child had a significant effect on anxiety, with those with a primary school child scoring higher (Table 4).
Table 4.
Test of Between-Subject Effects
| Variables | F | Sig. |
|---|---|---|
| Gender | ||
| EE | 3.488 | 0.063 |
| DP | 0.107 | 0.743 |
| RPA | 10.962 | 0.001 |
| MBI | 7.512 | 0.007 |
| DS | 0.172 | 0.679 |
| AS | 0.719 | 0.397 |
| SS | 1.967 | 0.162 |
| Education | ||
| EE | 0.753 | 0.387 |
| DP | 4.273 | 0.040 |
| RPA | 16.351 | 0.000 |
| MBI | 9.237 | 0.003 |
| DS | 0.913 | 0.340 |
| AS | 0.354 | 0.553 |
| SS | 0.047 | 0.829 |
| Hospital ward | ||
| EE | 0.091 | 0.913 |
| DP | 0.473 | 0.624 |
| RPA | 5.473 | 0.005 |
| MBI | 1.765 | 0.173 |
| AS | 0.039 | 0.962 |
| SS | 1.185 | 0.308 |
| Social support | ||
| EE | 0.725 | 0.538 |
| DP | 2.402 | 0.068 |
| RPA | 2.589 | 0.054 |
| MBI | 0.493 | 0.687 |
| DS | 1.721 | 0.164 |
| AS | 3.079 | 0.028 |
| SS | 2.904 | 0.036 |
| Family support | ||
| EE | 4.064 | 0.008 |
| DP | 3.579 | 0.015 |
| RPA | 1.262 | 0.288 |
| MBI | 3.741 | 0.012 |
| DS | 6.944 | 0.000 |
| AS | 5.719 | 0.001 |
| SS | 4.055 | 0.008 |
| Financial support | ||
| EE | 1.659 | 0.177 |
| DP | 1.709 | 0.166 |
| MBI | 4.852 | 0.003 |
| DS | 1.050 | 0.371 |
| AS | 0.716 | 0.543 |
| SS | 1.311 | 0.272 |
| Occupational satisfaction | ||
| EE | 4.875 | 0.003 |
| DP | 0.415 | 0.743 |
| RPA | 0.823 | 0.482 |
| MBI | 0.990 | 0.398 |
| DS | 1.225 | 0.302 |
| AS | 0.280 | 0.840 |
| SS | 1.980 | 0.118 |
| Social respect | ||
| EE | 8.342 | 0.000 |
| DP | 2.570 | 0.055 |
| RPA | 7.446 | 0.000 |
| MBI | 11.325 | 0.000 |
| DS | 6.621 | 0.000 |
| AS | 5.797 | 0.001 |
| SS | 6.286 | 0.000 |
| Family member death | ||
| DP | 4.284 | 0.040 |
| RPA | 0.958 | 0.329 |
| MBI | 2.197 | 0.140 |
| DS | 14.527 | 0.000 |
| AS | 12.823 | 0.000 |
| SS | 3.884 | 0.050 |
| Relatives death | ||
| EE | 5.036 | 0.026 |
| DP | 0.264 | 0.608 |
| RPA | 1.361 | 0.245 |
| MBI | 0.435 | 0.510 |
| DS | 0.239 | 0.626 |
| AS | 1.201 | 0.274 |
| SS | 0.044 | 0.834 |
| Child in primary school age | ||
| EE | 2.772 | 0.097 |
| DP | 0.615 | 0.434 |
| RPA | 0.311 | 0.578 |
| MBI | 2.303 | 0.131 |
| DS | 0.921 | 0.338 |
| AS | 7.550 | 0.006 |
| DS | 0.176 | 0.839 |
Abbreviation: EE, emotional exhaustion subscale; DP, depersonalization subscale; RPA, reduced personal accomplishment subscale; MBI, Maslach Burnout Inventory; DS, depression subscale; AS, anxiety subscale; SS, stress subscale.
5. Discussion
The recent pandemic has increased the nurses’ workload in many countries and has caused many of them to experience burnout and its negative affective consequences (8, 28-30). The present study aimed to examine the association between occupational burnout and depression, anxiety, and stress in nurses in one of the most important referral centers for patients with COVID-19 in the north of Iran. The findings of the present study showed a strong positive correlation between occupational burnout and depression, anxiety, and stress. In addition, according to the MANOVA test, most demographic variables of the study created a significant difference in occupational burnout and depression, anxiety, and stress scores.
The results of the present study regarding the association between nurses' occupational burnout and depression, anxiety, and stress during COVID-19 are consistent with the results of many other studies. Zhu et al. showed that the anxiety of nurses was significantly higher than that of physicians; however, there was no difference in depression between them (31). Zerbini et al. showed higher levels of fatigue, stress, depression, and low job satisfaction in nurses than other healthcare workers (32). Noh et al. also demonstrated that more than half of frontline nurses had burnout, and 59.6%, 23.0%, 36.0%, and 17.4% of nurses experienced insomnia, depression, anxiety, and stress, respectively (33). The results of several Iranian studies are consistent with the above-mentioned findings and have shown an increase in occupational burnout and its negative psychological correlates, especially anxiety, stress, and depression (20, 22, 34). In addition to nurses, this experience has been reported by other healthcare workers. In a qualitative study with a literature review approach, Ulfa et al. indicated that healthcare workers experienced more depression, stress, and burnout than other health workers who did not have personal involvement in medical work during the COVID-19 pandemic (35).
The other finding of the current study, which to some extent complements the above-mentioned finding, is the reduced personal accomplishment in nurses working in COVID-19 wards. Although burnout in emergency wards has been documented in the pre-COVID-19 (36), in the present study, nurses in COVID-19 and emergency wards obtained the highest and lowest scores in reduced personal accomplishment, respectively. This finding is consistent with some findings of the relevant literature and inconsistent with another one. A Chinese study showed that vulnerability measures of physiological and psychological responses in frontline nurses were significantly lower than those of non-frontline nurses (37). In a hospital study at the University of Augsburg, Germany, nurses in COVID-19 wards reported higher stress, fatigue, and depressed mood and lower work-related satisfaction than their colleagues in other wards (32). In addition, a systematic review by Bagheri et al. indicated that nurses in the intensive care unit experienced more psychological problems than other nurses due to high workload and more exposure to patients with COVID-19 (13).
In the research literature, some speculations have been made, and some findings have been obtained about the psychological correlates of occupational burnout. In Ariapooran et al.'s study, 56.41% of nurses intended to leave their jobs due to the burnout caused by COVID-19; however, a much larger number of them (86.41%) suffered from burnout (38). In a systematic review by Bagheri Sheykhangafshe et al., high workload, lack of personal protective equipment, sleep deprivation of work pressure, activity in the coronary special ward, history of psychological disorders, being a woman, fear of being infected with COVID-19 and transmitting it to family and relatives, social isolation, and lack of familiarity and sufficient previous training for dealing with epidemics and pandemics were among the factors that increased occupational burnout and reduced the mental health of nurses during the outbreak of COVID-19 (13). They have emphasized that these factors can lead to symptoms, such as anxiety and depression (13). In addition, another review indicated that heavy workload, lack of personal protective equipment, environmental problems of hospitals, and concern about the risk of disease transmission are among the causes of burnout and, as a result, its negative psychological consequences (39).
The results of the current study showed a significant difference in occupational burnout in terms of gender; accordingly, female nurses obtained higher scores. This result has also been obtained in many other studies. Female nurses are more likely to experience emotional distress and burnout. In a study in China, male subjects had less burnout than female medical staff (31). In a study, nurses were twice as likely to be depressed as other healthcare workers and female nurses working on the frontlines of caring for COVID-19 patients had the highest rates of depression, anxiety, insomnia, and distress (40). In addition, a review study showed more burnout in female nurses (39). However, in some studies, the opposite has been reported. For example, in Ariapooran et al.'s study, male nurses reported more occupational burnout during COVID-19 and had more intentions to leave their jobs (38). Various reasons have been mentioned as to why female nurses are more affected by COVID-19. Female nurses have a greater probability than male nurses of suffering from emotional exhaustion and symptoms of depression (30, 38). It has been shown that female nurses develop higher levels of stress than other healthcare workers when dealing with patients with COVID‐19 (30).
The present study has several limitations. Firstly, the sample size was relatively small and selected from a hospital in the north of Iran, and this might limit the generalizability of the results. Secondly, due to limited access to nurses under COVID-19 restrictions, we could not use structured diagnostic tools for evaluating emotional syndromes, and as a result, we inevitably used a self-report scale for measuring depression, anxiety, and stress. However, we are aware of the limitations of self-report measures in assessing psychological constructs. Thirdly, we did not pay due attention to the ethnicity of the participants in this study, and this can also limit the generalizability of its results. According to some findings regarding the importance of ethnicity in mental health during COVID-19 (38), it is recommended that this variable be paid sufficient attention in future studies.
5.1. Conclusions
In general, the findings show that nurses during the recent public health crisis were significantly affected by professional burnout, and this is associated with negative affective responses. Considering that burnout affects professional performance, it is expected that it has a detrimental effect on nurses' performance in caring for patients and their appropriate adaptation to stressful clinical conditions. Given that nurses are on the frontline of public health crises, it is necessary to pay attention, especially at an organizational scale, to the common vulnerabilities and the best way to deal with them. It seems that arrangements, such as training about dealing with diseases before the occurrence of health crises, providing personal protective equipment, increasing multifaceted support for workers, training emotion regulation techniques, and reducing long working hours, can have a positive effect on managing occupational burnout and negative emotional states associated with it.
