Although healthcare workers (HCWs) experienced significant stress during the 2015 outbreak of Middle East Respiratory Syndrome (MERS), the factors associated with this stress remain unknown. Thus, the present study assessed burnout among HCWs during the MERS outbreak to identify the influential factors involved in this process.
This study was a retrospective chart review of the psychological tests and questionnaires completed by 171 hospital employees from two general hospitals that treated MERS patients. The tests included the Oldenburg Burnout Inventory, Positive Resources Test, the questionnaires assessed exposure to the MERS outbreak event and perceptions about MERS.
Of the 171 HCWs, 112 (65.5%) experienced disengagement and 136 (79.5%) suffered from exhaustion. Disengagement was associated with lower levels of purpose and hope, a higher perception of job risk, and exposure to the media. Exhaustion was associated with lower levels of purpose and hope, a higher perception of little control of the infection, a higher perception of job risk, prior experience related to infections, and being female.
Our results revealed the risk and protective factors associated with burnout among HCWs during an outbreak of MERS. These findings should be considered when determining interventional strategies aimed at ameliorating burnout among HCWs.
Middle East Respiratory Syndrome (MERS) is a respiratory disease that can result in death. It was first reported in September 2012 in Saudi Arabia and then spread throughout the world in 2015 [
Considerable research has assessed burnout in HCWs. For example, Edwards et al. [
However, few studies have investigated burnout during or after a MERS outbreak. Kim and Choi [
The present study was conducted between June 2015 and June 2016 during the MERS outbreak. A retrospective chart review of psychological tests completed by 171 hospital employees from two general hospitals that treated MERS patients was conducted; the sample consisted of 32 doctors, 77 nurses, and 61 others (i.e., pharmacists, technician, officers, and so forth). Approval was obtained from the Institutional Review Board of National Medical Center in Korea (no. H-1507-056-004).
Burnout was measured with the Oldenburg Burnout Inventory (OLBI), which was validated in Korean by Na [
Positive resources were measured with the Positive Resources Test, which was developed and validated in Korean by Huh et al. [
Participants also answered the following questions about their exposure to MERS: “Have you been within 2 meters of a MERS patient while not wearing personal protective equipment (PPE)?,” “Have you treated MERS patients in person while wearing PPE?,” “Have you experiences MERS-like symptoms after contact with MERS patients?,” “Have you ever been quarantined?,” “Have your hospital or employees been exposed to media reports about the MERS outbreak, such as TV, internet, radio and so on?.” All questions were answered with either “yes” or “no.”
Perceptions about MERS were assessed with a 10-item measure similar to the one described by Chong et al. [
All data were analyzed using SPSS for Windows version 22.0 (IBM Corp., Armonk, NY, USA). The means and standard deviations (SD) were calculated for burnout, positive resources, and MERS-related recognition. Categorical data were summarized using frequencies and percentages of occurrence (sex, job title, and so forth). The psychological effects of the MERS outbreak on employees were assessed based on scores on items measuring burnout and factors that might explain the variance in burnout were identified using between-group differences and correlation analyses. Between-group differences in parametric variables were determined using Student’s t-tests and analysis of variance (ANOVA) and differences in non-parametric variables were determined using Mann-Whitney U tests. Correlation analyses were performed on continuous variables. Stepwise regression analyses were performed to performed to derive predictive model for burnout. All factors identified as significant by the difference tests and correlation analyses were included as predictor variables and burnout (exhaustion, disengagement) was treated as the criterion variable. Statistical significance was set at α=0.05 (two-tailed).
The mean score for disengagement was 17.6 and the mean score for exhaustion was 20.7 (
To identify the factors that influenced burnout, between-group analyses of demographic variables, medical history, traumatic experiences, work-related characteristics, and exposure to MERS were conducted (
Correlation analyses were performed to assess positive resources and perceptions about MERS (
Stepwise regression analyses were conducted to determine which variables accounted for significant variance in burnout (
We found that 65.5% of HCWs reported disengagement and 79.5% reported exhaustion during the 2015 MERS outbreak. In addition, disengagement was associated with lower levels of purpose and hope, the perception of higher job risk, and exposure to media whereas exhaustion was associated with lower levels of purpose and hope, a lower level of perceived control, and the perception of higher job risk. However, receiving MERS education did not have a significant effect.
It is important to note that the majority of participants in the present study experienced burnout during the outbreak. The rate of burnout was similar to that observed in emergency nurses after the outbreak although the present sample included various job types, including doctors, pharmacists, and technicians [
Of the personal strength variables, purpose and hope were the most powerful protective factors against both exhaustion and disengagement. This suggests that people who have purpose and hope can bear hardships and cope well with disastrous situations, which is consistent with previous findings. For example, Youssef found that employees with hope report higher levels of job satisfaction, work happiness, and actual performance [
We also found that perceptions about MERS were an important variable predicting HCW burnout, particularly in terms of perception of job risk and lack of control. The subjective perception of job risk was a risk factor for both exhaustion and disengagement and lack of control over infection was a risk factor for exhaustion. Previous studies have emphasized the importance of a sense of control and security in psychological health and Glass and McKnight revealed a modest association between perceived uncontrollability and burnout [
In terms of experiences at the time of infection, we found that exposure to media was one of the most important factors. When exposed to media, individuals may feel like they are under constant surveillance and could be subjected to negative comments by anonymous people, which may lead to the experience of stress and withdrawal from work. For example, when MERS broke out in Korea, it was common to observe a social stigma against patients who recovered from the infection, the families of infected people, and HCWs. In this type of environment, exposure to media might be stressful and leave HCWs feeling alienated. Volpone and Avery [
We also found that prior work experience with infections was related to high levels of exhaustion, which indicates that experienced workers were more likely to feel tired. It is possible that prior work experience with treating infected patients could be traumatic for workers. For example, workers with repeated indirect traumatic experiences are more likely to experience burnout, and thus the identification and treatment of individuals with traumatic work experience may be effective for reducing future burnout in HCWs [
The present study had several limitations that should be considered. First, we included HCWs from only two hospitals, which may limit the generalizability of the results. Second, although we targeted individuals who performed certain tasks related to MERS, we did not assess the exact tasks that they did at that time, and thus it was not possible to determine which specific tasks or experiences were more likely to lead to burnout. Regardless, our findings suggest that HCWs in risky work situations were more vulnerable to burnout. Third, perceptions about MERS and exposure to MERS were not measured using validated scales. Although the subjective perception of infections is a very important factor related to psychological problems, an appropriate scale has yet to be developed. Thus, it will be necessary to develop and validate instruments that can measure subjective attitudes and perceptions about infections. Fourth, the numbers of HCWs who experienced MERS-like symptoms, were quarantined, and who were exposed to MERS without PPE were insufficient to produce significant results. Future studies should include a sufficient number of individuals with these experiences.
The present findings are meaningful in several ways. First, we found that a majority of HCWs in hospitals that treated MERS patients during the outbreak experienced burnout. Second, nurses, women, and experienced workers are more vulnerable to burnout. Third, our findings expand on previous findings by showing that personal strength and perceptions about infections are important factors that predict burnout in HCWs, which emphasizes the importance of personal strength factors, such as purpose and hope, in protecting workers against burnout. On the other hand, perception of high job risk and a sense of a lack of control are risk factors of burnout. Finally, our findings suggest practical ways to reduce burnout. For example, exposure to media is an important factor in burnout, which suggests that guidelines for the media coverage of epidemic diseases should be set.
This study was supported by a grant from the Korean Mental Health Technology R&D Project, Ministry of Health & Welfare, Republic of Korea (HL19C0007).
The authors have no potential conflicts of interest to disclose.
Conceptualization: So Hee Lee. Data curation & Investigation: Hyun Chung Kim, So Young Yoo, Kang Uk Lee, So Hee Lee. Formal analysis: Yae Eun Seo, Hae Woo Lee. Funding acquisition: So Hee Lee. Writing—original draft: Yae Eun Seo. Writing—review & editing: So Hee Lee.
Major items of perception about MERS
Items |
---|
My job puts me at great risk |
I accept the risk of caring for MERS patients |
I am afraid of falling ill with MERS |
I have little control over whether I get infected or not |
I have little chance of survival if I were to get MERS |
I am afraid I will pass MERS to others |
My family and friends are worried they might get infected through me |
People avoid my family because of my work |
MERS: Middle East Respiratory Syndrome
General characteristics of the participants (N=171)
N (%) | M (SD) | |
---|---|---|
Sociodemographic variables | ||
Gender | ||
Female | 113 (66.1) | |
Male | 58 (33.9) | |
Age | 34.2 (9.8) | |
Job type | ||
Doctor | 32 (18.8) | |
Nurse | 77 (45.3) | |
Pharmacist, other health care worker | 30 (17.65) | |
Technician, office worker | 31 (18.24) | |
Working experience | ||
<3 year | 51 (30.0) | |
3–10 year | 69 (40.6) | |
>10 year | 50 (29.4) | |
MERS education | 149 (88.7) | |
Exposure to MERS | ||
Exposure without PPE | 6 (3.5) | |
Caring with PPE | 74 (44.0) | |
MERS like symptoms | 12 (7.3) | |
Quarantined | 5 (3.0) | |
Exposure to media | 125 (78.6) | |
Traumatic experiences | ||
Prior traumatic events | 36 (21.7) | |
Prior infection experience | 53 (32.1) |
PPE: personal protective equipment, MERS: Middle East Respiratory Syndrome
Prevalence of the burnout (N=171)
Burnout | M (SD) | Range (%) |
---|---|---|
Disengagement (≥16.8) | 17.6 (2.5) | 10–31 (65.8) |
Exhaustion (≥18) | 20.7 (3.5) | 9–30 (79.5) |
Relationship of gender, marital status, living with family, medical history, traumatic experience, work-related characteristics, exposure to MERS to burnout
N | Disengagement |
Exhaustion |
|||
---|---|---|---|---|---|
M (SD) | t, F/p | M (SD) | t, F/p (scheffe) | ||
Sociodemographical variables | |||||
Gender | |||||
Female | 113 | 18.0 (2.4) | t=2.68/p=0.016 | 21.4 (3.4) | t=3.72/p<0.001 |
Male | 58 | 17 (2.6) | 19.3 (3.4) | ||
Job type | |||||
Doctor1 | 32 | 18.4 (2.5) | F=1.63/p=0.104 | 20.8 (3.3) | F=6.12/p=0.006 (2>3) |
Nurse2 | 77 | 17.7 (2.4) | 21.4 (3.3) | ||
Others3 | 61 | 17.2 (2.6) | 19.6 (3.5) | ||
Working experience | |||||
<3 year | 51 | 17.2 (2.6) | F=3.79/p=0.043 | 20.4 (3.1) | F=0.27/p=0.599 |
3–10 year | 69 | 18.2 (2.5) | 21.0 (3.7) | ||
>10 year | 50 | 17.3 (2.3) | 20.5 (3.5) | ||
MERS education | |||||
Yes | 149 | 17.6 (2.5) | t=3.67/p=0.586 | 20.6 (3.4) | t=0.98/p=0.426 |
No | 19 | 17.9 (2.6) | 21.3 (4.2) | ||
Traumatic experience | |||||
Prior traumatic events | |||||
Yes | 36 | 18.1 (2.4) | t=-1.66/p=0.148 | 21.8 (3.3) | t=-2.09/p=0.023 |
No | 130 | 17.5 (2.5) | 20.3 (3.4) | ||
Prior experience related to infection | |||||
Yes | 53 | 18.1 (2.1) | t=-1.77/p=0.052 | 21.6 (3.3) | t=-2.91/p=0.007 |
No | 112 | 17.3 (2.6) | 20.1 (3.3) | ||
Exposure to MERS | |||||
Exposure without PPE | |||||
Yes | 6 | 18.8 (2.7) | t=-1.00/p=0.231 | 22.8 (2.6) | t=-1.93/p=0.071 |
Uncertain or no | 164 | 17.6 (2.5) | 20.6 (3.5) | ||
Caring with PPE | |||||
Yes | 74 | 18.3 (2.7) | t=2.61/p=0.006 | 21.5 (3.7) | t=-3.00/p=0.003 |
No | 94 | 17.2 (2.2) | 20.0 (3.2) | ||
MERS like symptoms | |||||
Yes | 12 | 18.7 (1.9) | t=2.06/p=0.147 | 22.5 (3.1) | t=2.15/p=0.063 |
No | 152 | 17.6 (2.5) | 20.6 (3.5) | ||
Quarantined | |||||
Yes | 5 | 19.4 (3.0) | U=269 |
22.2 (4.2) | U=289 |
No | 160 | 17.6 (2.5) | 20.6 (3.5) | ||
Exposure to media | |||||
Yes | 125 | 18.0 (2.4) | t=3.73/p<0.001 | 21 (3.4) | t=2.37/p=0.014 |
No | 34 | 16.3 (2.4) | 19.4 (3.3) |
Mann-Whitney U test
Relationship of the age, positive resources and perception about MERS to the burnout
Disengagement |
Exhaustion |
|||
---|---|---|---|---|
Pearson r | p | Pearson r | p | |
Age | -0.131 | 0.089 | -0.199 | 0.009 |
POREST | ||||
Optimism | -0.418 | <0.001 | -0.412 | <0.001 |
Purpose and hope | -0.455 | <0.001 | -0.394 | <0.001 |
Self regulation | -0.381 | <0.001 | -0.418 | <0.001 |
Social support | -0.098 | 0.204 | -0.165 | 0.031 |
Caring and serving | -0.252 | 0.001 | -0.137 | 0.074 |
Perception about MERS | ||||
Perceived job risk | 0.375 | <0.001 | 0.413 | <0.001 |
Acceptance | 0.102 | 0.189 | 0.182 | 0.018 |
Fear of falling ill with MERS | 0.199 | 0.009 | 0.305 | <0.001 |
Lack of control over infection | 0.256 | 0.001 | 0.387 | <0.001 |
Little chance of survival if infected | 0.163 | 0.034 | 0.185 | 0.016 |
Fear of passing MERS to others | 0.284 | <0.001 | 0.410 | <0.001 |
Worrying of family and friends of getting infected through me | 0.282 | <0.001 | 0.374 | <0.001 |
Avoiding my family because of my work | 0.158 | 0.041 | 0.255 | 0.001 |
Variables that explain variance in burnout
β | t | p | |
---|---|---|---|
Dependent variable: disengagement | |||
Purpose and hope (in POREST) | -0.321 | -5.971 | 0.000 |
Perceived job risk (in Perception of MERS) | 0.457 | 3.346 | 0.001 |
Exposure to media | 1.240 | 3.070 | 0.003 |
Model R2=0.33, p-value<0.001 | |||
Dependent variable: exhaustion Purpose and hope (in POREST) | -0.353 | -4.910 | 0.000 |
Lack of control over infection (in Perception of MERS) | 0.821 | 3.348 | 0.000 |
Perceived job risk (in Perception of MERS) | 0.446 | 2.156 | 0.033 |
Prior infection work experience | 1.120 | 2.343 | 0.020 |
Sex | 0.999 | 2.091 | 0.038 |
Model R2=0.38, p-value<0.001 |