Hospital workers' psychological resilience after the 2015 Middle East respiratory syndrome outbreak.
To respond successfully to an outbreak, hospital workers must be prepared in terms of knowledge, skills, and emotional capacity to deal with tremendous psychological distress (Mitroff, 2005). However, little research attention has been paid to the emotional disruption experienced by these workers during an infectious disease outbreak and its influences after an outbreak. Studies conducted in this context have been limited by a narrow focus on the risk factors of psychological disruption (e.g., heavy workload, health concerns, social isolation, and conflicting roles; Maunder, Hunter, et al., 2003; Maunder, Lancee, Rourke, et al., 2004; Nickell et al., 2004) experienced after the 2002 severe acute respiratory syndrome outbreak, with the results being most relevant to clinical staff (doctors and nurses). In contrast, we evaluated the 2015 Middle East respiratory syndrome (MERS) outbreak in South Korea, and compared psychological resilience, appraisal of risk, emotional experience, and use of personal resources between health-care workers (HCW) and non-health-care workers (non-HCW).
Resilience as a Positive Adaptation in the Face of Crises
Over the past two decades resilience has become a popular research topic, especially regarding its role in policy and practice in various contexts related to the enhancement of health, well-being, and quality of life (G. E. Richardson, 2002; Windle, 2011). Human resource management is one such area where there has been interest in employee resilience after an organizational crisis. The resilience of hospital workers, especially nurses, has been evaluated with respect to chronic stress, high staff turnover, and resource shortages (Grafton, Gillespie, & Henderson, 2010; Hart, Brannan, & De Chesnay, 2014).
Resilience is diversely defined and conceptualized among research areas and scholars. Focusing on the core components of risk and positive adaptation, we defined resilience as showing signs of positive adaptation despite the presence of substantial risk (Britt, Shen, Sinclair, Grossman, & Klieger, 2016; Cowden & Meyer-Weitz, 2016; Masten & Narayan, 2012). Luthar, Cicchetti, and Becker (2000) suggested that, if the risk is an acute (rather than chronic) traumatic event, indicators of resilience (i.e., positive adaptation) should capture psychiatric impairment after the event. Aligned with this recommendation, previous researchers of the psychological impact of an outbreak on hospital workers used psychiatric morbidities as primary outcomes (Chong et al., 2004; Maunder, 2004; Maunder, Lancee, Balderson et al., 2006). Therefore, we adopted the likelihood of developing posttraumatic stress disorder (PTSD) as a representative indicator of resilience. Abandonment of occupational duty during an outbreak is another indicator of resilience after experiencing previous outbreaks (Balicer et al., 2010; DeSimone, 2009; Devnani, 2012). Therefore, we also included willingness to work during a future infectious disease outbreak to measure hospital workers' resilience after the MERS outbreak.
Coping Ability as a Personal Resource
Some scholars believe that a general set of characteristics enables individuals to demonstrate consistent positive adaptation across multiple contexts involving significant risk (Block & Block, 1980). Such personal characteristics can be understood as resilience itself; for example, Connor and Davidson (2003) defined resilience as the personal characteristics that allow an individual to thrive in the face of risk.
However, most scholars now perceive such personal characteristics as resources that should be evaluated to understand resilience, along with risk and positive adaptation. Masten (2001) found that resources have the opposite effect to that of risk and counteract its effect on resilience, and G. E. Richardson (2002) asserted that successfully identifying, accessing, and nurturing resources could lead to positive adaptation of risk (i.e., resilient reintegration). Other theorists have focused on how resources influence the different stages of the resilience process, such as risk appraisal, the emotional experience, and the selection of coping strategies (Fletcher & Fletcher, 2005; Fletcher & Sarkar, 2013).
The lack of consensus as to the most relevant factors and measures to use when conducting resilience research (G. E. Richardson, 2002) led Connor and Davidson (2003) to develop the Connor--Davidson Resilience Scale (CD-RISC), which is composed of various personal resource factors that protect individuals from the impact of risk and increase the possibility of positive adaptation. Because we defined resilience as the demonstration of positive adaptation, as indicated by the likelihood of PTSD and willingness to work, personal resources measured using the CD-RISC should not be labeled as resilience. Instead, we adopted the term coping ability, as Connor and Davidson (2003) also viewed measured personal characteristics as indicating individuals' stress-coping ability.
Risk is a core component of resilience, as resilience is the demonstration of positive adaption in the presence of risk (Luthar & Cicchetti, 2000). In early studies of resilience in which those who achieved social and personal success despite obvious life risks were compared with those who did not, the role of risk was limited to a preceding condition of resilience (Benson, 1997; Garmezy, 1991; Rutter, 1985). However, this approach neglects the key psychological component that links risk and response: cognitive appraisal. According to Gill (1994), risk appraisal takes place simultaneously with risk, and, without appraisal, there is no risk because risk by itself does not raise a response. Fletcher and Sarkar (2012) further proposed that how people appraise risk influences their subsequent emotive, cognitive, and behavioral responses and the resulting outcomes from these responses--that is, risk appraisal not only initiates but also influences the entire resilience process. In addition, the psychological factors that represent the ability to utilize personal resources (e.g., positivity and confidence) affect the appraisal activity.
There are few empirical investigations of the role of risk appraisal in the resilience process in crisis or disaster contexts. Lopez-Vazquez and Marvan (2003) found that, in a catastrophic risk situation, individuals perceived risk as the highest priority and exposure to that risk generated a high level of stress. Likewise, hospital workers' perception of various types of risks (e.g., influenza pandemic, natural disaster, radiation) can explain their willingness to work during a disaster related to the risk (Devnani, 2012). We focused on how hospital workers perceived the risk of the MERS infectious disease outbreak and how this affected their resilience postoutbreak.
Emotional Disruption in the Resilience Process
Emotion is a direct product of risk appraisal, and it motivates the subsequent cognitive and behavioral coping that allows individuals to adapt to a risky situation. In this sense, emotion mediates risk (or risk appraisal) and adaptation (Izard, 2010). According to G. E. Richardson (2002), disruption carries negative emotional experiences (e.g., hurt, fear, and confusion), motivating people to identify, access, and use resources to cope with the situation, achieving resilience when successful or experiencing loss and despair when unable to cope.
Few researchers have highlighted emotions as a key theoretical factor in understanding resilience. Montpetit, Bergeman, Deboeck, Tiberio, and Boker (2010) proposed a novel way to conceptualize resilience by evaluating how people encounter stress without engaging negative emotions, and how quickly they recover from stress-induced negative emotions. Galatzer-Levy et al. (2013) evaluated police officers who had a high risk of exposure to potentially traumatic events and found that lower levels of negative emotions during academy training predicted resilience.
The importance of emotions is more prominent in a crisis situation, where the magnitude of risk exceeds the usual capacity for problem solving, thereby causing emotional turmoil (Miller, 2002). As the intensity of emotional disruption rises, the quantity of personal resources required for resilience rises as well, and those with limited access to sufficient resources are unlikely to be successful in achieving a state of resilience. In a crisis caused by an infectious disease outbreak, hospital workers present intensive negative emotional responses, such as fear, anxiety, anger, and frustration (Maunder, Hunter et al., 2003). We evaluated the emotional experience of hospital workers during the MERS outbreak in the context of resilience.
The Current Study
When infectious disease risk with a traumatic impact arises, hospital workers appraise its impact as being most likely beyond their usual tactics of problem solving, experience emotional disruption, and process the risk in various ways. We hypothesized that workers' likelihood of experiencing PTSD would decrease their willingness to work during a future outbreak. Although direct evidence of this relationship is rare, it has been found that past experience of damage from a certain risk increases the intention to avoid the risk (B. Richardson, Sorensen, & Soderstrom, 1987; Zaleskiewicz, Piskorz, & Borkowska, 2002). Further, to avoid the unrealistic assumption of complete mediation by hospital workers' negative emotional experience and the likelihood of PTSD, we hypothesized that there would be indirect paths from perceived risk through the likelihood of PTSD to willingness to work, and from perceived risk through negative emotional experience to willingness to work.
We constructed an analytic path model to investigate hospital workers' resilience after an infectious disease outbreak (see Figure 1). In this model we evaluated the effect of perceived risk and resources (indicated by coping ability) on resilience, as mediated by emotional disruption. Therefore, we examined 12 indirect-effect pathways starting from either perceived risk or coping ability and ending with either of the two indicators of resilience: one pathway starting from perceived risk and ending with the likelihood of PTSD (a1a3); three pathways starting from perceived risk and ending with willingness to work (a1a3a5, a1a4, a2a5); three pathways starting from coping ability and ending with the likelihood of PTSD (b1a1a3, b1a2, b2a3); and six pathways starting from coping ability and ending with willingness to work (b1a1a3a5, b1a1b4, b1a2a5, b2a3a5, b2a4, b3a5). Direct and indirect effects of precedent factors on resilience were also examined; then we analyzed and compared the role of each factor of the resilience process between HCW) and non-HCW groups.
Participants and Procedure
Voluntary participants were 280 hospital workers from a hospital affected by the 2015 MERS outbreak in South Korea. Referring to Imai et al. (2010), we included clinical staff (doctors and nurses), clinical technical/support staff (e.g., radiological technologists, clinical laboratory technicians), and nonclinical staff (office workers) as participants. All participants were directly or indirectly involved in the crisis at the hospital during the MERS outbreak. Clinical staff were categorized as HCW and others as non-HCW. The data were collected using a survey from late August to early September, approximately 1 month after the de facto end of the outbreak announced by the public health authority (July 28, 2015). We acquired ethical approval from the institutional review board of Myongji Hospital (IRB File MJH 2017-12-005). The informed consent form was waived because of the anonymity of the data. Participants' demographic characteristics are presented in Table 1.
The likelihood of PTSD. The likelihood of PTSD was measured using the Korean version (Lim et al., 2009) of the Impact of Event Scale-Revised (Weiss & Marmar, 1997). The scale comprises 22 items ([alpha] = .93) divided across four dimensions: hyperarousal (six items), numbness/dissociation (five items), avoidance (six items), and intrusion (five items). Responses are made on a 5-point Likert scale ranging from 0 (not at all) to 4 (very much).
Willingness to work. Previous researchers measured hospital workers' willingness to work during a crisis using a single question rated using either a dichotomous response option (yes/no; DeSimone, 2009; Imai et al., 2010), or a Likert scale (Balicer et al., 2010). We adopted the latter. Accordingly, participants responded to the statement "I would take for granted my task when an outbreak of an infectious disease, such as MERS, occurs," using a 5-point Likert scale from 1 (not at all) to 5 (very much).
Coping ability. We assessed coping ability with the Korean version (Baek, Lee, Joo, Lee, & Choi, 2010) of the CD-RISC (Connor & Davidson, 2003). The scale comprises 25 items ([alpha] = .93) divided across five dimensions: personal competence, high standards, and tenacity (eight items); trust in one's instincts, tolerance of a negative effect, and strengthening effects of stress (seven items); positive acceptance of change and secure relationships (five items); control (three items); and spiritual influences (two items). Participants respond using a 5-point Likert scale ranging from 1 (not at all) to 5 (very much).
Perceived risk. It is commonly accepted that risk perception has two components: the probability of occurrence of the adverse event and the seriousness/fatality of the consequence (Yeung & Morris, 2001). Following this approach, we asked participants to respond to two statements assessing the perceived risk of the infectious disease: "I might get infected by MERS," and "If I get infected by MERS, I might die." Responses were made on a 5-point Likert scale ranging from 1 (not at all likely) to 5 (very likely).
Negative emotional experience. We assessed negative and positive emotions based on the procedure used in previous studies of the effect of emotion on resilience (Galatzer-Levy et al., 2013). Responses to the question "What emotion did you experience the most during the MERS outbreak?" were made on a 9-point Likert scale ranging from 1 (negative) to 9 (positive).
We used structural equation modeling with maximum likelihood estimation to test the analytic path model. First, the goodness of fit of the model was evaluated using the chi-square statistic ([chi square]), comparative fit index (CFI), and root mean square error of approximation (RMSEA). Then, we tested between-group (HCW vs. non-HCW) differences in structural coefficients in the path model. Next, we compared the goodness of fit of the model without constraints on the structural coefficients (Model 1: hypothesizing between-group differences in the coefficients) and the model with constraints on the structural coefficients (Model 2: hypothesizing no between-group differences in the coefficients). The significance of the difference in the goodness of fit between Models 1 and 2 was tested using the likelihood ratio test. When significant differences between the groups were confirmed, we tested the goodness of fit and reported the results separately by occupational group.
The mean scores of the two components of perceived risk were calculated (Kuttschreuter, 2006), with the emotional experience score reversed so that a high score represented a more negative emotion for a more intuitive interpretation of the result. The diagnostic cutoff value of the likelihood of PTSD was set at 22, as proposed by Lim et al. (2009), for which the diagnostic efficiency was .87, with .95 sensitivity and .80 specificity. To test the indirect effects we adopted nonparametric bootstrapping estimation. According to the recommendation by Mallinckrodt, Abraham, Wei, and Russell (2006), we used 10,000 iterations and 95% confidence intervals (CI) for the analysis. All analyses were performed using Stata IC 13.
The descriptive analysis results are shown in Table 2. The score for likelihood of PTSD was M = 11.1, SD = 0.82, [alpha] = .97. When applying the cutoff value of 22, 18.6% of the participants were at risk of having PTSD. The total score and subscores for each component of PTSD were higher in the HCW group compared to the non-HCW group. The mean score for willingness to work during future outbreaks exceeded the midpoint of 3 (M = 3.75, SD = 0.05), indicating that the participants, particularly those in the non-HCW group, tended to be willing to work during future outbreaks. The coping ability score was M = 63.2, SD = 14.32, [alpha] = .96, and the total score and subscores for each component were higher in the non-HCW group than in the HCW group. The perceived risk was close to the midpoint of 3 ( M = 3.02, SD = 1.01, [alpha] = .67). Discrepancy was observed between the perceived possibility of occurrence (getting infected; M = 3.39, SD = 1.05) and the perceived severity of the consequence (health impact of the disease; M = 2.65, SD = 1.27), indicating that participants tended to perceive that they were likely to get infected by MERS but that it would not be fatal; scores on both subscales were higher in the HCW group than in the non-HCW group. The score for emotional experience was M = 5.6, SD = 0.11, which indicates that most participants experienced negative emotions, and the HCW group expressed a more negative emotional experience compared to the non-HCW group.
We tested the analytic path model regarding the relationships between risk appraisal, emotional disruption, coping ability, and resilience indicators (the likelihood of PTSD and the willingness to work) with all participants. The model fit indices indicated that the path model was acceptable, [chi square] = 0.45, p = .501; CFI = 1.000, RMSEA = .000, 90% CI [0.000, 0.138]. To determine whether there were differences between occupational groups in terms of structural coefficients, we compared the model without constraints on the coefficients (Model 1: between-group differences) and the model with constraints on the coefficients (Model 2: no between-group differences). The goodness of fit of Model 1, [chi square] = 0.54, p = .762; CFI = 1.000, RMSEA = .000, 90% CI [0.000, 0.113], was superior compared to that of Model 2, [chi square] = 20.69, p = .037; CFI = .925, RMSEA = .079, 90% CI [0.019, 0.131]. These results indicate that the HCW and non-HCW groups differed in terms of structural coefficients in the path model, and a likelihood ratio test indicated that the difference was significant, [chi square] = 20.2, p = .017. Therefore, the goodness-of-fit test was stratified according to occupational group. In both groups, the model fits were acceptable: HCW group, [chi square] = 0.01, p = .932; CFI = 1.000, RMSEA = .000, 90% CI [0.000, 0.064]; non-HCW group, [chi square] = 0.54, p = .464; CFI = 1.000, RMSEA = .000, 90% CI [0.000, 0.210].
Path Analysis Results
Estimation by occupational group yielded similarities between groups (see Figures 2 and 3). In terms of the direct effect of the precedent factors (perceived risk, coping ability, and negative emotional experience) on resilience, a high level of perceived risk and a more negative emotional experience lowered resilience in both HCW and non-HCW groups. Specifically, the likelihood of PTSD was greater when there was a high level of perceived risk (HCW group: [beta] = .42, p < .01; non-HCW group: [beta] = .17, p < .01) and a more negative emotional experience (HCW group: [beta] = .17, p < .01; non-HCW group: [beta] = .30; p < .05), and willingness to work was reduced by negative emotional experience (HCW group: [beta] = -.21, p < .05; non-HCW group: [beta] = -.21, p < .05).
High coping ability generally increased resilience. Willingness to work was increased by high coping ability in both the HCW group ([beta] = .24, p < .01) and the non-HCW group ([beta] = .41, p < .01). Negative emotional experience had an indirect mediating effect on the relationship between perceived risk and resilience in both groups. In the HCW group, high perceived risk increased negative emotional experience and, consequently, willingness to work in a future outbreak was reduced, a1a4: [beta] = -.05, p < .05; 95% CI [0.099, -0.001]. In the non-HCW group, high perceived risk increased negative emotional experience and this, in turn, increased the likelihood of PTSD, a1a3: [beta] = .08, p < .05; 95% CI [0.015, 0.145].
Differences between the groups were also identified (see Figures 2 and 3). The magnitude of the direct effect of perceived risk and emotional experience differed in terms of likelihood of PTSD. Specifically, the HCW group was significantly more affected by perceived risk (HCW group: [beta] = .42, p < .05; non-HCW group: [beta] = .17, p < .05), whereas the non-HCW group was significantly more affected by negative emotional experience (HCW group: [beta] = .17, p < .01; non-HCW group: [beta] = .30, p < .05).
Differences between the groups were also observed with regard to coping ability. In the non-HCW group coping ability reduced perceived risk ([beta] = -.22, p < .05) and the likelihood of PTSD ([beta] = -.18, p < .05) and increased willingness to work ([beta] = .41, p < .01), whereas in the HCW group coping ability affected only the willingness to work ([beta] = .24, p < .01). Further, the effect size was smaller in the HCW group compared to the non-HCW group.
For indirect effects, perceived risk and negative emotional experience mediated the effect of coping ability on resilience in the non-HCW group only. Although the effects of each pathway on coping ability (b1a1a3, b1a2, b2a3) were not significant, the total indirect effect was significant, [beta] = -.08, p < .05; 95% CI [-0.153, -0.008], that is, coping ability decreased the likelihood of PTSD indirectly through low perceived risk and low negative emotional experience.
We empirically evaluated the role and position of risk appraisal, emotional disruption, and coping ability as personal resources in the resilience process during an infectious disease outbreak. Path modeling results were compared between HCW and non-HCW groups. In both groups, high risk appraisal impaired resilience whereas personal resources facilitated resilience, which is consistent with previous findings (Masten, 2001). A high perception of risk was both directly and indirectly associated with low resilience, as indicated by a higher likelihood of PTSD and lower willingness to work during a future outbreak. In contrast, coping ability as a personal resource was both directly and indirectly associated with high resilience. However, coping ability did not decrease emotional disruption in both groups, indicating that even hospital workers with a strong coping ability could experience severe emotional disruption during an outbreak. Because MERS is an emerging disease that is characterized as unknown and uncontrollable (Lagadec, 2007), the risk might have been more influential than individuals' ability to cope. In addition, emotional disruption was directly associated with low resilience and also mediated the effect of perceived risk on resilience in both groups.
In terms of between-group differences, the role of emotional experience was more pronounced in the non-HCW group, particularly in explaining the likelihood of PTSD. In the non-HCW group, emotional experience had the largest direct effect and mediated the effect of perceived risk and coping ability. In the HCW group, emotional experience also directly affected the likelihood of PTSD; however, perceived risk was more influential. The influence of coping ability was also more pronounced in the non-HCW group than in the HCW group and affected multiple stages of the resilience process, including perceived risk, the likelihood of PTSD, and willingness to work. In the HCW group, willingness to work was the only variable affected by coping ability.
All precedent factors (perceived risk, emotional disruption, and coping ability) affected the resilience of both HCW and non-HCW, but we identified specific factors that could help each group. Our results indicate that overstatement of the degree of risk in facing an infectious disease outbreak would be especially harmful for HCW. Therefore, in the face of a highly threatening disease, protection (e.g., vaccines, protective clothing) should be provided so that the risk level in the workplace remains acceptable (Maunder, 2004; Maunder, Hunter et al., 2003; O'Boyle, Robertson, & Secor-Turner, 2006). In addition, resilience-building programs that are focused on facilitating individuals' ability to cope with crises might be more beneficial for non-HCW than for HCW. Further, our results indicate that, for non-HCW, priority should be placed on relieving the intensity of the negative emotions experienced during an outbreak, along with developing strategies for adjusting the perceived level of risk.
Overall, the observed variance in the indicators of resilience (the likelihood of PTSD and willingness to work) was somewhat low. Additional resources from various sources could not only enhance the understanding of hospital workers' resilience but could also provide leads for differentiated support for hospital workers by occupational group. In a similar vein, Hart et al. (2014) proposed resilience-building strategies for nurses from multilevel resources for support, such as, debriefing sessions for those involved in stressful situations, organizational/hospital level (e.g., interdisciplinary effective communication), and individual level (e.g., maintaining positivity). Similarly, in the employee resilience literature, resources from diverse sources are categorized as individual resources (e.g., ability), unit resources (e.g., cohesion), family resources (e.g., close connection), and community resources (e.g., connection; Britt et al., 2016).
This study has some limitations. We collected data from a single hospital; as such, caution is recommended when generalizing the implications of our results to other populations. Furthermore, the level of close contact with patients during an outbreak may vary within the HCW and non-HCW groups, which could differentiate their perception and/or experience of an infectious disease outbreak. Further, factors such as the availability of resources from a hospital or community may enhance hospital workers' resilience, and we recommend that these factors are considered in future research.
The data for this research were collected and provided by Myongji Hospital. This work was supported by the Institute of Health and Environment and the National Research Foundation of Korea Grant funded by the Korean Government (No. 21B20151213037).
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Heejung Son (1), Wang Jun Lee (2), Hyun Soo Kim (3), Kkot Sil Lee (4), Myoungsoon You (5)
(1) Department of Public Health Sciences, Graduate School of Public Health, Seoul National University
(2) Office of Chief Executive Officer and Chairman, Myongji Hospital
(3) Department of Psychiatry, Myongji Hospital
(4) Department of Infection, Myongji Hospital
(5) Department of Public Health Sciences, Graduate School of Public Health and Institute of Health and Environment, Seoul National University
How to cite: Son, H., Lee, W., Kim, H., Lee, K., & You, M. (2019). Hospital workers' psychological resilience after the 2015 Middle East respiratory syndrome outbreak. Social Behavior and Personality: An international journal, 47(2), e7228
CORRESPONDENCE Myoungsoon You, Department of Public Health Science at Graduate School of Public Health and Institute of Health and Environment, Seoul National University, 1, Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea. Email: email@example.com
Table 1. Participants' Descriptive Characteristics Variables n (%) Age (years) M = 32.4 (SD = 8.18), range - 20-63 20-29 138 (49.3%) 30-39 82 (29.3%) 40+ 60 (21.4%) Gender Male 72 (25.7%) Female 208 (74.3%) Occupation Health-care worker 153 (54.6%) Non-health-care worker 127 (45.4%) Tenure Less than 1 year 53 (18.9%) 1-5 years 116 (41.4%) More than 5 years 111 (39.7%) Total 280 (100%) Table 2. Descriptive statistics Variables M (SD) HCW (n = 153) Likelihood of trauma 11.1 (13.80) 13.0 (16.42) Hyperarousal 4.5 (4.06) 5.1 (4.90) Numbness/dissociation 1.3 (1.88) 1.5 (2.20) Avoidance 2.2 (3.35) 2.6 (3.89) Intrusion 4.2 (5.29) 5.0 (6.24) Willingness to work 3.7 (0.90) 3.6 (0.88) Coping ability 63.2 (14.23) 61.5 (13.97) Personal competence 20.0 (5.13) 19.3 (4.98) Trust in one's instincts 17.1 (4.31) 16.7 (4.32) Positive acceptance of change 13.6 (3.06) 13.3 (3.07) Control 7.7 (2.04) 7.5 (2.01) Spiritual influences 4.8 (1.36) 4.6 (1.40) Perceived risk 3.02 (1.01) 3.15 (1.02) Possibility 3.39 (1.04) 3.4 (1.06) Severity 2.65 (1.27) 2.9 (1.29) Emotional experience (negative) 5.6 (1.90) 5.7 (1.86) Variables Non-HCW (n = 127) Likelihood of trauma 8.8 (9.28) Hyperarousal 3.7 (2.54) Numbness/dissociation 1.0 (1.36) Avoidance 1.9 (2.52) Intrusion 3.2 (3.64) Willingness to work 3.8 (0.91) Coping ability 65.1 (14.33) Personal competence 20.9 (5.20) Trust in one's instincts 17.6 (4.27) Positive acceptance of change 13.9 (3.04) Control 7.9 (2.06) Spiritual influences 4.9 (1.31) Perceived risk 2.86 (0.98) Possibility 3.3 (1.04) Severity 2.4 (1.20) Emotional experience (negative) 5.5 (1.96) Note. HCW = health-care worker; non-HCW = non-health-care worker.
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|Author:||Son, Heejung; Lee, Wang Jun; Kim, Hyun Soo; Lee, Kkot Sil; You, Myoungsoon|
|Publication:||Social Behavior and Personality: An International Journal|
|Date:||Feb 1, 2019|
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