In South Korea, there were approximately 30 coronavirus 2019 (COVID-19) cases in early February 2020, but this number rapidly expanded nationwide. By April 2020, the number of infected people exceeded 10,000 (Korea Centers for Disease Control and Prevention, 2020). Disaster is defined as an ecological and sociopsychological collapse that is serious enough to go beyond the limits that a community can afford (World Health Organization [WHO], 1992). Pandemics (i.e., social disasters) have occurred in Korea twice since 2009, with H1N1 that year and Middle East Respiratory Syndrome (MERS) in 2015 (Jeong, 2017). COVID-19 is the third pandemic to occur in Korea.
COVID-19, declared a pandemic in March 2020 by the WHO, is expected to have a variety of social, historical, and cultural impacts worldwide (Eichenberger et al., 2020; WHO, 2020). “Social distancing,” a public health strategy, is effective in preventing infection; however, it has increased psychological distancing and loneliness, which impair mental health (Koh, 2020). Individuals who are separated or isolated from others feel bullied, abandoned, or neglected (Jiloha, 2016). In previous studies, isolated individuals complained of high anxiety and anger, with 19% reporting that anxiety symptoms persisted for up to 6 months after isolation (Jeong et al., 2016). Therefore, mental health management is an essential component of handling the COVID-19 pandemic. To manage mental health, it is necessary to identify mental health levels and predictive factors for impaired mental health.
Factors related to mental health that need to be examined include personal characteristics and environmental issues. According to previous studies, physical health status and mental health are closely related to personal factors, and individuals with chronic physical illness have been reported to have poorer mental health (El-Gabalawy et al., 2011; Lee & Kim, 2018). In addition, demographic characteristics should also be considered as factors contributing to mental health. As a result of analyzing the prognosis of patients with COVID-19 in China, age, comorbidity, and cardiovascular disease were factors found to differ between discharged and deceased patients (Ruan et al., 2020). Having these factors (e.g., old age, disease) is believed to exacerbate the health anxiety of people with these characteristics. Physical health and personal characteristics should be investigated to enable early classification of groups vulnerable to health problems.
Individual resilience is also thought to be a major variable in mental health. Resilience is an adaptive trait that allows a person to cope with and recover from stress events, trauma, and disaster (Iacoviello & Charney, 2020), and can be an indicator of whether an individual can return to their daily routine after the COVID-19 pandemic. Individuals who have high resilience will have a greater likelihood of overcoming the COVID-19 pandemic because high resilience provides optimism about the future when faced with adversity. High resilience also increases cognitive flexibility to accept negative situations and seek meaning and affirmation in those situations (Iacoviello & Charney, 2020). Conversely, some mental health disorders are characterized by inadequate fear of consequences or inadequate efforts to avoid fear or uncertainty (Hayes et al., 1996), which can result in low resilience if mental health problems occur. In other words, by assessing resilience, mental health levels can be estimated.
COVID-19 is a highly contagious disease and therefore it is necessary to consider environmental factors when discussing it. Community resilience is a community’s potential to strengthen its ability to cope with crisis situations, including social support and the use of public resources (Norris et al., 2008). In addition, it is important to identify community resilience as a predictor of mental health needs because environmental interventions that promote community resilience pathways are more important than individual treatment interventions that take place after a disaster (Heo & Choi, 2017).
Therefore, the current study aimed to identify mental health predictors after the COVID-19 pandemic by considering personal and environmental factors related to mental health, and to provide basic data for developing customized community mental health services after the pandemic. The specific purposes of the current study were to identify: (a) participants’ general and COVID-19–related characteristics, mental health, perceived physical health, and individual and community resilience; (b) the correlations among perceived physical health, individual resilience, and community resilience and mental health; (c) the differences in mental health levels according to the characteristics of participants; and (d) the predictors of mental health.
Method
Study Design
The current study was a descriptive survey study to identify mental health levels of Korean adults after the outbreak of COVID-19 and to identify predictors of mental health distress.
Participants
The G* Power 3.1 program was used as the basis for calculating the required number of participants, and according to Cohen’s (2013) criteria, the number of participants was determined to be 202 when the medium effect size required for multiple regression analysis was 0.15 and the power was 0.9. A previous study showed that an online survey had a low response rate of 47% compared to the offline questionnaire (Nulty, 2008); therefore, we considered the dropout rate to be 45% and planned to recruit more than 293 people. Because of the nature of the online survey, many people were able to access and fill out the questionnaire at the same time. As a result, the final number of participants was 338.
Inclusion criteria were: (a) adults age ≥18 years who voluntarily expressed willingness to participate and agreed to participate in the study; (b) anyone who could use a smartphone or the internet (for online data collection); and (c) anyone who had currently lived in their residence for more than 1 month (to measure community resilience). Exclusion criteria were: (a) those who did not have the ability to understand the purpose and contents of the study; (b) those having limited reading and writing skills; and (c) those who were receiving psychiatric treatment due to anxiety, depressive disorder, and mental health disorders prior to the COVID-19 pandemic.
General and COVID-19–Related Characteristics
General characteristics of participants were gender, age, residential area, educational level, religion, economic level, marital status, and number of dependents. COVID-19–related characteristics were investigated for infection and changes in economic activity.
Mental Health
Mental health was measured using the Korean version of the General Health Questionnaire-12 (GHQ-12), as modified by Park et al. (2012), that had been developed by Goldberg and Hillier (1979) for screening and early detection of mental disorders for the general population. This instrument can be used as a wide-ranging screening instrument for high-risk groups (Park et al., 2012) by measuring how the state of mental health has changed in the past few weeks compared to the psychological state that was usually experienced (Goldberg & Hillier, 1979). The GHQ-12 has two factors of “depression/anxiety” and “social dysfunction,” with higher scores indicating greater psychological pain (Park et al., 2012). There is “0-0-1-1” bimodal scoring and a “0-1-2-3” Likert scale; however, the two scoring methods show the same results (Kim et al., 2013). In the current study, a 4-point Likert scale was used. Cronbach’s alpha for this scale was 0.79 in the Park et al. (2012) study, and 0.85 in the current study.
Perceived Physical Health
In previous correlation studies between physical health status and mental health, physical health status was determined by subjective self-reporting (Argyle, 1997). In the current study, self-evaluation was conducted to evaluate one item of “self-perceived physical health level,” with scores ranging from 0 (very bad) to 10 (very good).
Community Resilience
Community resilience was measured by the Conjoint Community Resiliency Assessment Measure (CCRAM) developed by Leykin (2013). The original author approved the CCRAM for translation and the use of the Korean version. After translation into the Korean version, it was reviewed by a PhD in English literature who was fluent in English and Korean. The CCRAM is a 5-point Likert scale comprising 21 questions, and the higher the score, the higher the community’s resilience. The reliability of the CCRAM is considered very good, with a Cronbach’s alpha of 0.92 in the original version (Leykin et al., 2013) and 0.91 in the current study.
Individual Resilience
Participants’ resilience was measured with the response to one statement: “Resilience is defined as the ability to quickly return to routine following an emergency event; my personal level of resilience is high.” Responses were rated on a 5-point Likert scale, ranging from 1 (not at all) to 5 (very much).
Data Collection
Data collection was conducted after obtaining approval from the Institutional Review Board (IRB) of Daegu Haany University Korean Medicine Hospital (IRB no. DHUMC-D-20007-ANS-01). The data collection period lasted 5 days, from May 19 to May 24, 2020. According to the quarantine policy, data were only to be collected online as most concentrated areas had to be avoided. Participants were recruited from publicity on websites (e.g., text messaging services, blogs) that share information from the community, using a web or app-based online survey system. We posted the uniform resource locator (URL) and quick response (QR) code, which could be accessed through the questionnaire on the online bulletin board, and explained the research purpose, inclusion/exclusion criteria, and recruitment period. Data from 338 people were collected, but four respondents were excluded, and the final 334 were used for data analysis. Participants who were excluded were those whose residence period was either <1 month or unclear.
Data Analysis
Collected data were analyzed using SPSS version 21.0. The specific method was as follows: (1) participants’ general characteristics and COVID-19–related characteristics, mental health status, perceived physical health, individual resilience, and community resilience were calculated by frequency and percentage, and mean and standard deviation; (2) independent t test and one-way analyses of variance were used to examine the differences in mental health according to general characteristics of participants and COVID-19– related characteristics; (3) Pearson correlation analysis was used to examine the correlation among mental health, perceived physical health, individual resilience, and community resilience; (4) hierarchical linear regression was performed to identify predictors of mental health.
Results
General and COVID-19–Related Characteristics
Of 334 participants, 217 (62%) were women, and mean age of the overall study population was 32.22 (SD = 10.46, range = 18 to 65). The study comprised 14 regions, with 15.3% of participants from Daegu, which was designated as a special disaster area. The educational level of most participants included graduation from high school, with most (n = 187, 56%) being university graduates. Monthly income levels were low, middle, and high, with most participants reporting middle income (n = 144, 43.1%). Unmarried participants comprised 57.8% (n = 193) of the total. A family of two to three people was described for 136 (40.7%) participants. Of the total participants, 78 (23.4%) were raising young children, 19 (5.7%) were caring for an older adult, and four (1.2%) were caring for an individual with a disability. Regarding COVID-19–related characteristics, 324 (97%) participants were not infected, and those who were not infected but had come in contact with someone with COVID-19 com- prised 3% (n = 10) of the sample. Regarding economic activity due to the COVID-19 pandemic, 273 (81.7%) participants reported no change (Table 1).
Table 1: General and Covid-19–Related Characteristics of Participants (N = 334) |
Mental Health, Perceived Physical Health, Individual Resilience, and Community Resilience
Measurement of the mental health of participants was 0.97 (SD = 0.56, range = 0 to 2.5) for the item average, and 11.75 (SD = 6.15, range = 0 to 30) for the total score mean; perceived physical health was 7.17 (SD = 1.91, range = 1 to 10); individual resilience was 3.85 (SD = 1.91, range = 1 to 5); and community resilience was 3.30 (SD = 0.53, range = 1.38 to 5) (Table 2).
Table 2: Levels of Mental Health, Perceived Physical Health, and Individual and Community Resilience |
Differences in Mental Health by General and COVID-19–Related Characteristics
Among the general characteristics, there was only a difference in gender, and it was found that women were more vulnerable to poorer mental health than men (t = −3.09, p = 0.002). The geographical regions were analyzed by dividing them into disaster zones (Daegu) and non-disaster zones (13 zones), and they showed no difference (t = −0.945, p = 0.345). There was no difference in infection status (t = −0.216, p = 0.829), and no difference in economic activity changes (F = 0.642, p = 0.633). There were no differences in mental health among other variables (Table 3).
Table 3: Differences in Mental Health by Characteristics of Participants (N = 334) |
Correlations Among Mental Health, Perceived Physical Health, Individual Resilience, and Community Resilience
Mental health was negatively correlated with perceived physical health (r = −0.283, p < 0.001), individual resilience (r = −0.321, p < 0.001), and community resilience (r = −0.243, p < 0.001) (Table 4). These findings indicate that the lower the physical health, individual resilience, and community resilience, the poorer the mental health.
Table 4: Correlations Among Mental Health, Perceived Physical Health, Individual Resilience, and Community Resilience |
Predictors of Mental Health
Gender was controlled after dummy processing, and hierarchical regression analysis was performed by inputting perceived physical health, individual resilience, and community resilience. Since the skewness (0.409) and kurtosis (−0.272) of the standard residual are both less than two absolute values, the assumption of normality is satisfied, and there is no abnormality value. The autocorrelation of the dependent variable ensured independence because the Durbin-Watson index was 1.931. The multicollinearity between independent variables was used for the variance inflation factor (VIF), and the VIF was <10, ranging from 1.014 to 1.256, indicating that there was no multicol-linearity.
As a control variable, in the first-level hierarchy, gender had a significant effect on mental health (B = 0.178), and the explanatory power for mental health was 5.7%. In the second hierarchy, Model 2, which included independent variables, the explanatory power increased significantly to 15.5% (p < 0.001). Perceived physical health (β = −0.160, p = 0.004), individual resilience (β = −0.212, p < 0.001), and community resilience (β = −0.119, p = 0.031) all had a significant effect on mental health when gender was controlled (Table 5).
Table 5: Predictors of Mental Health |
Discussion
The purpose of the current study was to investigate the mental health levels of Korean adults during the COVID-19 pandemic and to identify the predictors of mental health status. In this study, gender, perceived physical health, individual resilience, and community resilience were identified as predictors of mental health status, and it is significant that the importance of community roles was verified after the start of the pandemic. Therefore, it is thought that early screening of mental health disorders should be performed for individuals with weak physical condition or those who lack the welfare or crisis response resources of the community. In addition, if these basic data are accumulated, national health policies can be proposed and those who need to benefit from public services can be carefully managed.
The total mental health score for adults was 11.75 in the current study. In a study of 6,510 Korean individuals between ages 18 and 65, the score calculated by bimodal scoring (0-0-1-1) was 1.63 (Kim et al., 2013). The two scores were converted to a total score of 100 so that the two studies could be compared when each was evaluated using the bimodal and Likert scales. The previous study’s score was 13.58, compared to 32.66 in the current study. Compared to the study by Kim et al. (2013), scores in the current study were found to be lower in individuals with mood disorders (5.33 raw score, 100 points converted: 44.42), and higher in individuals with anxiety disorders (3.25 raw score, 100 points converted: 27.08). The current study suggests that the mental health of adults has deteriorated since the COVID-19 pandemic because people with existing mental illness were excluded. In addition, the GHQ-12 results show that it is necessary to screen for mental health disorders, such as anxiety disorders, during the COVID-19 pandemic because it is a valid and reliable instrument used to screen for mental health disorders in the community.
In terms of demographic characteristics, mental health was significantly different according to gender. In a study by Kim et al. (2013), mental health results were lower in women, which showed the same results as the current study. Women experiencing more depressive symptoms may be understood as a result of differences in societal roles rather than women experiencing more stressful events than men (Nazroo, 2001). It is presumed that the role of women has become more significant due to the increased time spent indoors with family due to COVID-19. In the current study, there was no difference according to support for young children or older adults, but as women take care of young children, older adults, or adolescents in their social role, women’s mental health affects the family’s mental health (Afifi, 2007). Therefore, women will need to be the targets of careful mental health care in the community under these conditions.
Regarding general characteristics, there were no significant differences in mental health problems in terms of region, status of infection, religion, educational level, marital status, number of family members, or presence of dependents. According to a previous study, experiences of life changes, such as economic difficulties and asset decline, were found to increase post-traumatic stress disorder by 1.71 times and 1.59 times, respectively, which was a factor inhibiting the recovery of daily life (Lim & Sim, 2018). In the current study, an income of <2 million Won and change in economic activity showed the lowest level of mental health function in the unemployed group, but there was no statistically significant difference among other groups. Contrary to previous studies, results of the current study showed no difference in mental health according to economic level, thus additional research is necessary. Future studies should be conducted to identify differences in mental health according to changes in leisure activities (e.g., shopping, travel, cultural and artistic activities), which can be affected by economic level.
When gender was controlled, the independent variable was found to affect mental health in the order of individual resilience, perceived physical health, and community resilience. The results of individual resilience affecting mental health in the current study are similar to that of resilience as a predictor of absence of anxiety in a study of disaster survivors (Docena, 2015). In particular, resilience in this preceding study showed an adaptive coping response and correlation, which could indirectly support the current study, including social dysfunction in mental health. Therefore, it is important to provide programs that promote resilience to help improve mental health.
Another factor affecting mental health was subjective physical health. Older adults have been reported to have deteriorated mental health when they have chronic diseases or common allergies, cataracts, arthritis, and lung diseases (El-Gabalawy et al., 2011). In particular, the physical health reported subjectively in this previous study was found to be related to anxiety, supporting the results of the current study. Considering the situation in which it was difficult to use the hospital or to go outside after the outbreak of the COVID-19 pandemic, it is presumed that proper treatment could not be obtained when physical health was poor, and that the weakening of body functions may have raised fears of infection. It is believed that these situations had an impact on mental health.
According to a study by Iacoviello and Charney (2014), improving physical fitness through physical exercise increases resilience and the likelihood of survival in traumatic situations. It is not necessary to give up the benefits of physical activity and exercise even during the pandemic because exercise can take place alone and indoors (Koh, 2020). Therefore, physical activity during the pandemic should be encouraged as it will also help recovery after the pandemic.
Finally, community resilience was one of the predictors of mental health. Similar to previous studies (Docena, 2015), community resilience was a predictor of the absence of anxiety as well as individual resilience. Community resilience includes collective efficacy. Community resilience is similar to social support, and social support networks can mean the difference between a resilient outcome and the development of psychopathology (Iacoviello & Charney, 2014). In addition, the preparedness of the community is an important factor, and local environmental factors, such as the number of hospital beds in the community, have been reported to affect mental health (Seong et al., 2019). Social capital and public trust have also been shown to affect mental health, even for those who have experienced indirect disasters (Moon-kyung et al., 2018). These facts support the results of the current study.
Meanwhile, awareness of the available “safety net” helps individuals face or recover from stress or traumatic situations (Seong et al., 2019). In addition, the community’s approach to resilience explains the pain of trauma experience in context, and it can promote healing and posttraumatic growth by leveraging the strengths and resources of relational networks (Walsh, 2007). Therefore, our study suggests that community resilience can be used as an appropriate mediation strategy after the COVID-19 pandemic.
In summary, perceived physical health, individual resilience, and community resilience were predictors of mental health outcomes and their effects suggest the development of a mental health promotion program that incorporates these factors. The predictors mentioned above are not individual and are particularly focused on resilience (Iacoviello & Charney, 2014). Community resilience needs to be addressed. Increasing community resilience means that the resources available to people in times of crisis are well-prepared, which can contribute to mental health.
Limitations
Because data were only collected online, we had to limit the number of questions for the convenience of respondents. Inevitably, individual resilience was measured using a single question. There was a limitation in that resilience has an unknown effect on mental health according to its subfactors. In a future study, it is suggested that those sub-factors be measured using a questionnaire designed for that purpose.
Conclusion
The current study was performed to identify the predictors of mental health after the outbreak of COVID-19 in Korea. These predictive factors were gender, subjective physical health, and personal and community resilience. The roles of the community and individual resilience in the stage of recovery and during the pandemic were found to be most important. Mental health predictors should be used for early assessment of high-risk groups for mental health disorders in the community, and to provide mental health programs to help people recover their daily health.
References
- Afifi, M. (2007). Gender differences in mental health. Singapore Medical Journal, 48(5), 385–391 PMID:17453094
- Argyle, M. (1997). Is happiness a cause of health?Psychology & Health, 12(6), 769–781 doi:10.1080/08870449708406738 [CrossRef]
- Cohen, J. (2013).Statistical power analysis for the behavioral sciences. Taylor & Francis. doi:10.4324/9780203771587 [CrossRef]
- Docena, P. S. (2015). Adaptive coping, resilience, and absence of anxiety among displaced disaster survivors. Philippine Journal of Psychology, 48(2), 27–49 https://www.pap.org.ph/sites/default/files/pdf/PJP1502_Final_2Docena.pdf
- Eichenberger, R., Hegselmann, R., Savage, D., Stadelmann, D. & Torgler, B. (2020). Certified coronavirus immunity as a resource and strategy to cope with pandemic costs. Kyklos, 73, 464–474 doi:10.1111/kykl.12227 [CrossRef]
- El-Gabalawy, R., Mackenzie, C. S., Shooshtari, S. & Sareen, J. (2011). Comorbid physical health conditions and anxiety disorders: A population-based exploration of prevalence and health outcomes among older adults. General Hospital Psychiatry, 33(6), 556–564 doi:10.1016/j.genhosppsych.2011.07.005 [CrossRef] PMID:21908055
- Goldberg, D. P. & Hillier, V. F. (1979). A scaled version of the General Health Questionnaire. Psychological Medicine, 9(1), 139–145 doi:10.1017/s0033291700021644 [CrossRef] PMID: doi:10.1017/S0033291700021644 [CrossRef]424481
- Hayes, S. C., Wilson, K. G., Gifford, E. V., Follette, V. M. & Strosahl, K. (1996). Experimental avoidance and behavioral disorders: A functional dimensional approach to diagnosis and treatment. Journal of Consulting and Clinical Psychology, 64(6), 1152–1168 doi:10.1037//0022-006x.64.6.1152 [CrossRef] PMID: doi:10.1037/0022-006X.64.6.1152 [CrossRef]8991302
- Heo, S. & Choi, H. (2017). Intervention principles promoting community resilience after disasters: A systematic review [article in Korean]. Korean Journal of Clinical Psychology, 36(2), 255–282 doi:10.15842/kjcp.2017.36.2.009 [CrossRef]
- Iacoviello, B. M. & Charney, D. S. (2014). Psychosocial facets of resilience: Implications for preventing posttrauma psychopathology, treating trauma survivors, and enhancing community resilience. European Journal of Psychotraumatology, 5(1), 23970 doi:10.3402/ejpt.v5.23970 [CrossRef] PMID:
- Iacoviello, B. M. & Charney, D. S. (2020). Cognitive and behavioral components of resilience to stress. In Stress resilience (pp. 23–31). Elsevier. doi:10.1016/B978-0-12-813983-7.00002-1 [CrossRef]
- Jeong, E. K. (2017). Public health emergency preparedness and response in Korea. Journal of the Korean Medical Association, 60(4), 296–299 doi:10.5124/jkma.2017.60.4.296 [CrossRef]
- Jeong, H., Yim, H. W., Song, Y.-J., Ki, M., Min, J.-A., Cho, J. & Chae, J.-H. (2016). Mental health status of people isolated due to Middle East respiratory syndrome. Epidemiology and Health, 38, e2016048 doi:10.4178/epih.e2016048 [CrossRef] PMID:
- Jiloha, R. (2020). COVID-19 and mental health. Epidemiology International, 5(1), 7–9 doi:10.24321/2455.7048.202002 [CrossRef]
- Kim, Y. J., Cho, M. J., Park, S., Hong, J. P., Sohn, J. H., Bae, J. N., Jeon, H. J., Chang, S. M., Lee, H. W. & Park, J.-I. (2013). The 12-item general health questionnaire as an effective mental health screening tool for general Korean adult population. Psychiatry Investigation, 10(4), 352–358 doi:10.4306/pi.2013.10.4.352 [CrossRef] PMID:
- Koh, K. W. (2020). Physical activity guideline for social distancing during COVID-19 [article in Korean]. Korean Journal of Health Education and Promotion, 37(1), 109–112 doi:10.14367/kjhep.2020.37.1.109 [CrossRef]
- Korea Centers for Disease Control and Prevention. (2020). COVID-19 domestic occurrence. http://ncov.mohw.go.kr
- Lee, J. Y. & Kim, Y. J. (2018). Analysis of differences in mental health according to amount of daily work and leisure physical activity and physical health status: Focusing on the 2016 National Health and Nutrition Survey. The Korean Society of Sports Science, 27(6), 73–81 doi:10.35159/kjss.2018.12.27.6.73 [CrossRef]
- Leykin, D., Lahad, M., Cohen, O., Goldberg, A. & Aharonson-Daniel, L. (2013). Con-joint Community Resiliency Assessment Measure–28/10 items (CCRAM28 and CCRAM10): A self-report tool for assessing community resilience. American Journal of Community Psychology, 52(3–4), 313–323 doi:10.1007/s10464-013-9596-0 [CrossRef] PMID:
- Lim, H. S. & Sim, K. (2018). The effects of life changes on post-traumatic stress disorder after disasters [article in Korean]. The Korean Journal of Stress Research, 26(4), 319–326 doi:10.17547/kjsr.2018.26.4.319 [CrossRef]
- Moon-kyung, M., Hye-sun, J. & Hyunnie, A. (2018). Psychosocial factors influential to the mental health of the public indirectly affected by the 9/12 Gyeong-ju earthquake: Focusing on individual resilience, social support, social capital, and public trust. Korea Journal of Counseling, 19(5), 93–116 doi:10.15703/kjc.19.5.201810.93 [CrossRef]
- Nazroo, J. Y. (2001). Exploring gender difference in depression. https://www.psychiatrictimes.com/view/exploring-gender-difference-depression
- Norris, F. H., Stevens, S. P., Pfefferbaum, B., Wyche, K. F. & Pfefferbaum, R. L. (2008). Community resilience as a metaphor, theory, set of capacities, and strategy for disaster readiness. American Journal of Community Psychology, 41(1–2), 127–150 doi:10.1007/s10464-007-9156-6 [CrossRef] PMID:
- Nulty, D. D. (2008). The adequacy of response rates to online and paper surveys: What can be done?Assessment & Evaluation in Higher Education, 33(3), 301–314 doi:10.1080/02602930701293231 [CrossRef]
- Park, J.-I., Kim, Y. J. & Cho, M. J. (2012). Factor structure of the 12-item General Health Questionnaire in the Korean general adult population [article in Korean]. Journal of Korean Neuropsychiatric Association, 51, 178–184 doi:10.4306/jknpa.2012.51.4.178 [CrossRef]
- Ruan, Q., Yang, K., Wang, W., Jiang, L. & Song, J. (2020). Clinical predictors of mortality due to COVID-19 based on an analysis of data of 150 patients from Wuhan, China. Intensive Care Medicine, 46, 846–848 doi:10.1007/s00134-020-05991-x [CrossRef] PMID:32125452
- Seong, L. D., Gwon, N. H. & Hoon, L. (2019). The effects of individual, regional and environmental factors on health level: Focused on physical and mental health awareness. Journal of the Korean Management Association, 32(4), 41–52 doi:10.36700/KRUMA.2019.12.32.4.41 [CrossRef]
- Walsh, F. (2007). Traumatic loss and major disasters: Strengthening family and community resilience. Family Process, 46(2), 207–227 doi:10.1111/j.1545-5300.2007.00205.x [CrossRef] PMID:17593886
- World Health Organization. (1992). Psychosocial consequences of disasters: Prevention and management. https://apps.who.int/iris/handle/10665/58986
- World Health Organization. (2020). WHO Director-General’s opening remarks at the media briefing on COVID-19 – 11 March 2020. https://www.who.int/dg/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19—11-march-2020
General and Covid-19–Related Characteristics of Participants (
Gender | |
Female | 217 (65) |
Male | 117 (35) |
COVID-19 infection | |
No | 324 (97) |
Had contact with someone infected |
10 (3) |
Yes | 0 (0) |
Region of residence | |
Seoul | 79 (23.7) |
Gyeonggi | 68 (20.4) |
Gyeongbuk | 66 (19.8) |
Daegu | 51 (15.3) |
Chungnam | 17 (5.1) |
Pusan | 17 (5.1) |
Incheon | 12 (3.6) |
Gyeongnam | 9 (2.7) |
Jeonnam | 5 (1.5) |
Deajeon | 4 (1.2) |
Gangwon | 2 (0.6) |
Ulsan | 2 (0.6) |
Chungbuk | 1 (0.3) |
Gwaanju | 1 (0.3) |
Educational level | |
High school | 105 (31.4) |
University | 187 (56) |
Graduate school | 42 (12.6) |
Religion | |
None | 201 (60.2) |
Christianity | 58 (17.4) |
Buddhism | 48 (14.4) |
Catholicism | 27 (8.1) |
Income |
|
Low | 92 (27.5) |
Middle | 144 (43.1) |
High | 37 (11.1) |
Not applicable | 61 (18.1) |
Economic activity | |
No change | 273 (81.7) |
Decrease in sales | 20 (6) |
Unpaid vacation | 17 (5.1) |
Unemployment | 5 (1.5) |
Other | 19 (5.7) |
Marital status | |
Unmarried | 193 (57.8) |
Married | 141 (42.2) |
Family members in residence | |
0 | 69 (20.7) |
2 to 3 | 136 (40.7) |
≥4 | 129 (38.6) |
Dependents | |
Children | |
No | 256 (76.6) |
Yes | 78 (23.4) |
Older adults | |
No | 315 (94.3) |
Yes | 19 (5.7) |
Disabled | |
No | 330 (98.8) |
Yes | 4 (1.2) |
Health problem | |
No | 270 (80.8) |
Mild |
50 (15) |
Severe |
14 (4.2) |
Age (years) | 32.22 (10.46) (18 to 65) |
Levels of Mental Health, Perceived Physical Health, and Individual and Community Resilience
Mental health | |
Item average | 0.97 (0.56) (0 to 2.5) |
Total score | 11.75 (6.15) (0 to 30) |
Physical condition | 7.17 (1.91) (1 to 10) |
Individual resilience | 3.85 (0.72) (1 to 5) |
Community resilience | 3.3 (0.53) (1.38 to 5) |
Differences in Mental Health by Characteristics of Participants (
Gender | −3.098 | 0.002 | ||
Female | 217 (65) | 1.04 (0.51) (0 to 2.5) | ||
Male | 117 (35) | 0.86 (0.48) (0 to 2.25) | ||
Age (years) | 0.87 | 0.501 | ||
<20 | 12 (3.6) | 0.81 (0.54) (0 to 1.75) | ||
20 to 29 | 140 (41.9) | 1.01 (0.54) (0.08 to 2.25) | ||
30 to 39 | 111 (33.2) | 0.95 (0.51) (0 to 2.5) | ||
40 to 49 | 43 (12.9) | 0.99 (0.44) (0.25 to 2.08) | ||
50 to 59 | 20 (6) | 0.87 (0.34) (0.25 to 1.42) | ||
≥60 | 8 (2.4) | 1.16 (0.25) (0.75 to 1.5) | ||
COVID-19 infection | −0.216 | 0.829 | ||
No | 324 (97) | 0.97 (0.5) (0 to 2.5) | ||
Had contact with someone infected |
10 (3) | 1.01 (0.6) (0.25 to 1.83) | ||
Region | −0.945 | 0.345 | ||
Non-disaster area | 283 (84.7) | 0.96 (0.51) (0 to 2.25) | ||
Disaster area | 51 (15.3) | 1.04 (0.49) (0.17 to 2.5) | ||
Educational level | 1.768 | 0.172 | ||
High school | 105 (31.4) | 1.05 (0.53) (0 to 2.33) | ||
University | 187 (56) | 0.93 (0.49) (0.08 to 2.5) | ||
Graduate school | 42 (12.6) | 0.98 (0.51) (0 to 2.25) | ||
Religion | −0.423 | 0.672 | ||
No religion | 201 (60.2) | 0.96 (0.52) (0 to 2.25) | ||
Religious | 133 (39.8) | 0.99 (0.49) (0 to 2.5) | ||
Income |
1.877 | 0.133 | ||
Low | 92 (27.5) | 1.05 (0.54) (0 to 2.5) | ||
Middle | 139 (43.1) | 0.93 (0.47) (0 to 2.08) | ||
High | 37 (11.1) | 0.87 (0.42) (0 to 1.67) | ||
Not applicable | 61 (18.3) | 1.04 (0.56) (0 to 2.33) | ||
Economic activity | 0.642 | 0.633 | ||
No change | 273 (81.7) | 0.96 (0.5) (0 to 2.25) | ||
Decrease in sales | 20 (6) | 1.02 (0.36) (0.42 to 1.92) | ||
Unpaid vacation | 17 (5.1) | 1.06 (0.49) (0.17 to 1.83) | ||
Unemployment | 5 (1.5) | 1.22 (0.82) (0.42 to 2.50) | ||
Other | 19 (5.7) | 1.05 (0.59) (0.42 to 2.33) | ||
Marital status | 0.629 | 0.53 | ||
Unmarried | 193 (57.8) | 0.99 (0.54) (0 to 2.33) | ||
Married | 141 (42.2) | 0.95 (0.46) (0 to 2.5) | ||
Family members in residence | 1.175 | 0.31 | ||
0 | 69 (20.7) | 0.94 (0.56) (0 to 2.25) | ||
2 to 3 | 136 (40.7) | 0.94 (0.49) (0 to 2.33) | ||
≥4 | 129 (38.6) | 1.03 (0.49) (0.08 to 2.5) | ||
Dependents | ||||
Children | −0.19 | 0.849 | ||
No | 256 (76.6) | 0.98 (0.52) (0 to 2.33) | ||
Yes | 78 (23.4) | 0.96 (0.48) (0 to 2.5) | ||
Older adults | 1.123 | 0.262 | ||
No | 315 (94.3) | 0.97 (0.51) (0 to 2.5) | ||
Yes | 19 (5.7) | 1.10 (0.44) (0.25 to 1.58) | ||
Disabled | 1.014 | 0.312 | ||
No | 330 (98.8) | 0.97 (0.51) (0 to 2.5) | ||
Yes | 4 (1.2) | 1.23 (0.34) (0.75 to 1.5) | ||
Health problem | 1.829 | 0.162 | ||
No | 270 (80.8) | 0.95 (0.5) (0 to 2.5) | ||
Mild |
50 (15) | 1.07 (0.52) (0.08 to 2.33) | ||
Severe |
14 (4.2) | 1.13 (0.44) (0.58 to 2.17) |
Correlations Among Mental Health, Perceived Physical Health, Individual Resilience, and Community Resilience
Perceived physical health | −0.283 | <0.001 | ||||
Individual resilience | −0.321 | <0.001 | 0.351 | <0.001 | ||
Community resilience | −0.243 | <0.001 | 0.282 | <0.001 | 0.371 | <0.001 |
Predictors of Mental Health
(constant) | 0.859 | 2.136 | ||||
Gender |
0.178 | 0.168 | 0.002 | 0.146 | 0.138 | 0.007 |
Perceived physical health | −0.042 | −0.160 | 0.004 | |||
Individual resilience | −0.149 | −0.212 | <0.001 | |||
Community resilience | −0.114 | −0.119 | 0.031 | |||
F | 9.598 | 0.002 | 18.047 | <0.001 | ||
Radj2(ΔRadj2) | 0.025 | 0.155 (0.137) |
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