COVID-19: Predictors of Resilience in the Face of Adversity

Background and Purpose: Early December 2019, an unusual form of pneumonia was diagnosed in the city of Wuhan, China (del Rio and Malani, 2020). This pneumonia has developed into what is now recognized as the 2019 Novel Coronavirus (COVID-19). The impact of the virus has led to a complete global shutdown and stay-at-home orders instituted globally. The lesser known, and often overlooked effects of catastrophic pandemic events, such as the COVID-19, can result in acute and long-term risks and impacts to mental health (Ferreira, 2020). Given the complexity of pandemics, more empirical research is needed on the impact of pandemic on mental health and which factors enable coping (Douglas et al., 2009; Hayes et al. 2018). This study investigated the role of previous disaster experience, resilience, perceived stress, current living situation, and demographics on overall resilience from a recently completed study conducted during the first six weeks of stay-at-home orders being instituted globally during the COVID-19 pandemic.

Methods: A 15-minute, online survey was administered through various social media platforms (e.g. Facebook, Instagram, LinkedIn) for an initial total of 335 respondents. The target sample was anyone with access to the survey link, older than 18 years, and awareness of COVID19. The survey focused on respondent’s previous disaster experience; respondent’s resilience (Connor Davidson resilience scale); perceived stress (perceived stress scale); current situation as it relates to COVID-19, and personal and household demographics. Resilience was measured using the 10 item Connor Davidson Resilience Scale, which uses a 5-point Likert scale, and has been shown to have high internal consistency, construct validity, and test-retest reliability.

Results: A hierarchical multiple regression analysis indicated that risk factors explained 8% of the variance in resilience, (F (9,332) = 120.24, p < .0001). Furthermore, protective factors explained an additional 11% of the variance in individual resilience (F (7, 331) = 138.24 p < .0001) above and beyond the variance explained by, and accounted for, by risk factors. Significant risk predictors are job loss, stay-at-home, perceived consequences, nutritional status, and stress. Significant protective factors are education level, self-care, employment, income level, and being a pet owner.

Conclusions and Implications: This study identified predictors of resilience among individuals who experienced the COVID-19 pandemic. The results identified significant risk and protective factors associated with COVID-19. Specifically, the findings suggest more nuanced and targeted interventions are needed in the areas of disaster response and recovery. Given the complexity of the pandemic, social work research should be directed at both identifying and meeting the immediate post-disaster needs of exposed populations. With the associated uncertainty of COVID-19 and the accompanying psychosocial impacts, the need to identify and support resilience attributes among those affected is crucial to enhancing well-being. Future social work research should be directed at identifying specific factors that affect resilience among various populations, and on strengthening resilience especially for vulnerable populations. Implications for social work policy, practice, and research will be discussed.