ABSTRACT
In view of the grave consequences of distress reactions to the COVID-19 pandemic, this study investigated CSE (Core Self-evaluations) – internal/external health locus of control, generalized self-efficacy and trait optimism – and intolerance of uncertainty as potential correlates of distress reactions. We conducted an online questionnaire-based cross-sectional study with 422 Israeli respondents. Pandemic-related distress was defined by perceived stress, negative and positive affect, and worries. Predictors were: health locus of control, generalized self-efficacy, trait optimism, and intolerance of uncertainty. The findings show that CSEs and intolerance of uncertainty added between 11% (to perceived stress) and 22% (to negative affect) of explained variance beyond the background variables. Specifically, higher trait optimism and generalized self-efficacy were associated with less distress, and greater intolerance of uncertainty was correlated with higher distress. In conclusion, the CSE framework is useful for explaining psychological distress during the COVID-19 pandemic. Beyond their theoretical contribution, the findings may have practical implications for increasing resilience and ameliorating distress during a pandemic.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Notes
1. While self-esteem is part of the CSE taxonomy, measures of self-esteem have considerable overlap with measures of self-efficacy and are often used interchangeably (Kammeyer-Mueller et al., Citation2009). Due to overload considerations, we did not measure self-esteem in the study.
2. The survey included additional measures that were not part of this study (e.g. coronavirus representations, adherence to protective health behaviors).
3. Intolerance of uncertainty was combined with CSEs in the Tables.
4. Dichotomous variables were recoded as dummy variables prior to analyses.