Frontiers in Psychology | |
Identifying Variables That Predict Depression Following the General Lockdown During the COVID-19 Pandemic | |
article | |
Einav Gozansky1  Gal Moscona1  Hadas Okon-Singer1  | |
[1] Department of Psychology, School of Psychological Sciences, University of Haifa;The Integrated Brain and Behavior Research Center (IBBR), University of Haifa | |
关键词: depression; loneliness; COVID-19; intolerance of uncertainty; lockdown; social isolation; emotion evaluation bias; | |
DOI : 10.3389/fpsyg.2021.680768 | |
学科分类:社会科学、人文和艺术(综合) | |
来源: Frontiers | |
【 摘 要 】
This study aimed to define the psychological markers for future development of depression symptoms following the lockdown caused by the COVID-19 outbreak. Based on previous studies, we focused on loneliness, intolerance of uncertainty and emotion estimation biases as potential predictors of elevated depression levels. During the general lockdown in April 2020, 551 participants reported their psychological health by means of various online questionnaires and an implicit task. Out of these participants, 129 took part in a second phase in June 2020. Subjective loneliness during the lockdown rather than objective isolation was the strongest predictor of symptoms of depression 5 weeks later. Younger age and health related worry also predicted higher non-clinical levels of depression and emotional distress. The results support the diathesis-stress model, which posits that a combination of preexisting vulnerabilities along with stressors such as negative life events are among the factors affecting the development of psychopathology. Moreover, our results correspond with those of previous studies conducted worldwide during the COVID-19 pandemic. Taken together, these findings call for focusing on psychological factors, especially among younger people, to identify individuals at risk for future development of depression and to promote new strategies for prevention.
【 授权许可】
CC BY
【 预 览 】
Files | Size | Format | View |
---|---|---|---|
RO202108170008974ZK.pdf | 1101KB | download |