Predictive factors of anxiety and depression among nurses fighting coronavirus disease 2019 in China.
Int J Ment Health Nurs
; 30(2): 524-532, 2021 Apr.
Article
in English
| MEDLINE | ID: covidwho-1045710
ABSTRACT
Anxiety and depression are common mental illnesses among nurses fighting coronavirus disease 2019 (COVID-19). However, the precise factors that affect anxiety and depression in this population require further evaluation. This study aimed to explore factors associated with anxiety and depression among nurses fighting COVID-19 in China. We used convenience sampling to recruit 282 nurses fighting COVID-19 in three hospitals. Participants were questioned about demographic characteristics, daily working time, daily sleep duration, sleep quality, anxiety, depression, resilience, and coping styles. Linear regression analysis indicated that resilience (ß = -0.217, P < 0.001), positive coping style (ß = -0.281, P < 0.001), negative coping style (ß = 0.395, P < 0.001), and sleep quality (ß = 0.153, P = 0.010) were predictive factors for anxiety, and the model explained 44.20% (P < 0.001) of variability. Resilience (ß = -0.239, P < 0.001), positive coping style (ß = -0.222, P < 0.001), negative coping style (ß = 0.152, P < 0.001), and sleep quality (ß = 0.104, P = 0.003) were identified as explanatory factors for depression, and the model explained 34.50% (P < 0.001) of variability. The present study suggested that resilience, coping styles, and sleep quality could account for an individual's levels of anxiety and depression.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Anxiety
/
Depression
/
COVID-19
/
Nursing Staff, Hospital
/
Occupational Diseases
Type of study:
Experimental Studies
/
Observational study
/
Prognostic study
/
Qualitative research
/
Randomized controlled trials
Limits:
Adult
/
Humans
/
Middle aged
/
Young adult
Country/Region as subject:
Asia
Language:
English
Journal:
Int J Ment Health Nurs
Journal subject:
Nursing
/
Psychology
Year:
2021
Document Type:
Article
Affiliation country:
Inm.12817
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