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Challenge, threat, coping potential: How primary and secondary appraisals of job demands predict nurses' affective states during the COVID‐19 pandemic.
- Source :
- Nursing Open; Jun2023, Vol. 10 Issue 6, p3840-3853, 14p
- Publication Year :
- 2023
-
Abstract
- Aim: The COVID‐19 pandemic has led to a rapid raise of work‐related stress among nurses, affecting their emotional well‐being. This study examined how nurses appraise job demands (i.e. time pressure, emotional demands and physical demands) during the pandemic, and how primary (i.e. challenge and threat) and secondary appraisals (i.e. coping potential) of job demands predict nurses' affective states (i.e. positive affect, anger and anxiety). Design: A cross‐sectional online survey. Methods: 419 nurses completed self‐report measures of job demands and related appraisals. Data analyses comprised correlation analysis, factor analysis, hierarchical linear regression analysis and dominance analysis. Results: Emotional and physical demands correlated exclusively with threat appraisal, while time pressure correlated with challenge and threat appraisal. Time pressure, emotional demands and threat appraisals of job demands predicted negative affective states, while challenge appraisals of emotional and physical demands predicted positive affect. Coping potential was identified as the most important predictor variable of nurses' affective states. Public Contribution: The current study identified statistically significant risk and protective factors in view of nurses' affective states experienced during the COVID‐19 pandemic. [ABSTRACT FROM AUTHOR]
- Subjects :
- CROSS-sectional method
SELF-evaluation
STATISTICAL correlation
SCALE analysis (Psychology)
DATA analysis
HOSPITAL nursing staff
ANGER
STATISTICAL sampling
MULTIPLE regression analysis
EMOTIONS
PSYCHOLOGICAL adaptation
ANXIETY
DESCRIPTIVE statistics
CHI-squared test
JOB stress
RESEARCH
STATISTICS
BODY movement
AFFECT (Psychology)
FACTOR analysis
DATA analysis software
CONFIDENCE intervals
COVID-19 pandemic
TIME
REGRESSION analysis
Subjects
Details
- Language :
- English
- ISSN :
- 20541058
- Volume :
- 10
- Issue :
- 6
- Database :
- Complementary Index
- Journal :
- Nursing Open
- Publication Type :
- Academic Journal
- Accession number :
- 163632254
- Full Text :
- https://doi.org/10.1002/nop2.1642