The DOI matches its Crossref record; 80 of the 80 references it cites were found in a registry or an index.
Staying well and engaged when demands are high: The role of psychological detachment.
Open accessAbstract
The authors of this study examined the relation between job demands and psychological detachment from work during off-job time (i.e., mentally switching off) with psychological well-being and work engagement. They hypothesized that high job demands and low levels of psychological detachment predict poor well-being and low work engagement. They proposed that psychological detachment buffers the negative impact of high job demands on well-being and work engagement. A longitudinal study (12-month time lag) with 309 human service employees showed that high job demands predicted emotional exhaustion, psychosomatic complaints, and low work engagement over time. Psychological detachment from work during off-job time predicted emotional exhaustion and buffered the relation between job demands and an increase in psychosomatic complaints and between job demands and a decrease in work engagement. The findings of this study suggest that psychological detachment from work during off-job time is an important factor that helps to protect employee well-being and work engagement.
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The Job Demands‐Resources model: state of the art
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Sonnentag, S., Venz, L., & Casper, A. (2017) · Journal of Occupational Health Psychology · doi:10.1037/ocp0000079 · Cited by 587
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Metadata
- Authors
- Sonnentag, S., Binnewies, C., & Mojza, E. J.
- Venue
- Journal of Applied Psychology
- Published
- 2010-09
- Volume
- 95
- Issue
- 5
- Pages
- 965-976
- ISSN
- 0021-9010 (ISSN-L)
- 1939-1854
- OpenAlex
- W2049488061
- Cited by
- 608
- References
- 80
- FWCI
- 31.98
- Type
- journal article
- Language
- en
- Open access
- green
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.