Association between psychosocial working conditions and well-being before retirement: a community-based study.

Psychosocial working conditions job control job demand leisure activities population-based study well-being

Journal

Psychology, health & medicine
ISSN: 1465-3966
Titre abrégé: Psychol Health Med
Pays: England
ID NLM: 9604099

Informations de publication

Date de publication:
29 Oct 2023
Historique:
medline: 30 10 2023
pubmed: 30 10 2023
entrez: 30 10 2023
Statut: aheadofprint

Résumé

Psychosocial working conditions have been linked to mental health outcomes, but their association with well-being is poorly studied. We aimed to investigate the association between psychosocial working conditions and well-being before retirement, and to explore the role of gender and leisure activities in the association. From the Swedish National Study on Aging and Care in Kungsholmen, 598 community dwellers aged 60-65 years were included in the cross-sectional study. Lifelong occupational history was obtained through an interview. Job demands and job control in the longest-held occupation were graded with job exposure matrices. Psychosocial working conditions were classified into high strain (high demands, low control), low strain (low demands, high control), passive job (low demands, low control), and active job (high demands, high control). Well-being was assessed with the 10-item version of positive and negative affect schedule, and scored using confirmatory factor analysis. Engagement in leisure activities was categorized as low, moderate, and high. Data were analyzed using linear regression. Both high job control and high job demands were dose-dependently associated with higher well-being. Overall, compared to active jobs, passive jobs were associated with lower well-being (β -0.19, 95% CI -0.35 to -0.02,

Identifiants

pubmed: 37899630
doi: 10.1080/13548506.2023.2274316
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

1-15

Auteurs

Yuchen Zhang (Y)

King's Business School, King's College London, London, UK.
Aging Research Center, Department of Neurobiology, Health Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.

Wenzhe Yang (W)

Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China.

Weili Xu (W)

Aging Research Center, Department of Neurobiology, Health Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.
Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China.

Kuan-Yu Pan (KY)

Unit of Occupational Medicine, Institute of Environmental Medicine, Stockholm, Sweden.

Classifications MeSH