Journal Article

Working-time patterns, job-related well-being, and turnover intentions among paid caregivers: a population-based longitudinal study

Publication details

Publication date
Published in
Social Science & Medicine
Volume
410:119921
Publisher
Elsevier
ISSN
2779536

Summary

Paid care often involves long working hours and shift work schedules, yet national evidence on caregivers' working-time patterns and their associations with well-being and turnover intentions remains limited. Drawing on the effort-recovery framework, we compared care workers and home carers with other workers. This population-based longitudinal study used six waves of the UK Household Longitudinal Study (2012–2024). Five working-time exposures were modelled separately: usual weekly hours, total overtime, paid overtime, weekend work and nonstandard schedules. Covariate-adjusted generalised estimating equations examined associations with job-related anxiety and depression, measured with Warr's job-related affective well-being scale, and intentions to change employer or leave paid employment. Hours and schedule samples included 1476 and 1492 caregivers, respectively. At the first eligible hours observation, caregivers averaged 39.2 years and 82.8% were women. Caregivers reported fewer unadjusted usual weekly hours (30.0 versus 32.1), yet 2.00 more hours after adjustment (95% CI 1.54–2.46). They also had a 0.396-point higher weekend-work score (95% CI 0.360–0.431) and 3.82 times the odds of nonstandard work (95% CI 3.43–4.25). Within caregivers, longer usual hours, total overtime, weekend work and nonstandard schedules were associated with greater job-related anxiety and depression. Paid overtime showed no clear association with either outcome. Nonstandard work was associated with wanting a new employer (OR 1.46, 95% CI 1.21–1.78). Evidence for wanting to give up paid work was limited. These findings highlight schedule timing alongside work duration as a focus for supporting caregiver well-being and workforce retention.

Notes

© 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Topics