ArticleThe European journal of health economics : HEPAC : health economics in prevention and care2025
Workload and short sickness absences in a cohort of Finnish hospital employees.
Article in The European journal of health economics : HEPAC : health economics in prevention and care, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Working time traffic light recommendations: development, use, and implementation in the Finnish social and healthcare.Industrial health · 2026Article
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Authors and funding
4 authors.
Funding
Abstract
This article used register data on day-to-day working hours of hospital employees combined with patient characteristics at work unit (wards) level to measure workload and its implications for short, self-certified sickness absences. We measured statistically the average nursing treatment burden of different patient mixes in hospital wards, and then analyzed the overall workload (intensity) of working days by comparing it to the actual available nursing workforce. We found that a significant part of the workload variation occurred between working days, and it was related to unexpected changes in the number of employees. In atypical situations a long work shift was associated with caring for patients with fewer resources. The high workload of a day increased the risk of short sickness absences along the following 3-week period. The results show that managing short-term workload variability should be a key aim from the perspective of well-being at work, and that combining different data sources can provide novel, important insights to the measurement of workload.
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Registered trials
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