What degree of work overload is likely to cause increased sickness absenteeism among nurses? Evidence from the RAFAELA patient classification system
Article first published online: 12 JAN 2007
Journal of Advanced Nursing
Volume 57, Issue 3, pages 286–295, February 2007
How to Cite
Rauhala, A., Kivimäki, M., Fagerström, L., Elovainio, M., Virtanen, M., Vahtera, J., Rainio, A.-K., Ojaniemi, K. and Kinnunen, J. (2007), What degree of work overload is likely to cause increased sickness absenteeism among nurses? Evidence from the RAFAELA patient classification system. Journal of Advanced Nursing, 57: 286–295. doi: 10.1111/j.1365-2648.2006.04118.x
- Issue published online: 12 JAN 2007
- Article first published online: 12 JAN 2007
- Accepted for publication 5 September 2006
- acute hospital wards;
- cohort study;
- empirical research report;
- nurses’ workload;
- sickness absence RAFAELA system
Title. What degree of work overload is likely to cause increased sickness absenteeism among nurses? Evidence from the RAFAELA patient classification system
Aim. This paper reports a study examining whether nurses’ work overload is associated with increased sick leave and quantifying the loss of working days from work overload.
Background. The RAFAELA patient classification system indicates nursing care intensity in relation to an optimum and is one of the few validated monitoring instruments of patient-associated workload among nurses. However, it is not clear whether work overload is a risk factor for increased sickness absenteeism, an important occupational problem in health care.
Method. An observational cohort study was carried out with 877 nurses, 31 wards and five Finnish hospitals. Patient-associated workload scores from the RAFAELA system were based on a 6-month monitoring period in 2004. Records of 12-month self certified (1–3 days) and medically certified (>3 days) periods of sick leave in the same year were obtained from employers’ registers.
Findings. The mean workload was 9% (sd = 8%) above the optimum. There was a linear trend between increasing workload and increasing sick leave (P ≤ 0·006). Among nurses with workload ≥30% above the optimum the rate of self certified periods of sick leave was 1·44 (95% CI 1·13–1·83) times higher than among those with an optimum workload. The corresponding rate ratio for medically certified sick leave was 1·49 (1·10–2·03). These excess rates of sickness absence resulted in 12 extra sick leave days per person-year.
Conclusion. Measuring nurses’ workload may be an important part of strategic human resource management of nurses to reduce sick leave among nurses.