What degree of work overload is likely to cause increased sickness absenteeism among nurses? Evidence from the RAFAELA patient classification system

J Adv Nurs. 2007 Feb;57(3):286-95. doi: 10.1111/j.1365-2648.2006.04118.x.

Abstract

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 < or = 0.006). Among nurses with workload > or =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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Absenteeism*
  • Adult
  • Cohort Studies
  • Female
  • Finland
  • Humans
  • Male
  • Middle Aged
  • Nursing Staff, Hospital / organization & administration*
  • Personnel Staffing and Scheduling Information Systems
  • Sick Leave / statistics & numerical data*
  • Workload*