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Auvo Rauhala

Publications and source records attributed to Auvo Rauhala.

5 recordsLinked to original sources

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 < 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.

Absenteeism↗

Determining optimal nursing intensity: the RAFAELA method.

BACKGROUND: RAFAELA is a modern system of patient classification. In the last few years the system has become widely used in Finland and has aroused international interest. It comprises three parts: (1) The Oulu Patient Classification (OPC) instrument and (2) a file on nurse resources. Using these, the daily nursing care intensity, expressed as OPC points per nurse, can be calculated. The existing nursing care intensity can then be compared with the optimal by using the third instrument, (3) the Professional Assessment of Optimal Nursing Care Intensity Level (PAONCIL). This is a daily questionnaire that nurses complete in a 2-month period at intervals every few years. The daily workload is scored from -3 to +3, where zero is the optimal level. The optimal nursing care intensity per nurse is then defined by using linear regression analysis. No expensive time studies are needed. AIMS: This paper reports on a study which aimed to identify the minimum requirements for determining optimal nursing care intensity that allow the results to be accepted as correct, in terms of: length of the PAONCIL examination period, PAONCIL questionnaire response rate, explanatory power of the regression analysis and mean values of the OPC and PAONCIL instruments. DESIGN: The results of analyses of optimal nursing care intensity from 61 wards in eight Finnish hospitals for the period 1997-2001 are presented. The data do not contain any information about the identity of the patients. METHODS: Linear regression analysis, one-way analysis of variance, t-tests and correlation analysis were used, as well as parameters of distribution of the data. RESULTS: The results of the analysis of optimal nursing care intensity can be regarded as reliable if the PAONCIL response rate is above 70%, the period of examination is at least 3-4 weeks, the mean PAONCIL value is below 0.65 and the explanatory power is above 25%. CONCLUSION: On the basis of the RAFAELA system, the optimal nursing care intensity of a ward can be reliably determined. The prerequisites for achieving reliable results were clear and mostly fulfilled. Because a study period shorter than that which has previously been the practice is enough, the use of this system will be easier than before. The credibility and usefulness of the RAFAELA system have thus received considerable additional confirmation.

Analysis of Variance↗