Spanish 
Chinese 
Russian 
German 
French 
Japanese 
Portuguese 
Hindi Short Communication, Rje Vol: 5 Issue: 8
The impact of psychosocial risk reduction on sickness absence leave days: Structural equation model analysis
Introduction
Objectives: This study was conducted to determine the impact of workplace psychosocial risk reduction on sickness absence days of employees.
Methods: We used longitudinal data with two points of time: t0 represents the baseline analysis of psychosocial risk; at t1 the effects of psychosocial risk management measures between t0 and t1 were evaluated. At both points of time we determined the sickness leave absence days of employees. We measured Psychosocial Risk with the Module2 of Psychosocial Stressors (PBM2). Data for both t0 and t1 was available for 183 departments of 28 companies from various lines of business. The difference between t0 and t1 for the average PBM2 results and sick leave days for each department was calculated.
Results: The average sickness leave reduction was 0.9 days per employee (p<0, 05). The psychosocial risk reductions on the 0-100% scales of the four PBM2 dimensions were: Social Climate (SC): 10.0%, Work Organization (WO): 4.7%, Job Requirements (JR): 9.8% and for the Work Environment (WE): 17.4% (P<0.05). The structural equation model showed a significant relationship between the psychosocial risk reduction of the PBM2 dimensions and the decrease of sickness absence days.
Abstract
Objectives: This study was conducted to determine the impact of workplace psychosocial risk reduction on sickness absence days of employees.
Methods: We used longitudinal data with two points of time: t0 represents the baseline analysis of psychosocial risk; at t1 the effects of psychosocial risk management measures between t0 and t1 were evaluated. At both points of time we determined the sickness leave absence days of employees. We measured Psychosocial Risk with the Module2 of Psychosocial Stressors (PBM2). Data for both t0 and t1 was available for 183 departments of 28 companies from various lines of business. The difference between t0 and t1 for the average PBM2 results and sick leave days for each department was calculated.
