Citation

Metzler YA, von Groeling-Müller G, Bellingrath S. Safety Sci. 2019; 114: 122-139.

Copyright

(Copyright © 2019, Elsevier Publishing)

DOI

10.1016/j.ssci.2019.01.003

PMID

unavailable

Abstract

Psychosocial risk assessment is becoming increasingly important for research and occupational health and safety due to legislative amendments obliging employers to implement psychosocial work factors into general risk assessment. While various sources provide guidance on hazard identification, statistically assessing the risk probability of psychosocial hazards remains poorly understood. In the current study, we investigate the risk potential of psychosocial hazards using the German Copenhagen Psychosocial Questionnaire, in a cross-sectional sample of 549 blast furnace workers of a German steel company. We examine and compare established methods for risk evaluation in four occupational groups and in total, reflecting the working area, aiming to determine the utility of these methods for risk assessment: (1) An exposure-based approach using the scales' average scores, (2) comparing the scales' average scores with a job-exposure matrix using t-tests, (3) regression analyses, and (4) an approach by Clarke and Cooper (2000) referring to a common risk equation. Analyses show similar results of risk evaluation by each method when used within four occupational groups. Therefore, results on the sample level sufficiently represent results on the job level. Substantial differences, however, appear between the methods. Determining the approach of Clarke and Cooper to be most promising, our findings indicate that the choice of risk evaluation method strongly impacts risk management, as the derived risk minimizing measures are conducted only for those hazards evaluated as risks and according to their level of priority. Future research and policy development must focus stronger on how to best achieve psychosocial risk evaluation.

Language: en

Keywords

COPSOQ; Occupational stress; Psychosocial hazard; Risk assessment; Risk evaluation; Steel industry