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The Use of Circular Causality Networks: A Prerequisite for the Development of Efficient Psychosocial Risk Prevention and Management Plans

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Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 824)

Abstract

Problem. In the occupational health and safety field, it is usually recommended to narrow the intervention field before developing an action plan. Yet, one of the characteristics of psychosocial risks is presenting a chain of causality that can be especially complex. For example, a burnout can be caused by an overload, which is itself caused by a shortage of staff, which is caused by a lack of succession, which is caused by a lack of means in the HR budgets, etc. Yet, if numerous scientifically validated questionnaires help to evaluate health, quality of life and workplace wellness determinants (psychosocial factors), very often, the causal factors on which the action plan and intervention should be based, can be hard to identify. This presentation helps to describe a diagnosis process that fosters the identification of the causal factors on which the intervention must be based.

Method and Results. The approach proposed, used in numerous occupational health interventions for more than 15 years, is based on the fault-tree analysis used in occupational safety. Nevertheless, given the specific characteristics of psychosocial factors, the approach used builds on the implementation of a circular causality network. This simple and empirical approach allows for collective work with the different protagonists of the situation and fosters the diagnosis and especially the development of a relevant action plan. If the circular causality network construction can be based on the results of a scientifically validated questionnaire, the design of the action plan can perfectly mobilize existing tools such as Hoshin Kanri X Matrix or A3 templates used in Lean.

Keywords

  • Psychosocial factors
  • Analysis method
  • Diagnosis
  • Action plans
  • Intervention

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Voirol, C. (2019). The Use of Circular Causality Networks: A Prerequisite for the Development of Efficient Psychosocial Risk Prevention and Management Plans. In: Bagnara, S., Tartaglia, R., Albolino, S., Alexander, T., Fujita, Y. (eds) Proceedings of the 20th Congress of the International Ergonomics Association (IEA 2018). IEA 2018. Advances in Intelligent Systems and Computing, vol 824. Springer, Cham. https://doi.org/10.1007/978-3-319-96071-5_184

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