A Bayesian belief network for quality assessment: application to employment officer support

'Supported' employment stipulates that disabled people should have real jobs for real pay. This paper models kinds of supported employment, assesses how the support and placement features affect its outcomes and its quality from the perspective of the employees, and provides a dynamic model to help explore what types of interventions might promote greater social inclusion for people with learning and other disabilities.

Bayesian belief networks (BBNs) provide the general framework for modelling the relationships between the variables and features of interest. The structure, probabilistic specification and quality indicators were elicited from project advisory groups, including people with learning disability, and took into account a pilot survey of 30 individuals. A subsequent survey of 1,461 supported employees was used to update the model and to provide actual assessments of quality of placement.

We present the BBN methodology in some detail, as novel to this discipline. We show how the model was constructed, and its implications for supported employment. We derive indices for quality of placement, taking into account the views of clients. We show how survey and individual results can be used to update the model. Use of the model suggests that quality of placement is, on average, relatively high, with small differences between groups with differing primary disability.

The BBN is the appropriate methodology to model complex relationships and interventions for problems such as these. The model developed in this study can be used to assess and improve the fit between people and jobs, both at the individual level and for groups of employees, and can take into account different kinds of quality for different stakeholders.