The MG Rover closure and policy response: an evaluation of the Task Force model in the UK

In recent years there have been a number of high-profile plant closures in the UK. In several cases, the policy response has included setting up a task force to deal with the impacts of the closure. It can be hypothesised that task force involving multi-level working across territorial boundaries and tiers of government is crucial to devising a policy response tailored to people's needs and to ensuring success in dealing with the immediate impacts of a closure. This suggests that leadership, and vision, partnership working and community engagement, and delivery of high quality services are important. This paper looks at the case of the MG Rover closure in 2005, to examine the extent to which the policy response to the closure at the national, regional and local levels dealt effectively with the immediate impacts of the closure, and the lessons that can be learned from the experience. Such lessons are of particular relevance given the closure of the LDV van plant in Birmingham in 2009 and more broadly – such as in the case of the downsizing of the Opel operation in Europe following its takeover by Magna.

Introducing a pilot data collection model for real-time evaluation of data redundancy

In order to reduce serious health incidents, individuals with high risks need to be identified as early as possible so that effective intervention and preventive care can be provided. This requires regular and efficient assessments of risk within communities that are the first point of contacts for individuals. Clinical Decision Support Systems CDSSs have been developed to help with the task of risk assessment, however such systems and their underpinning classification models are tailored towards those with clinical expertise. Communities where regular risk assessments are required lack such expertise. This paper presents the continuation of GRiST research team efforts to disseminate clinical expertise to communities. Based on our earlier published findings, this paper introduces the framework and skeleton for a data collection and risk classification model that evaluates data redundancy in real-time, detects the risk-informative data and guides the risk assessors towards collecting those data. By doing so, it enables non-experts within the communities to conduct reliable Mental Health risk triage.