This is a discussion paper which examines the impact of austerity policies on the provision of mental health services in the United Kingdom. Austerity is a shorthand for a series of policies introduced by the Conservative and Liberal Democrat Coalition government in the UK from 2010 onwards. In response to the fiscal crisis following the bail out of the banks in 2008, it was argued that significant reductions in public spending were required. The background to these policies is examined before a consideration of their impact on mental health services. These policies had a disproportionate impact on people living in poverty. People with health problems including mental problems are overrepresented in this group. At the same time, welfare and community services are under increasing financial pressures having to respond to increased demand within a context of reduced budgets. There is increasing recognition of the role that social factors and adverse childhood experiences have in the development and trajectory of mental health problems. Mental health social workers, alongside other professionals, seek to explain mental distress by the use of some variant of a biopsychosocial model. The extent of mental health problems as a one of their measures of the impact of inequality. More unequal societies create greater levels of distress. There is a social gradient in the extent of mental health problems—the impact of severe mental illness means that many individuals are unable to work or, if they can return to work, they find it difficult to gain employment because of discrimination. The paper concludes that austerity and associated policies have combined to increase the overall burden of mental distress and marginalisation within the UK.

In his recent report, Lord Adebowale (2013) described mental health issues as "core police business". The recent retrenchment in mental health and wider public services mean that the demands on the police in this area are likely to increase. Mental health triage is a concept that has been adapted from general and mental health nursing for use in a policing context. The overall aim of triage is to ensure more effective health outcomes and the more effective use of resources. This article examines the current policy and practice in this area. It then goes on to explore the models of mental health triage that have been developed to try and improve working between mental health services and the police.

One effect of the policy of deinstitutionalisation has been to increase police contact with people, who are experiencing the effects of acute mental illness. Policy documents such as Home Office circular 66/90 recognise that adults with mental health problems are especially vulnerable within the criminal justice system. The overall aim of policy is that vulnerable adults should be diverted to mental health services at the earliest opportunity unless the offence is so serious that this would not be in the public interest. However, there is little concrete evidence of the success of this policy. The result is that police officers have an increasing role to play in working with individuals experiencing acute mental health problems. In this process, custody officers have a key role to play as decision‐makers as to whether the protections that PACE (1984) offers to vulnerable adults should apply. This article is based on a small‐scale indicative research study, which examined how officers make these decisions and the training that they receive relating to mental health issues.

Loic Wacquant is currently Professor of Sociology at the University of California, Berkeley. He has written extensively on issues related to urban poverty, race and the expansion of imprisonment. Wacquant is heavily influenced by the work of the late Pierre Bourdieu. Specifically, Wacquant employs Bourdieu's theoretical tools of analysis to provide a critique of contemporary neo-liberal social and penal policy. This article considers the potential applications of Wacquant's scholarship to contemporary social work practice. For the purposes of this analysis, Wacquant's work is divided into three broad areas: the analysis of neo-liberalism and precarious forms of employment, the development of the penal state and his critical approach to doxa. Bourdieu uses the term doxa to refer to those views or opinions that are taken for granted within any society. They thus create the limits of, or provide a strong framework for, political and policy debates. It is argued that Wacquant's theorisation provides an explanation of the forces that have led to the concentration of areas of poverty in the midst of relative affluence. In addition to facing long-standing problems of high unemployment, poor housing and a lack of social amenities, these areas -the banlieues in France, housing projects in the USA and estates in England -are stigmatized in public and media discourse. Wacquant's work can be used to challenge the development of a form of social work that places emphasis on bureaucratic managerialism. In addition, it should encourage social work as a profession to re-engage with criminal justice issues. Finally, the critical approach to doxa provides a model for social work to challenge the limitations of current debates.

Wireless sensor networks have become incredibly popular due to the Internet of Things' (IoT) rapid development. IoT routing is the basis for the efficient operation of the perception-layer network. As a popular type of machine learning, reinforcement learning techniques have gained significant attention due to their successful application in the field of network communication. In the traditional Routing Protocol for lowpower and Lossy Networks (RPL) protocol, to solve the fairness of control message transmission between IoT terminals, a fair broadcast suppression mechanism, or Drizzle algorithm, is usually used, but the Drizzle algorithm cannot allocate priority. Moreover, the Drizzle algorithm keeps changing its redundant constant k value but never converges to the optimal value of k. To address this problem, this paper uses a combination based on reinforcement learning (RL) and trickle timer. This paper proposes an RL Intelligent Adaptive Trickle-Timer Algorithm (RLATT) for routing optimization of the IoT awareness layer. RLATT has triple-optimized the trickle timer algorithm. To verify the algorithm's effectiveness, the simulation is carried out on Contiki operating system and compared with the standard trickling timer and Drizzle algorithm. Experiments show that the proposed algorithm performs better in terms of packet delivery ratio (PDR), power consumption, network convergence time, and total control cost ratio.