Assessing needs for psychiatric treatment in prisoners: 2. Met and unmet need

BACKGROUND: In a companion paper, we established high levels of psychiatric morbidity in prisoners (Bebbington et al. Soc Psychiatry Psychiatr Epidemiol, 2016). In the current report, we evaluate how this morbidity translates into specific needs for treatment and the consequent implications for services. Mental health treatment needs and the extent to which they had been met were assessed in a representative sample of prisoners in a male and a female prison in London (Pentonville and Holloway). METHODS: Prisoners were sampled at random in a sequential procedure based on the Local Inmate Data System. We targeted equal numbers of male remand, male sentenced, female remand, and female sentenced prisoners. Following structured assessment of psychosis, common mental disorders, PTSD, personality disorders and disorders of abuse, we used the MRC Needs for Care Assessment (NFCAS) to establish whether potential needs for care in ten areas of mental health functioning were met, unmet, or incapable of being met by services. RESULTS: Data on treatment experience were provided by 360 inmates. Eighty percent of females and 70% of males had at least one need for treatment. Over half (53.7%) of the needs of female prisoners were met, but only one third (36.5%) in males. Needs for medication were unmet in 32% of cases, while those for psychological treatment were unmet in 51%. CONCLUSIONS: Unmet needs for mental health treatment and care were common in the two prisons. This has adverse consequences both for individual prisoners and for the effective functioning of the criminal justice system

Assessing needs for psychiatric treatment in prisoners: 1. Prevalence of disorder

BACKGROUND: High levels of psychiatric morbidity in prisoners have important implications for services. Assessing Needs for Psychiatric Treatment in Prisoners is an evaluation of representative samples of prisoners in a male and a female prison in London. This paper reports on the prevalence of mental disorders. In a companion paper, we describe how this translates into mental health treatment needs and the extent to which they have been met. METHODS: Prisoners were randomly sampled in a sequential procedure based on the Local Inmate Data System. We interviewed roughly equal numbers from the following groups: male remand; male sentenced prisoners (Pentonville prison); and female remand; female sentenced prisoners (Holloway prison). Structured assessments were made of psychosis, common mental disorders, PTSD, personality disorder and substance abuse. RESULTS: We interviewed 197 male and 171 female prisoners. Psychiatric morbidity in male and female, sentenced and remand prisoners far exceeded in prevalence and severity than in equivalent general population surveys. In particular, 12% met criteria for psychosis; 53.8% for depressive disorders; 26.8% for anxiety disorders; 33.1% were dependent on alcohol and 57.1% on illegal drugs; 34.2% had some form of personality disorder; and 69.1% had two disorders or more. Moreover, in the year before imprisonment, 25.3% had used mental health services. CONCLUSIONS: These rates of mental ill-health and their similarity in remand and sentenced prisoners indicate that diversion of people with mental health problems from the prison arm of the criminal justice system remains inadequate, with serious consequences for well-being and recidivism

Goal neglect, fluid intelligence and processing speed:Manipulating instruction load and inter-stimulus interval

Goal maintenance is the process where task rules and instructions are kept active to exert their control on behavior. When this process fails, an individual may ignore a rule while performing the task, despite being able to describe it after task completion. Previous research has suggested that the goal maintenance system is limited by the number of concurrent rules which can be maintained during a task, and that this limit is dependent on an individual's level of fluid intelligence. However, the speed at which an individual can process information may also limit their ability to use task rules when the task demands them. In the present study, four experiments manipulated the number of instructions to be maintained by younger and older adults and examined whether performance on a rapid letter-monitoring task was predicted by individual differences in fluid intelligence or processing speed. Fluid intelligence played little role in determining how frequently rules were ignored during the task, regardless of the number of rules to be maintained. In contrast, processing speed predicted the rate of goal neglect in older adults, where increasing the presentation rate of the letter-monitoring task increased goal neglect. These findings suggest that goal maintenance may be limited by the speed at which it can operate

Predictive modelling of the granulation process using a systems-engineering approach

© 2016 Elsevier B.V.The granulation process is considered to be a crucial operation in many industrial applications. The modelling of the granulation process is, therefore, an important step towards controlling and optimizing the downstream processes, and ensuring optimal product quality. In this research paper, a new integrated network based on Artificial Intelligence (AI) is proposed to model a high shear granulation (HSG) process. Such a network consists of two phases: in the first phase the inputs and the target outputs are used to train a number of models, where the predicted outputs from this phase and the target are used to train another model in the second phase to lead to the final predicted output. Because of the complex nature of the granulation process, the error residual is exploited further in order to improve the model performance using a Gaussian mixture model (GMM). The overall proposed network successfully predicts the properties of the granules produced by HSG, and outperforms also other modelling frameworks in terms of modelling performance and generalization capability. In addition, the error modelling using the GMM leads to a significant improvement in prediction