The mental health needs of people with a learning disability detained in police custody

Few research studies examine the prevalence or mental health needs of people with a Learning Disability (LD) detained in police custody. This paper describes the population of detainees with an LD who presented to an inner city inter-agency police liaison service during a three-year period. Two forensically trained Community Mental Health Nurses (CMHNs) screened all custody record forms (n=9014) for evidence of a mental health problem or LD. The CMHNs interviewed positively screened detainees (n=1089) using a battery of measures designed to assess mental health status, risk-related behaviour and alcohol or drug abuse. Almost one-in-ten of those interviewed (95/1089) were judged to have a possible or definite LD. Fifty-two per cent were cases on the General Health Questionnaire (GHQ) whilst 61% attained 'above threshold' Brief Psychiatric Rating Scale (BPRS) scores. The majority (63%) had a history of causing harm to others while 56 per cent had a history of self-harm. More than half (56%) regularly consumed harmful levels of alcohol while one-in-four (27%) reported abusing drugs. Higher than expected numbers of detainees have a learning disability and most have complex mental health needs. A police liaison service offers a way of identifying people with LD and connecting them with appropriate health and social care agencies.

Additivity Pretraining and cue competition effects: Developmental evidence for a reasoning account of causal learning

The effect of additivity pretraining on blocking has been taken as evidence for a reasoning account of human and animal causal learning. If inferential reasoning underpins this effect, then developmental differences in the magnitude of this effect in children would be expected. Experiment 1 examined cue competition effects in children's (4- to 5-year-olds and 6- to 7-year-olds) causal learning using a new paradigm analogous to the food allergy task used in studies of human adult causal learning. Blocking was stronger in the older than the younger children, and additivity pretraining only affected blocking in the older group. Unovershadowing was not affected by age or by pretraining. In experiment 2, levels of blocking were found to be correlated with the ability to answer questions that required children to reason about additivity. Our results support an inferential reasoning explanation of cue competition effects. (c) 2012 APA, all rights reserved.

Analysis of pharmacists attitudes toward a distance learning initiative on health screening

A questionnaire was developed to investigate pharmacists' attitudes to distance learning (DL) as a vehicle for continuing education (CE). It was included in each of a two part DL course on Health Screening. Part One was mailed to all community pharmacists in England (16,400) and returns were received from 1487. The questionnaire in Part Two was returned by 436 pharmacists. Attitude statements were scored using a five-point Likert scale. The mean response to all attitude statements was positive. Participants were significantly more satisfied than non-participants with DL in general and the DL course studied (P less than or equal to 0.05). Over 80 percent of respondents completing the course found DL to be enjoyable and more suitable than other CE methods. More females and less males than expected (based on registration statistics) requested (P less than or equal to 0.001) and completed the course (P less than or equal to 0.001). Pharmacists of all ages participated, although those recently qualified showed greater interest.

The link between the learning organisation and curriculum innovation:A comparative study of public and private primary schools in Greece

This article examines the relationship between the learning organisation and the implementation of curriculum innovation within schools. It also compares the extent of innovative activity undertaken by schools in the public and the private sectors. A learning organisation is characterised by long-term goals, participatory decision-making processes, collaboration with external stakeholders, effective mechanisms for the internal communication of knowledge and information, and the use of rewards for its members. These characteristics are expected to promote curriculum innovation, once a number of control factors have been taken into account. The article reports on a study carried out in 197 Greek public and private primary schools in the 1999-2000 school year. Structured interviews with school principals were used as a method of data collection. According to the statistical results, the most important determinants of the innovative activity of a school are the extent of its collaboration with other organisations (i.e. openness to society), and the implementation of development programmes for teachers and parents (i.e. communication of knowledge and information). Contrary to expectations, the existence of long-term goals, the extent of shared decision-making, and the use of teacher rewards had no impact on curriculum innovation. The study also suggests that the private sector, as such, has an additional positive effect on the implementation of curriculum innovation, once a number of human, financial, material, and management resources have been controlled for. The study concludes by making recommendations for future research that would shed more light on unexpected outcomes and would help explore the causal link between variables in the research model.

Machine Learning for Predictive Maintenance: A Multiple Classifiers Approach

In this paper a multiple classifier machine learning methodology for Predictive Maintenance (PdM) is presented. PdM is a prominent strategy for dealing with maintenance issues given the increasing need to minimize downtime and associated costs. One of the challenges with PdM is generating so called ’health factors’ or quantitative indicators of the status of a system associated with a given maintenance issue, and determining their relationship to operating costs and failure risk. The proposed PdM methodology allows dynamical decision rules to be adopted for maintenance management and can be used with high-dimensional and censored data problems. This is achieved by training multiple classification modules with different prediction horizons to provide different performance trade-offs in terms of frequency of unexpected breaks and unexploited lifetime and then employing this information in an operating cost based maintenance decision system to minimise expected costs. The effectiveness of the methodology is demonstrated using a simulated example and a benchmark semiconductor manufacturing maintenance problem.

A self-learning teaching-learning based optimization for dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads

Economic and environmental load dispatch aims to determine the amount of electricity generated from power plants to meet load demand while minimizing fossil fuel costs and air pollution emissions subject to operational and licensing requirements. These two scheduling problems are commonly formulated with non-smooth cost functions respectively considering various effects and constraints, such as the valve point effect, power balance and ramp rate limits. The expected increase in plug-in electric vehicles is likely to see a significant impact on the power system due to high charging power consumption and significant uncertainty in charging times. In this paper, multiple electric vehicle charging profiles are comparatively integrated into a 24-hour load demand in an economic and environment dispatch model. Self-learning teaching-learning based optimization (TLBO) is employed to solve the non-convex non-linear dispatch problems. Numerical results on well-known benchmark functions, as well as test systems with different scales of generation units show the significance of the new scheduling method.

Transposing Musical Skill: Sonification of movement as concurrent augmented feedback enhances learning in a bimanual task

Concurrent feedback provided during acquisition can enhance performance of novel tasks. The ‘guidance hypothesis’ predicts that feedback provision leads to dependence and poor performance in its absence. However, appropriately-structured feedback information provided through sound (‘sonification’) may not be subject to this effect. We test this directly using a rhythmic bimanual shape-tracing task in which participants learned to move at a 4:3 timing ratio. Sonification of movement and demonstration was compared to two other learning conditions: (1) sonification of task demonstration alone and (2) completely silent practice (control). Sonification of movement emerged as the most effective form of practice, reaching significantly lower error scores than control. Sonification of solely the demonstration, which was expected to benefit participants by perceptually unifying task requirements, did not lead to better performance than control. Good performance was maintained by participants in the sonification condition in an immediate retention test without feedback, indicating that the use of this feedback can overcome the guidance effect. On a 24-hour retention test, performance had declined and was equal between groups. We argue that this and similar findings in the feedback literature are best explained by an ecological approach to motor skill learning which places available perceptual information at the highest level of importance.

Person Re-Identification using Deep Convnets with Multi-task Learning

Person re-identification involves recognizing a person across non-overlapping camera views, with different pose, illumination, and camera characteristics. We propose to tackle this problem by training a deep convolutional network to represent a person’s appearance as a low-dimensional feature vector that is invariant to common appearance variations encountered in the re-identification problem. Specifically, a Siamese-network architecture is used to train a feature extraction network using pairs of similar and dissimilar images. We show that use of a novel multi-task learning objective is crucial for regularizing the network parameters in order to prevent over-fitting due to the small size the training dataset. We complement the verification task, which is at the heart of re-identification, by training the network to jointly perform verification, identification, and to recognise attributes related to the clothing and pose of the person in each image. Additionally, we show that our proposed approach performs well even in the challenging cross-dataset scenario, which may better reflect real-world expected performance. 

Bespoke Radiology e-Learning

Introduction: Foundation doctors are expected to assess and interpret plain x-ray studies of the chest/abdomen before a definitive report is issued by senior staff. The Royal College of Radiologists have published guidelines (RCR curriculum) on the scope of plain film findings medical students should be familiar with.1 Studies have shown that the x-ray interpretation without feedback does not significantly improve diagnostic ability. 2 Queen’s University, Belfast Trust Radiology and Experior Medical developed an online system to assess individual student ability to interpret X-ray findings. Over a series of assessments each student’s profile is built up, identifying strengths and weakness. The system can then create bespoke individual assessments re-evaluating previously identified weak areas and quantifying interpretative skill improvement. Aim: To determine how readily an online system is adopted by senior medical students, investigating if increasing exposure to x-ray interpretation combined with cyclical formative feedback enhances performance. Methods: The system was offered to all 270 final year medical students as an online resource. The system comprised a series of 20 weekly 30 minute assessments, containing normal and abnormal x-rays within the RCR curriculum. After each assessment students were given formative feedback, including their own result, annotated answers, peer group comparison and a breakdown of areas of strength and weakness. Focus groups of 4-5 students addressed student perspectives of the system, including ease of use, image resolution, system performance across different operating platforms, perceived value of formative feedback loops, breakdown of performance and the value of bespoke personalised assessments. Research Ethics Approval was granted for the study. Data analysis was via two-sided one-sample t-test; initial minimal recruitment was estimated as 60 students, to detect a mean 10% change in performance, with a standard deviation of 20%. Results and Discussion: Over 80% (n = XXX/270) of the student cohort engaged with the study. Student baseline average was 39%, increasing to 62% by the exit test. The steadily sustained improvement (57% relative performance in interpretative diagnostic accuracy) was despite increasing test difficulty. Student feedback via focus groups was universally positive throughout the examined domains. Conclusion: The online resource proved to be valuable, with high levels of student engagement, improving performance despite increasingly difficulty testing and positive learner experience with the system. References: 1. Undergraduate Radiology Curriculum, The Royal College of Ra, April 2012. Ref No. BFCR(12)4 The Royal College of Radiologists, April 2012 2. I Satia, S Bashagha, A Bibi, R Ahmed, S Mellor, F Zaman. Assessing the accuracy and certainty in interpretating chest x-rays in the medical division. Clin Med August 2013 Vol.13 no. 4 349-352