Evaluation of the Primary Care Mental Health Specialist role: Final Report

This report details an evaluation to assess the impact of the new primary care mental health specialist (PCMHS) role in Kent and Medway. The evaluation was undertaken by the Centre for Health Services Studies (CHSS) at the University of Kent and was conducted June 2013 to December 2014. The evaluation was commissioned by NHS Kent and Medway and supported by Kent and Medway Commissioning Support. The evaluation encompasses six CCG areas across Kent and Medway, with 13 PCMHS employed in these areas (see Table 1-1 for breakdown). The number of posts per CCG is dependent on the amount CCGs invest (roughly equating to population size), rather than prevalence of illness. The PCMHS have been seconded from Kent and Medway NHS and Social Care Partnership Trust (KMPT) for the duration of the pilot, and are either community psychiatric nurses (CPN) or occupational therapists (OT) by profession. The majority of PCMHS are hosted by a voluntary organisation (mcch); three are hosted by GP practices and two by a community Interest Company, Invicta CIC. The main objectives of the evaluation are: 1. To assess the impact on patients by capturing their experience of the service; 2. To assess the impact by capturing experiences of those delivering the service (i.e., PCMHS); 3. To assess the impact by capturing experiences of other professions who work alongside the service (i.e., mental health professionals in secondary care, GPs); 4. To assess the economic cost of the new service via a unit cost analysis

Large-scale image collection cleansing, summarization and exploration

A perennially interesting topic in the research field of large scale image collection organization is how to effectively and efficiently conduct the tasks of image cleansing, summarization and exploration. The primary objective of such an image organization system is to enhance user exploration experience with redundancy removal and summarization operations on large-scale image collection. An ideal system is to discover and utilize the visual correlation among the images, to reduce the redundancy in large-scale image collection, to organize and visualize the structure of large-scale image collection, and to facilitate exploration and knowledge discovery. In this dissertation, a novel system is developed for exploiting and navigating large-scale image collection. Our system consists of the following key components: (a) junk image filtering by incorporating bilingual search results; (b) near duplicate image detection by using a coarse-to-fine framework; (c) concept network generation and visualization; (d) image collection summarization via dictionary learning for sparse representation; and (e) a multimedia practice of graffiti image retrieval and exploration. For junk image filtering, bilingual image search results, which are adopted for the same keyword-based query, are integrated to automatically identify the clusters for the junk images and the clusters for the relevant images. Within relevant image clusters, the results are further refined by removing the duplications under a coarse-to-fine structure. The duplicate pairs are detected with both global feature (partition based color histogram) and local feature (CPAM and SIFT Bag-of-Word model). The duplications are detected and removed from the data collection to facilitate further exploration and visual correlation analysis. After junk image filtering and duplication removal, the visual concepts are further organized and visualized by the proposed concept network. An automatic algorithm is developed to generate such visual concept network which characterizes the visual correlation between image concept pairs. Multiple kernels are combined and a kernel canonical correlation analysis algorithm is used to characterize the diverse visual similarity contexts between the image concepts. The FishEye visualization technique is implemented to facilitate the navigation of image concepts through our image concept network. To better assist the exploration of large scale data collection, we design an efficient summarization algorithm to extract representative examplars. For this collection summarization task, a sparse dictionary (a small set of the most representative images) is learned to represent all the images in the given set, e.g., such sparse dictionary is treated as the summary for the given image set. The simulated annealing algorithm is adopted to learn such sparse dictionary (image summary) by minimizing an explicit optimization function. In order to handle large scale image collection, we have evaluated both the accuracy performance of the proposed algorithms and their computation efficiency. For each of the above tasks, we have conducted experiments on multiple public available image collections, such as ImageNet, NUS-WIDE, LabelMe, etc. We have observed very promising results compared to existing frameworks. The computation performance is also satisfiable for large-scale image collection applications. The original intention to design such a large-scale image collection exploration and organization system is to better service the tasks of information retrieval and knowledge discovery. For this purpose, we utilize the proposed system to a graffiti retrieval and exploration application and receive positive feedback

Variable selection for functional index coefficient models and its applications in finance and engineering

Variable selection with a non-concave penalty function has become popular in recent years, since it has ability to select significant variables and to estimate unknown regression coefficients simultaneously. In this dissertation, I consider variable selection in a functional index coefficient model under strong mixing context. My selection procedures with smoothly clipped absolute deviation penalty function consist of two steps. The first is to select significant covariates with functional coefficients and it is then to do variable selection for local significant variables with parametric coefficients. The asymptotic properties such as consistency, sparsity and the oracle property of these two step estimators are established, whereas easy computational algorithms are suggested to highlight the implementation of the proposed procedures. Monte Carlo simulation studies are conducted to examine the finite sample performance of the proposed estimators and selection procedures. Meanwhile, two financial examples including functional index coefficient autoregressive models and functional index coefficient models for the stock return predictability are presented. Finally, two engineering applications of local annual average daily traffic (AADT) estimation and seasonal factors calculation are extensively studied in two chapters respectively

SUPPORT EFFECTIVE DISCOVERY MANAGEMENT IN VISUAL ANALYTICS

Visual analytics promises to supply analysts with the means necessary to ana- lyze complex datasets and make effective decisions in a timely manner. Although significant progress has been made towards effective data exploration in existing vi- sual analytics systems, few of them provide systematic solutions for managing the vast amounts of discoveries generated in data exploration processes. Analysts have to use off line tools to manually annotate, browse, retrieve, organize, and connect their discoveries. In addition, they have no convenient access to the important discoveries captured by collaborators. As a consequence, the lack of effective discovery manage- ment approaches severely hinders the analysts from utilizing the discoveries to make effective decisions. In response to this challenge, this dissertation aims to support effective discov- ery management in visual analytics. It contributes a general discovery manage- ment framework which achieves its effectiveness surrounding the concept of patterns, namely the results of users’ low-level analytic tasks. Patterns permit construction of discoveries together with users’ mental models and evaluation. Different from the mental models, the categories of patterns that can be discovered from data are pre- dictable and application-independent. In addition, the same set of information is often used to annotate patterns in the same category. Therefore, visual analytics sys- tems can semi-automatically annotate patterns in a formalized format by predicting what should be recorded for patterns in popular categories. Using the formalized an- notations, the framework also enhances the automation and efficiency of a variety of discovery management activities such as discovery browsing, retrieval, organization, association, and sharing. The framework seamlessly integrates them with the visual interactive explorations to support effective decision making. Guided by the discovery management framework, our second contribution lies in proposing a variety of novel discovery management techniques for facilitating the discovery management activities. The proposed techniques and framework are im- plemented in a prototype system, ManyInsights, to facilitate discovery management in multidimensional data exploration. To evaluate the prototype system, two long- term case studies are presented. They investigated how the discovery management techniques worked together to benefit exploratory data analysis and collaborative analysis. The studies allowed us to understand the advantages, the limitations, and design implications of ManyInsights and its underlying framework

“I don’t bother with the phone!”: Feeling Closer to Physician using Secure Messaging

This study explores the use of phone and secure messaging via an online patient portal in mediating the communication between patients and their healthcare providers. In analyzing the messages handling processes, we found that although both phone and secure messages were answered in similar manners, the interplay of the front- and back-end roles in collaborative work resulted in patients’ preference for secure messages in communication as they believed it offered direct and empowered communication experiences. This study offers insights on the choice of how different communication media affect patients’ perception toward the quality of the communication and patient-provider relationship.

Spectroscopic study of optical confinement and transport effects in coupled microspheres and pillar cavities

In this thesis we investigated the spatial and spectral mode profiles, and the optical transport properties of single and multiple coupled cavities. We performed numerical modeling of whispering gallery modes (WGMs) in such cavities in order to explain recent experiments on semiconductor micropillars. High quality (Q up to 20 000) WGMs with small mode volumes V ~0.3 µm3 in 4-5 µm micropillars were reproduced. The WGM spectra were found to be in a good agreement with the experimental data. The coupling between size-matched spheres from 2.9 to 6.0 µm in diameter was characterized using spectroscopy. We observed peculiar kites in the spectral images of such coherently coupled bispheres. The origin of these kites was explained due to the coupling of multiple pairs of azimuthal modes. We quantified the coupling constant for WGMs located in the equatorial plane of spheres parallel to the substrate which plays the most important role in the transport of WGMs in such structures. It was shown that in long (>10 spheres) chains of size-disordered polystyrene microspheres the transmission properties are dominated by photonic nanojet-induced modes (NIMs) leading to periodic focusing of light along the chain. In the transmission spectra of such chains we observed Fabry-Pe´rot fringes with propagation losses of only 0.08 dB per sphere at the maxima of the transmission peaks. The fringes of NIMs are found to be in a good agreement with the results of numerical modeling. These modes can be used in various biomedical applications requiring tight focusing of the beams

Deciphering chemotaxis pathways using cross species comparisons

<p>Abstract</p> <p>Background</p> <p>Chemotaxis is the process by which motile bacteria sense their chemical environment and move towards more favourable conditions. <it>Escherichia coli </it>utilises a single sensory pathway, but little is known about signalling pathways in species with more complex systems.</p> <p>Results</p> <p>To investigate whether chemotaxis pathways in other bacteria follow the <it>E. coli </it>paradigm, we analysed 206 species encoding at least 1 homologue of each of the 5 core chemotaxis proteins (CheA, CheB, CheR, CheW and CheY). 61 species encode more than one of all of these 5 proteins, suggesting they have multiple chemotaxis pathways. Operon information is not available for most bacteria, so we developed a novel statistical approach to cluster <it>che </it>genes into putative operons. Using operon-based models, we reconstructed putative chemotaxis pathways for all 206 species. We show that <it>cheA-cheW </it>and <it>cheR-cheB </it>have strong preferences to occur in the same operon as two-gene blocks, which may reflect a functional requirement for co-transcription. However, other <it>che </it>genes, most notably <it>cheY</it>, are more dispersed on the genome. Comparison of our operons with shuffled equivalents demonstrates that specific patterns of genomic location may be a determining factor for the observed <it>in vivo </it>chemotaxis pathways.</p> <p>We then examined the chemotaxis pathways of <it>Rhodobacter sphaeroides</it>. Here, the PpfA protein is known to be critical for correct partitioning of proteins in the cytoplasmically-localised pathway. We found <it>ppfA </it>in <it>che </it>operons of many species, suggesting that partitioning of cytoplasmic Che protein clusters is common. We also examined the apparently non-typical chemotaxis components, CheA3, CheA4 and CheY6. We found that though variants of CheA proteins are rare, the CheY6 variant may be a common type of CheY, with a significantly disordered C-terminal region which may be functionally significant.</p> <p>Conclusions</p> <p>We find that many bacterial species potentially have multiple chemotaxis pathways, with grouping of <it>che </it>genes into operons likely to be a major factor in keeping signalling pathways distinct. Gene order is highly conserved with <it>cheA-cheW </it>and <it>cheR-cheB </it>blocks, perhaps reflecting functional linkage. CheY behaves differently to other Che proteins, both in its genomic location and its putative protein interactions, which should be considered when modelling chemotaxis pathways.</p

Perfectionism and exam performance: The mediating effect of task-approach goals

Perfectionistic strivings are positively correlated with students’ achievement goals and exam performance. However, so far no study has employed a prospective design investigating whether achievement goals mediate the positive relationship between perfectionistic strivings and exam performance. In the present study, 100 university students completed a measure of self-oriented perfectionism and socially prescribed perfectionism (Hewitt & Flett, 1991) and received a chapter from a textbook to study for 2-4 days. Then they returned to the lab to complete a measure of achievement goals following the 3 x 2 model (Elliot, Murayama, & Pekrun, 2011) and sit a mock exam testing their knowledge of the chapter. Multiple regressions showed that socially prescribed perfectionism negatively predicted exam performance when the overlap with self-oriented perfectionism was controlled for. In contrast, self-oriented perfectionism—a defining indicator of perfectionistic strivings—positively predicted exam performance. Moreover, task-approach goals mediated the positive relationship between self-oriented perfectionism and exam performance. The findings suggest that perfectionistic strivings make students adopt task-approach goals that help them achieve better results on exams

The role of school connectedness and friend contact in adolescent loneliness, and implications for physical health

JI was supported by the Medical Research Council (MC_UU_00022/1) and the Scottish Government Chief Scientist Office (SPHSU16). The HBSC 2013/14 study in Scotland was funded by NHS Health Scotland (now Public Health Scotland).The current study investigated how adolescents' loneliness relates to school connectedness, classmate support, teacher support, and offline and online communication with friends. We also examined the association between loneliness, physical health, and sleep. Data came from the Scottish Health Behavior in School-aged Children (HBSC). The total sample was 2983 adolescents (F = 1479 [49.6%]) aged 14-17 years (M = 15.66, SD = 0.39) from 117 secondary schools in Scotland. Results showed that (1) higher teacher support, classmate support, and offline contact with friends predicted lower levels of loneliness, (2) online friendship engagement predicted higher levels of loneliness, and (3) poor health and sleep were positively associated with loneliness. The study offers new findings, highlighting the role played by classmates/peers and teachers in reducing loneliness. Supporting previous research, we also found associations between loneliness, poor sleep, and worse physical health.Publisher PDFPeer reviewe