Yes / The complex healthcare system that provides patients with medicines places them at risk when care is transferred between healthcare organisations, for example discharge from hospital. Consequently, under-standing and improving medicines management, particularly at care transfers, is a priority.Objectives: This study aimed to explore the medicines management system as patients experience it and determine differences in the patient-perceived importance of people in the system.Methods: We used a Social Network Analysis framework, collecting ego-net data about the importance of people patients had contact with concerning their medicines after hospital discharge. Single- and multi-level logistic regression models of patients' networks were constructed, and model residuals were explored at the patient level.This enabled us to identify patients' networks with support tie patterns different from the general patterns suggested by the model results. Qualitative data for those patients were then analysed to understand their differing experiences.Results: Networks comprised clinical and administrative healthcare staff and friends and family members.Networks were highly individual and the perceived importance of alters varied both within and between patients. Ties to spouses were significantly more likely to be rated as highly important and ties to community pharmacy staff (other than pharmacists) and to GP receptionists were less likely to be highly rated. Patients with low-value medicines management networks described having limited information about their medicines and alack of understanding or help. Patients with high-value networks described appreciating support and having confidence in staff.Conclusions: Patients experienced medicines management as individual systems within which they interacted with healthcare staff and informal support to manage their treatment. Multilevel models indicated that there are unexplained variables impacting on patients' assessments of their medicines management networks. Qualitative exploration of the model residuals can offer an understanding of networks that do not have the typical range of support ties. / National Institute for Health Research (NIHR) Yorkshire and Humber Patient Safety Translational Research Centre (NIHR Yorkshire and Humber PSTRC)

京都大学 / 新制・課程博士 / 博士(理学) / 甲第24875号 / 理博第4985号 / 新制||理||1712(附属図書館) / 京都大学大学院理学研究科生物科学専攻 / (主査)准教授 MacIntosh Andrew, 教授 岡本 宗裕, 教授 明里 宏文 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DFAM

每一項學術研究進行，其理論基礎都必需要建立於過去已完成的研究之上，因此文獻尋找與探討是進行研究過程非常重要的一個步驟。在數位時代與網際網路的加乘效益之下，改變了過去研究者必需為參考文獻東奔西跑的文獻資料尋找方式，但是卻會造成研究者被許多數位文獻淹沒。借用自網頁分析技術而設計的Google學術搜尋網路工具，能透過已經計算好的文獻權重PaperRank排序使用者所尋找的文獻集合，讓使用者能在數位文獻之中依單篇文獻被引用次數為原則而理出頭緒，但其順序式的排列仍然不能夠揭露出搜尋到的文獻集合裡彼此之間的關聯，其中包括了文獻所使用的關鍵字、作者與參考文獻。為了處理了解文獻中多維度的複雜資料關聯，最好的方式還是依賴人類的視覺化資訊處理能力，特別是當資料量大並且需要在短時間內決策時。 此外使用在文獻分析研究中，學者們使用共同引用(co-citation)、共同作者(co-work)、共同作者引用(co-author)等分析方式，配合延伸自社會網路分析理論中的社會密度(social distance)、關聯層級(social degree)、群(clique)等參數概念，試將複雜的文獻資料有脈絡地按排供參考。僅管此是工作難以機械化且消耗時間的(Börner, Chen , Boyack, 2003)，但是卻能將某一特定領域的發展直覺地呈現出來，如此若能將這些分析方式配合視覺化的呈現，則研究學者便能更進一步了進行大量文獻資料視覺化的分析、探索。 本研究試提出一個新的協助文獻探索平台系統架構，將傳統的文字搜尋轉變為視覺化的資料探索。使用者能透過三種不同的層級的資料：知識本體與關鍵字層、引文網路層及人員網路層，並與呈現的資料互動進一步了解資料間的關聯方式。最後實作視覺化雛型平台，並使用在國家圖書館所提供的博、碩士論文網所提供的論文資料，提供給研究人員探索特定知識領域中新研究方向的探索工具，並能協助研究者能在尚未完瞭解的專業領域之前，能快速地瞭解在該其領域重要文獻的導引平台。 / Paper survey is the most important task for building earnest theories, while researchers conducting academic researches. One must touches the fundamental detail of each theory and track down the develop-path of what achievement have been established by previous researches. Benefit from synergy of information age and document digitalized, it not only reduces the cost of finding reference documents, but also makes researchers suffer from information overwhelming after click single “search it” bottom. Stand in for traditional paper web search methods, new academic paper search technology borrowing from the idea of web search engine calculates the importance of each paper by cited number, and recommends users the most important papers by serial listing. However, serial listing does never spell the relationships of suggesting papers out, but only those results match some specific criteria. Those relationships of papers can be classified into 3 different types: the relations of keywords and references that author used and social relationship of authors like co-author and author co-citation which have been developed to explain the complex citation network structures. Those multi-dimensional relationships are extremely abundant and complex, so there is no better way to deal with but depending on visual data processing within human nature. In this paper, we try to propose a new platform to transform paper search in serial listing, into a visualized explore platform by demonstrating 3 different types of relationship: ontology-keywords, papers-references and personnel-references. End users can fallow the relationships between each difference nodes to explore considerable references, as well as change into different view and interact with existing information by using interactive mechanizes. In order to bring this idea to practical application usage, we build a proto-type platform to show our idea by using data from ETDS (electronic theses and dissertations system) of Ministry of education. We hope sincerely by using this proto-type platform, users can catch the major ideas of specific knowledge domain and researchers can explore acceptable references and even conduct new search topic.

Nonprofit housing organizations primarily exist to address the housing needs of low-income residents, whose housing needs are not sufficiently met by the public or private housing market. NHOs are very similar to private corporations in their size, productivity and commitment to the “bottom line.” However, unlike private firms, NHOs are “mission driven” instead profit-driven corporations. The development of affordable housing in the nonprofit housing sector requires a myriad of financial and non-financial resources. As competition for financial resources intensifies many organizations are adopting strategies as a means to not only reduce organizational uncertainty and sustain them, but also increase or maintain organizational capacity. The evolution of the role of nonprofit organizations coupled with market pressures such as attracting investment, competing for clients, and retaining and hiring skilled employees shapes the need for them to adopt market culture strategies (Salamon, 1999). A key strategy of market culture is collaboration (Frost and Sullivan, 2006). This dissertation study was designed to examine interorganizational relationships between nonprofit housing organizations in the Richmond Metropolitan area, and the influence of organizational characteristics, environmental conditions, and resource availability on an organization’s Level of Collaboration. Furthermore, the study examined the attitudes and perceptions of executive directors of collaboration. The primary research question is: Do nonprofit housing organizations display identifiable patterns of relationships with each other? This study contributes several important findings to furthering the understanding of collaboration within the nonprofit sector, and the relationship between organizational characteristics, environmental conditions, and resource availability and an organization’s Level of Collaboration (interorganizational relationships). Study findings convey that the examination of the network itself using social network analysis is a useful tool for examining relationships and identifying opportunities for collaboration. For this network it revealed that the organizations interact on an informal basis as well as identified the prominent actors are in the network. The findings of this study suggests that there are two key factors that influence nonprofit organizations establishing relationships interorganizational learning and personal characteristics.

No description available.

Dans cette thèse, j'étudie des jeux de données concernant des liens sociaux entre personnes (appels et SMS), leur mobilité ainsi que des informations économiques sur ces personnes, comme leur revenu et leurs dépenses. Les sept travaux couvrent un spectre assez large et apportent des contributions en informatique des réseaux mais aussi en sociologie, économie et géographie. Les questions posées sont très diverses. Comment quantifier la perte d'information causée par une agrégation de flot de liens en série de graphe ? Comment inférer les mouvements des utilisateurs quand on ne connaît que les localisations des utilisateurs aux moments des appels, et que l'on ne détecte donc que les mouvements qui ont eu lieu entre deux appels consécutifs, sans connaître leur nombre ni les instants auxquels ils ont lieu ? Est-il possible de transmettre des SMS dans une région dense en utilisant la densité des téléphones, la mobilité des utilisateurs ainsi que la localité des messages échangés ? Est-il possible de comprendre les inégalités sociales avec une approche Big Data ? Cette dernière question fait l'objet d'une première étude socio-économique approfondie au prisme du Big Data. Il a été possible d'étudier à grande échelle la stratification de la société, l'existence de clubs de riches, la ségrégation spatiale et la structure des dépenses par classe sociale.Au delà de la variété de ces études et de ces nombreuses applications, cette thèse montre que l'analyse de données individuelles riches à l'échelle d'une population permettent de répondre à de nouvelles questions et à d'anciennes hypothèses avec une approche Big Data. Cette thèse tient à mettre l'accent sur la potentialité d'une approche Big Data mais aussi de sa complémentarité avec les approches classiques (modélisation, sociologie avec enquêtes, …). Un effort particulier a été mis dans l'explication des étapes qui amènent aux résultats et dans la prise en compte des biais ce qui est trop souvent négligé. / In this thesis, I have carried out data-driven studies based on rich, large-scale combined data sets including social links between users (calls and SMS), their demographic parameters (age and gender), their mobility and their economic information such as income and spendings. These seven studies bring insights in network science but also in sociology, economy and geography. The questions asked are very diversified. How can one quantify the loss of temporal information caused by the aggregation of link streams into series of graphs? How can one infer mobility of a user from his or her localisations of calls? Is it possible to transmit SMS in a dense region by using the density of phones, the mobility of users and the locality of the messages? How can one quantify and prove empirically the social stratification of the society at a large population scale? I present, for this last question, a first socio-economic study with a data-driven approach. It has been possible to study, at a very large scale, the stratification of the society, the existence of "rich-clubs", the spatial segregation and purchase patterns for each social class. Beyond the variety of studies and their numerous applications, this thesis shows that the analysis of individual rich combined datasets at a large population scale gives the opportunity to answer long-standing hypotheses and to address novel questions. This work not only points out the potentiality of Big Data approach but also its complementarity to classical approaches (modelization, surveys, …). Particular attention was given in order to explain each steps that lead to results and to take into account biases which is too often neglected.

This thesis seeks to identify and explain some technology in order to more fully understand modern communication. This study makes use of metaphoric cluster analysis to examine the technological rhetoric of Nicholas Negroponte.

Globally, the incidence of infection caused by Neisseria gonorrhoeae is the second highest among the bacterial sexually transmitted infections. In Canada, declining rates during the 1990s suggested progress toward curbing gonorrhea; however, those have been increasing since 1999, with rates in Saskatchewan among the highest in the country. Infection can cause serious complications in men and women, and reported resistance to third-generation cephalosporins could lead to potentially untreatable infections. Increased understanding of gonorrhea transmission dynamics, sexual networks, and predictors of antimicrobial resistance development is needed to inform the development of improved approaches to prevention and treatment. The research presented herein draws upon data from Shanghai, China, and Saskatchewan, Canada, to compare and contrast varying epidemiologic approaches to enhancing understanding of gonorrhea in the two settings. Using traditional statistical approaches, multi-level statistical modeling, social network analysis, and dynamic simulation modeling, questions related to sexual behavior, partner presentation, and antimicrobial resistance development are explored. Each technique is evaluated for its potential contribution to overall understanding of the issues related to the ongoing gonorrhea epidemic, globally, and in Saskatchewan. The relative strengths and limitations of the application of the analytical approaches in the different settings are described. Socio-demographic characteristics provided useful indicators of antimicrobial resistant infection among patients with gonorrhea from Shanghai. Further, socio-demographic characteristics were also useful for predicting presentation of a partner for testing and treatment and the use of condoms during intercourse, among this study population. In Saskatchewan, socio-demographic characteristics were useful in predicting coinfection with gonorrhea and chlamydia at the time of diagnosis as well as repeat infection with gonorrhea. Social network analysis of the Saskatchewan dataset provided little additional understanding of the gonorrhea epidemic in the province. This result was largely related to how STI data are collected and stored in the province. The utility of dynamic simulation modeling to investigate the potential impact of antimicrobial resistance in Saskatchewan was also limited due to the same data constraints. However, the insight gained from the model building process and findings from the working model did offer a starting point for conversations around the best ways to postpone the development of antimicrobial resistance in N. gonorrhoeae in Saskatchewan, as well as contribute additional information about how the ways in which STI data are collected and stored in the province considerably restrict the applicability of otherwise powerful epidemiologic tools. With persistently high rates of disease transmission, and the threat of untreatable infections due to antimicrobial resistance, N. gonorrhoeae remains a substantial public health threat locally and globally. The research presented herein describes various approaches to understanding and controlling this disease, applied in contrasting settings. There are a wide variety of elements that should be considered when choosing the appropriate tool(s) to address gonorrhea in a given population; there is no “one size fits all” solution. The local epidemiology of disease, cultural and behavioural norms, the characteristics of the notifiable disease reporting and information systems, and the availability of suitable data all affect the relative strengths and weaknesses of the available analytic methods and disease control approaches.

This thesis considers the problem of how to design an industrial network to reduce cost, increase efficiency, and reduce environmental burdens. A recent approach is further developed that uses analogies with biological food webs to guide industry design. Studying ecological food webs shows that among the metrics in use, critical quantities of interest for industry design include the internal cycling of energy, the ratio of producers to consumers, and the ratio of efficiency to redundancy in the network. Metrics that are calculated using flow based information are also introduced for use in industry, a significant step forward for bio-inspired network design. A comprehensive data set of proposed, operational, and failed eco-industrial parks is compiled for use with structural food web analyses. A data set of biological food webs is also assembled to calculate sustainable benchmark values used as goals for the industrial designs. This research an essential difficulty in bio-inspired design approaches by quantitatively analyzing components of food web design by reconstructing found relationships from science and engineering 1st principles, specifically using thermodynamic 1st law efficiency. Results from this work have the potential to provide industry-wide cost savings, increase efficiency, and reduce environmental burdens through a reduction in raw material consumption and waste disposal. The results also support the view that financial competitiveness and sustainability need not be mutually exclusive: using food web network patterns embodying both economically and environmentally desirable properties, biologically redesigned industrial networks can ease both environmental and economic burdens.

Thesis (MSc)--Stellenbosch University, 2011 / ENGLISH ABSTRACT: With billions of current internet users interacting through social networks, the need has arisen to analyze the structure of these networks. Many authors have proposed random graph models for social networks in an attempt to understand and reproduce the dynamics that govern social network development. This thesis proposes a random graph model that generates social networks using a community-based approach, in which users’ affiliations to communities are explicitly modeled and then translated into a social network. Our approach explicitly models the tendency of communities to overlap, and also proposes a method for determining the probability of two users being connected based on their levels of commitment to the communities they both belong to. Previous community-based models do not incorporate community overlap, and assume mutual members of any community are automatically connected. We provide a method for fitting our model to real-world social networks and demonstrate the effectiveness of our approach in reproducing real-world social network characteristics by investigating its fit on two data sets of current online social networks. The results verify that our proposed model is promising: it is the first community-based model that can accurately reproduce a variety of important social network characteristics, namely average separation, clustering, degree distribution, transitivity and network densification, simultaneously. / AFRIKAANSE OPSOMMING: Met biljoene huidige internet-gebruikers wat deesdae met behulp van aanlyn sosiale netwerke kommunikeer, het die analise van hierdie netwerke in die navorsingsgemeenskap toegeneem. Navorsers het al verskeie toevalsgrafiekmodelle vir sosiale netwerke voorgestel in ’n poging om die dinamika van die ontwikkeling van dié netwerke beter te verstaan en te dupliseer. In hierdie tesis word ’n nuwe toevalsgrafiekmodel vir sosiale netwerke voorgestel wat ’n gemeenskapsgebaseerde benadering volg, deurdat gebruikers se verbintenisse aan gemeenskappe eksplisiet gemodelleer word, en dié gemeenskapsmodel dan in ’n sosiale netwerk omskep word. Ons metode modelleer uitdruklik die geneigdheid van gemeenskappe om te oorvleuel, en verskaf ’n metode waardeur die waarskynlikheid van vriendskap tussen twee gebruikers bepaal kan word, op grond van hulle toewyding aan hulle wedersydse gemeenskappe. Vorige modelle inkorporeer nie gemeenskapsoorvleueling nie, en aanvaar ook dat alle lede van dieselfde gemeenskap vriende sal wees. Ons verskaf ’n metode om ons model se parameters te pas op sosiale netwerk datastelle en vertoon die vermoë van ons model om eienskappe van sosiale netwerke te dupliseer. Die resultate van ons model lyk belowend: dit is die eerste gemeenskapsgebaseerde model wat gelyktydig ’n belangrike verskeidenheid van sosiale netwerk eienskappe, naamlik gemiddelde skeidingsafstand, samedromming, graadverdeling, transitiwiteit en netwerksverdigting, akkuraat kan weerspieël.