A Assistência Farmacêutica (AF) é um conceito de prática profissional onde o usuário que precisa do medicamento é o mais importante beneficiário das ações do farmacêutico e dos demais profissionais de saúde que atendem este serviço, ou seja, o usuário é o centro da atenção destes profissionais. Ela, entretanto, não pode ser visualizada apenas pela função de dispensar medicamentos, mas sim como um serviço que complementa as ações em saúde, com um enfoque amplo, incorpora a multidisciplinaridade e a integralidade. O sucesso deste processo depende da participação das diferentes classes de profissionais da saúde, além dos usuários. Este trabalho tem como objetivo analisar os papéis, as formas de comunicação e os fluxos de informação que influenciam nas operações multiprofissionais de atendimento ao usuário na Assistência Farmacêutica. Para tanto, foi escolhido o município de Arroio do Tigre/RS onde foram entrevistados os médicos, enfermeiros, técnicos de enfermagem, farmacêuticos, agentes de saúde, dentistas, psicólogos e assistentes sociais envolvidos na rede multiprofissional de Assistência Farmacêutica do município. Os dados obtidos nas entrevistas foram analisados qualitativamente, com o apoio de planilhas no software Excel para identificação de padrões entre os respondentes. Os fluxos de comunicação/informação foram analisados com o auxílio da ferramenta de análise de redes sociais, o NodeXL. O estudo permitiu observar que há uma necessidade de melhoria da comunicação e do fluxo de informação existente entre os profissionais além de um aumento da clareza do conceito do sistema entre os envolvidos na rede de atendimento ao usuário, esclarecendo as responsabilidades e papéis de cada um dentro da rede para uma gestão de atendimento na AF mais eficaz. / The Pharmaceutical Services consists of a set of people with different relations among them, exchanging lots of information on a daily basis. Thus, it cannot be seen as just a function of dispensing drugs, but as a resource complementing healthcare actions, with a wide approach that incorporates a multidisciplinary and integrality. The success of this process depends on the participation of different classes of healthcare professionals, besides users. The aim of this paper is to study the roles, the forms of communication and information flows that influence multidisciplinary operations of service to users in Pharmaceutical Services. Therefore, the city of Arroio do Tigre/RS was chosen and interviews occurred with professionals such as doctors, nurses, nursing technicians, pharmacists, health workers, dentists, psychologists and social workers involved in the city’s Pharmaceutical Services multidisciplinary network. The data obtained from the interviews were qualitatively analyzed, with the support of Microsoft Excel’s spreadsheets for the identification of patterns among the interviewed. The communication/information flows were analyzed with the aid of NodeXL, a social network analysis tool. The study concluded that there is a need of communication and information flow improvement as well as a more clarity regarding the system concept among the professionals involved in the user service network, clarifying the accountabilities and roles of each one inserted in the network for a more effective drug management.

This dissertation is comprised of three papers that analyze the relationship between political money, elite interests and policies. Individual papers in this work are connected through this overarching theme and the methodology that is used. Each paper employs statistical methods on large-scale datasets with an emphasis on network analysis. The first paper investigates the relationship between the strength of elite connections and the success of renewable energy and emission reduction policies. Based on an original dataset created from social media accounts of the ministers in 34 countries, this analysis uses a stochastic block model and modularity analysis to compare the strength of connections between different types of elites. The quantitative analysis is complemented by in-depth interviews conducted in seven European countries. The second paper explores the relationship between socio-political capital of state-level American politicians and their agenda holding power in legislation. Using a very extensive dataset on campaign contribution records and state-level bill proposals in the United States, this paper employs survival analysis to explore the aforementioned connection. The third paper is a quantitative description of the large datasets on federal- and state-level campaign contribution records and state-level bill proposals. Using visualization, network analysis, and clustering, the last part of the dissertation uncovers some of the connections between big political donors, parties, private sector, and legislation. The last paper in the dissertation also contains a typological identification section for donors and lawmakers. The goal of the dissertation is to expand the literature on elites, to explore what new stories can be told about political money in the United States, and to make use of large-scale datasets for more conclusive arguments in American politics and policy literature.

Lee, Kwan Ho = 探討網絡上的若干動態過程 / 李鈞浩. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 114-118). / Abstracts in English and Chinese. / Lee, Kwan Ho = Tan tao wang luo shang de ruo gan dong tai guo cheng / Li Junhao. / Chapter 1 --- Overview --- p.1 / Chapter 2 --- Networks --- p.5 / Chapter 2.1 --- Describing Networks --- p.5 / Chapter 2.1.1 --- Adjacency Matrix --- p.6 / Chapter 2.1.2 --- "Degree, Degree Distribution and Mean Degree" --- p.6 / Chapter 2.1.3 --- Clustering Coefficient --- p.7 / Chapter 2.1.4 --- "Shortest Path, Shortest Distance and Diameter" --- p.7 / Chapter 2.1.5 --- Betweenness --- p.9 / Chapter 2.2 --- Barabasi-Albert (BA) Network --- p.10 / Chapter 2.2.1 --- Construction of BA Network --- p.10 / Chapter 2.2.2 --- Analytical Study of Degree Distribution --- p.11 / Chapter 2.2.3 --- Numerical Study of Degree Distribution --- p.12 / Chapter 2.2.4 --- Shortest Distance --- p.13 / Chapter 2.3 --- Summary --- p.14 / Chapter 3 --- Routing in Networks: A Review --- p.16 / Chapter 3.1 --- Introduction --- p.16 / Chapter 3.2 --- Dijkstra´ةs Algorithm --- p.17 / Chapter 3.2.1 --- Algorithm --- p.17 / Chapter 3.2.2 --- Running Time --- p.18 / Chapter 3.2.3 --- Routing Table Based on Shortest Path Algorithm --- p.19 / Chapter 3.3 --- Routing Model --- p.19 / Chapter 3.3.1 --- General Setup --- p.19 / Chapter 3.3.2 --- Phase Transition and Evaluation of Network Performance --- p.20 / Chapter 3.4 --- Using Shortest Path as Routing Algorithm on BA Networks --- p.21 / Chapter 3.5 --- Other Routing Algorithms --- p.22 / Chapter 3.5.1 --- Efficient Path --- p.23 / Chapter 3.5.2 --- Routing based on Local Structural Information --- p.24 / Chapter 3.5.3 --- Routing based on Dynamical Information --- p.25 / Chapter 3.6 --- Summary --- p.26 / Chapter 4 --- Optimization of Routing Efficiency through Redistributing Limited Resources --- p.28 / Chapter 4.1 --- A Reallocation Rule - Short to Long (S2L) --- p.29 / Chapter 4.2 --- Performance Enhancement After Applying S2L --- p.33 / Chapter 4.3 --- Optimized Capability Distribution in Detail --- p.36 / Chapter 4.4 --- Summary --- p.44 / Chapter 5 --- N-person Evolutionary Snowdrift Game: A Review --- p.47 / Chapter 5.1 --- Snowdrift Game (SG) and Evolutionary Snowdrift Game (ESG) --- p.47 / Chapter 5.2 --- N-person Evolutionary Snowdrift Game --- p.49 / Chapter 5.2.1 --- Payoffs of C-character and D-character Agents --- p.49 / Chapter 5.2.2 --- Replicator Dynamics --- p.50 / Chapter 5.2.3 --- Numerical Simulations --- p.52 / Chapter 5.3 --- Summary --- p.55 / Chapter 6 --- NESG on Complex Network --- p.56 / Chapter 6.1 --- Models --- p.57 / Chapter 6.2 --- Results of Model A (varying N) --- p.58 / Chapter 6.2.1 --- Correlation of Characters in Degree --- p.60 / Chapter 6.2.2 --- Correlation of Characters in Distance --- p.62 / Chapter 6.3 --- Results of Model B (Fixed-N) --- p.63 / Chapter 6.3.1 --- Correlation of Characters in Degree --- p.64 / Chapter 6.4 --- Summary --- p.69 / Chapter 7 --- Synchronization: A Review --- p.71 / Chapter 7.1 --- Kuramoto Model --- p.72 / Chapter 7.1.1 --- Analytical Method --- p.74 / Chapter 7.1.2 --- Numerical Method --- p.78 / Chapter 7.1.3 --- Summary of Kuramoto Model --- p.81 / Chapter 7.2 --- Integrate-and-Fire Model --- p.81 / Chapter 8 --- Kuramoto Model with Spatially Distributed Oscillators --- p.84 / Chapter 8.1 --- Model --- p.84 / Chapter 8.2 --- Numerical Results --- p.85 / Chapter 8.3 --- Analytic Results --- p.87 / Chapter 8.4 --- Summary --- p.90 / Chapter 9 --- Integrate-Fire-and-Run Model --- p.92 / Chapter 9.1 --- Model --- p.92 / Chapter 9.2 --- Two-Body System --- p.94 / Chapter 9.2.1 --- Case I: Oscillators A and B are in different sites --- p.94 / Chapter 9.2.2 --- Case II: Oscillators A and B are in the same site --- p.101 / Chapter 9.2.3 --- Comparison of Analytic Results and Numerical Simulations of Two-Body system --- p.103 / Chapter 9.3 --- N-Body System --- p.105 / Chapter 9.4 --- Summary --- p.111 / Chapter 10 --- Outlook --- p.112 / Bibliography --- p.114

Executive function describes high-level cognitive-abilities including planning, decision-making, set switching and response inhibition. Impairments of the executive functions in disease states may be subtle but can greatly reduce the quality of life and independence. The overarching theme of this project was to investigate the network of brain regions that are needed to support executive function. This was undertaken using a two-fold approach: one, to apply network analysis to resting state functional Magnetic Resonance Imaging (rs-fMRI) and Diffusion Tensor Imaging (DTI) data in order to describe how differences in morphometry and connectivity correlate to executive function differences of individuals with Mild Cognitive Impairment (MCI), and two, to describe the brain networks involved in one form of executive function, decision-making under uncertain conditions, in young, healthy individuals. Impaired decision-making can dramatically impact day-to-day functioning and understanding the underlying network of regions that support this task can provide a target for future intervention studies. Data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) were used in the studies of MCI. Individuals were grouped by their executive abilities. A regions-of-interest approach was used to parcel and label various brain regions and a network of connections was constructed out of these regions. Differences between the networks were then compared between the MCI subjects with good and poor executive functions. Those with high executive abilities showed decreased functional network connectivity and increased structural network connectivity. The second arm of these studies was based an original decision-making paradigm that was used to compare of networks involved in decision-making at times of uncertainty in healthy young individuals using both electroencephalography (EEG) and task-based functional magnetic resonance imaging (fMRI). Overall we found greater network connectivity in the uncertain condition of the task than in the certain condition. This suggests that with increased uncertainty comes increased organized connectivity. Taken together, the results of this study re-iterate the notion that cognition depends upon the efficient communication between a network of brain regions rather than on isolated regions. They also highlight the importance of having a well-defined network of nodes and connections for optimal executive functioning.

In a field as old as Classics, it difficult to find truly innovative approaches to literary works that have been studied for millennia, and it only becomes more difficult to find something new to explore in works as fundamental to the field as Marcus Tullius Cicero’s. However, in the burgeoning field of Digital Humanities, new avenues for textual exploration arise even among the over-picked rubble that is the Classical World. Through the use of computer software, we can search through and statistically analyze corpora of massive sizes. This project uses such techniques to perform a mesoanalysis of Cicero’s corpus. Through the use of R and Gephi, I will “read” Cicero’s works from a distance and see a much broader view of his character than I could through a traditional close reading of a few texts. This mesoanalysis includes a stylometric analysis of Cicero’s entire corpus, a sentiment analysis of his orations, and a network analysis of his letters. The sentiment analysis will explore Cicero as a literary figure. Through a hierarchical cluster analysis in R, I will assess not only how his style changes from genre to genre but within a genre (orations) as well. That analysis will close with an exploration of the lexical richness of his works, how it varies from genre to genre and over his lifetime. For the sentiment analysis, I built a lexicon based on Stoic theory, primarily as it is explained in the Tusculunae Disputationes, and Robert Kaster’s work with emotional scripts. After the lexicon was built, I applied it to Cicero’s orations in a method similar to Matthew Jockers’ syuzhet package for R, and I traced his use of sentiment across the speech. I then compared those trajectories to Latin rhetorical theory, especially the theories included in Cicero’s own treatises, in order to see if Cicero had put into effect his own advice or if he had a few techniques that he kept hidden. The mesoanalysis closes with a network analysis of the Epistulae ad Familiares. I merged Cicero’s social network with a sentiment analysis in order to assess how Cicero felt about and interacted with his peers. From this analysis, one could gather an idea of Cicero as a person. At the end of the mesoanalysis, we can attain a much broader sense of Cicero’s character. This project also has a second aim, and that is to explain how these techniques could be applied to other literary corpora, outside of Cicero’s and Latin. I have carefully detailed my process and provide more instruction in my appendices so that readers could attempt these analyses and be successful in them.

Applying data analytics for talent acquisition and retention has been identified as one of the most urgent challenges facing HR leaders around the world; however, it is also one of the challenges that firms are least prepared to tackle. Our research strives to narrow such a capability gap between the urgency and readiness of data-driven human resource management. First, we predict interfirm competitors for human capital in the labor market utilizing the rich information contained in over 89,000 LinkedIn users' profiles. Using employee migrations across firms, we derive and analyze a human capital flow network. We leverage this network to extract global cues about interfirm human capital overlap through structural equivalence and community classification. The online employee profiles also provide rich data on the explicit knowledge base of firms and allow us to measure the interfirm human capital overlap in terms of similarity in their employees' skills. We validate our proposed human capital overlap metrics in a predictive analytics framework using future employee migrations as an indicator of labor market competition. The results show that our proposed metrics have superior predictive power over conventional firm-level economic and human resource measures. Second, we estimate the effect of skilled immigrants on the native U.S. workers' turnover probability. We apply unsupervised machine learning to categorize employees' self-reported skills and find that skilled immigrants disproportionately specialize in IT. In contrast, the native workers predominantly focus on management and analyst skills. Utilizing the randomness in the H-1B visa lottery system and a 2SLS design, we find that a 1 percentage point increase in a firm's proportion of skilled immigrant employees leads to a decrease of 0.69 percentage points in a native employee's turnover risk. However, this beneficial crowding-in effect varies for native workers with different skills. Our methodology highlights the need to account for a multifaceted view of the skilled immigration's effect on native workers. Finally, we also propose a set of features and models that are able to effectively predict future employee turnover outcomes. Our predictive models can provide significant utility to managers by identifying individuals with the highest turnover risks.

The following dissertation comprises three studies that aim to better understand organizational identity in the field of U.S. higher education. Research in this area has focused largely on attributes that emphasize college and university distinctiveness, though a good deal of studies in higher education have found that many postsecondary institutions are very alike in their structure and behavior. On the other hand, qualitative research demonstrates that organizational identity helps to explain differences between colleges and universities. The studies herein conceptualize organizational identity as a series of claims about self that balance both sameness and distinctiveness. These studies analyze how organizational identity establishes an institution’s group membership, while distinguishing the institution from colleges and universities in other groups. Manuscripts analyze how organizational identity is associated with action using institutions’ self-selected comparison groups. The first study examines how institutions’ identity claims and aspirational identity claims, measured through their comparison groups, are associated with future action. The second study applies social network analysis techniques to identity communities of colleges and universities that are routinely nominated together. With specific focus on public comprehensive institutions (CIs), this analysis finds salient institutional groups that draw on a common set of organizing principles. CIs, for example, tend to enroll diverse student bodies and maintain low tuition prices when compared with other types of postsecondary institutions. The final study investigates how these common organizing principles influence organizational action in response to their environments. Findings suggest that under the same conditions, CIs enroll more students from low-income backgrounds as a share of their undergraduate bodies when compared with public research universities.

The goal of this study was to understand how a secondary principal uses a distributed perspective of leadership to support informal teacher leaders (ITLs) to improve classroom technology integration. Using a phenomenological lens, I employed a multi-site case study to inform the research goals. A conceptual framework based on Bandura’s (1977) social learning theory and Wenger’s (1998) communities of practice theory was used to guide the study’s methods and data collection. Data were collected in three phases. At each site, the first phase consisted of a digital survey with only closed-ended questions that was administered to all classroom teachers. The survey was analyzed using social network analysis to identify the ITLs at each school. During the second phase, individual interviews with the ITLs and the principal as well as a follow-up focus group interview with ITLs at each school were conducted. During the final phase of data collection, I observed the ITLs at work to understand how they embodied informal teacher leadership. Analyses of diverse data revealed how a principal influences the nature of informal teacher leadership in a school. Findings revealed that principals establish cultural expectations using teacher voice in leadership decisions, modeling the effective use of education technology, providing in-school and out-of-school leadership opportunities for ITLs, and establishing expectations for all teachers to assume roles of instructional leadership. It was clear in this study that although principals are not directly connected to the informal leading and learning network that occurs in a school, they indirectly influence the informal network by establishing school-wide cultural expectations for informal teacher leadership and by personally interacting with the ITLs.

Attention-deficit/hyperactivity disorder (ADHD) is one of the most prevalent and impairing childhood disorders (5%; American Psychiatric Association, 2013), yet it is often studied in isolation. Such an approach is at odds with the clinical reality, where ADHD has a high comorbidity with oppositional defiant disorder, anxiety, and depression (Jensen, Martin, & Cantwell, 1997). Based on the possible presentations of ADHD with both externalizing and internalizing symptoms, there may be differences in associated characteristics, areas of impairment, and resulting assessment interventions. Therefore, the present study investigated how ADHD comorbidities manifested in a population of 233 elementary age children and how these profiles varied in already established characteristics (i.e., traits, social behaviors) and areas of deficit for children with ADHD (i.e., social functioning, academics, narrative comprehension). Characteristics and outcomes were examined using rating scales, behavior observations, laboratory tasks, and grades. Based on latent profile analyses, different patterns of comorbidity were identified using both parent and teacher ratings of ADHD. Based on parent and teacher report, those with high ADHD/ODD symptoms had more negative characteristics and outcomes. Network analyses corroborated these results, showing that internalizing symptoms were less relevant for associated characteristics and outcomes compared to ADHD and ODD symptoms. Overall, these results suggest that ADHD comorbidities may be primarily driven by ADHD and ODD symptoms, with this profile displaying more severe negative characteristics and outcomes.

Callous – unemotional (CU) traits are a key factor in understanding the persistence and severity of conduct problems. The factor structure of CU traits has been primarily examined through confirmatory factor analysis (CFA) in childhood and adolescent samples, yet little research has examined the structure of CU traits in preschool. Further, current CFA models have yielded poor – to – marginally acceptable fit, suggesting the need for a more nuanced approach in understanding the structure of CU traits in early childhood using an interitem approach (i.e., network analysis). Within a sample of 109 preschool children (M age = 4.77, SD = 1.10), CFA results supported a two – factor structure of the ICU, comprised of “callous” and “uncaring” factors. Results of the network analysis identified seems cold and uncaring as most central to the CU network. Results from the CFA demonstrated that CU traits can be assessed in preschool children using 12 of the original 24 items from the ICU, which is consistent with a small portion of research. Further, results of the network analysis suggested that seems cold and uncaring may be useful in screening for psychopathic traits in preschool children. Clinical implications, including ICU measure refinement, are explored.