Group membership, power and social rules: A study of doctor-nurse relationship

The purposes of this research are: (1) to compare the similarides and differences in intra-group and inter-group social rules of hospital doctors and nurses; (2) to compare rule following, rule breaking & tolerance of rule breaking of doctors and nurses with respect to different work reladonships. Professional discipline and idendficadon, ingroup-outgroup membership and reladve status were used as predictors. In-depth interview of 20 doctors and 20 nurses were conducted to elicit social rules and goals. In the second study, 30 rules and 10 goals with high consensus were selected from study one and developed into a quesdonnaire which measured their applicadon to four different work reladonships, namely, padents, peers, seniors and doctors/nurses. Forty-three doctors and one hundred and seven nurses completed this questionnaire. In the third study, the frequency and goals of violation and tolerance of violation of five different social rules were measured. One hundred and thirty-six doctors and one hundred and sixty-six nurses completed the questionnaire.

Size frequency distributions of the florid prion protein aggregates in variant Creutzfeldt-Jakob disease follow a power-law function

The objective was to test the hypothesis that the size frequency distributions of the prion protein (PrP) plaques in cases of variant Creutzfeldt-Jakob disease (vCJD) follow a power-law function. The design was a retrospective neuropathological study. The patients were 11 cases of clinically and neuropathologically verified vCJD. Size distributions of the diffuse and florid-type plaques were measured in several areas of the cerebral cortex and hippocampus from each case and a power-law function fitted to each distribution. The size distributions of the florid and diffuse plaques were fitted successfully by a powerlaw function in 100% and 42% of brain areas investigated respectively. Processes of aggregation/disaggregation may be more important than surface diffusion in the pathogenesis of the florid plaques. By contrast, surface diffusion may be a more significant factor in the development of the diffuse plaques. © Springer-Verlag Italia 2006.

Affect networks:a structural analysis of the relationship between work ties and job-related affect

The relationship between organizational networks and employees' affect was examined in 2 organizations. In Study 1, social network analysis of work ties and job-related affect for 259 employees showed that affect converged within work interaction groups. Similarity of affect between employees depended on the presence of work ties and structural equivalence. Affect was also related to the size and density of employees' work networks. Study 2 used a 10-week diary study of 31 employees to examine a merger of 2 organizational divisions and found that negative changes in employees' affect were related to having fewer cross-divisional ties and to experiencing greater reductions in network density. The findings suggest that affect permeates through and is shaped by organizational networks.

A social psychological analysis of strikes

A participant observation method was employed :in the study of a 20-week stoppage at Ansells Brewery Limited, a constituent company of Allied Breweries (U.K.). The strike, :involving 1,000 workers, began :in opposition to the implementation of a four-day working week and culminated in the permanent closure of the brewery. The three main phases of the strike's development (i.e., its :initiation, maintenance and termination) were analysed according to a social-cognitive approach, based on the psychological imagery, beliefs, values and perceptions underlying the employees' behaviour. Previous psychological treatments of strikes have tended to ignore many of the aspects of social definition, planning and coordination that are an integral part of industrial action. The present study is, therefore, unique in concentrating on the thought processes by which striking workers .make sense of their current situation and collectively formulate an appropriate response. The Ansells strike provides an especially vivid illustration of the ways in which the seminal insights of a small number of individuals are developed, via processes of communication and:influence, into a consensual interpretation of reality. By adopting a historical perspective, it has been possible to demonstrate how contemporary definitions are shaped by the prior history of union-management relations, particularly with regard to: (a) the way that previous events were subjectively interpreted, and (b) the lessons that were learned on the basis of that experience. The present approach is psychological insofar as it deals with the cognitive elements of strike action. However, to the extent that it draws from relevant sections of the industrial relations, organizational behaviour, sociology, anthropology and linguistics literatures, it can claim to be truly interdisciplinary.

Social network theory and the sales manager role:engineering the right relationship flows

Sales leadership research has typically taken a leader-focused approach, investigating key questions from a top-down perspective. Yet considerable research outside sales has advocated a view of leadership that takes into account the fact that employees look beyond a single designated individual for leadership. In particular, the social networks of leaders have been a popular topic of investigation in the management literature, although coverage in the sales literature remains rare. The present paper conceptualizes the sales leadership role as one in which the leader must manage a network of simultaneous relationships; several types of sales manager relationships, such as the sales-manager-to-top-manager and the sales-manager-to-sales manager relationships, have received limited attention in the sales literature to date. Taking an approach based on social network theory, we develop a conceptualization of the sales manager as a "network engineer," who must manage multiple relationships, and the flows between them. Drawing from this model, we propose a detailed agenda for future sales research. © 2012 PSE National Educational Foundation. All rights reserved.

Quantifying short-term dynamics of Parkinson's disease using self-reported symptom data from an internet social network

Background: Parkinson’s disease (PD) is an incurable neurological disease with approximately 0.3% prevalence. The hallmark symptom is gradual movement deterioration. Current scientific consensus about disease progression holds that symptoms will worsen smoothly over time unless treated. Accurate information about symptom dynamics is of critical importance to patients, caregivers, and the scientific community for the design of new treatments, clinical decision making, and individual disease management. Long-term studies characterize the typical time course of the disease as an early linear progression gradually reaching a plateau in later stages. However, symptom dynamics over durations of days to weeks remains unquantified. Currently, there is a scarcity of objective clinical information about symptom dynamics at intervals shorter than 3 months stretching over several years, but Internet-based patient self-report platforms may change this. Objective: To assess the clinical value of online self-reported PD symptom data recorded by users of the health-focused Internet social research platform PatientsLikeMe (PLM), in which patients quantify their symptoms on a regular basis on a subset of the Unified Parkinson’s Disease Ratings Scale (UPDRS). By analyzing this data, we aim for a scientific window on the nature of symptom dynamics for assessment intervals shorter than 3 months over durations of several years. Methods: Online self-reported data was validated against the gold standard Parkinson’s Disease Data and Organizing Center (PD-DOC) database, containing clinical symptom data at intervals greater than 3 months. The data were compared visually using quantile-quantile plots, and numerically using the Kolmogorov-Smirnov test. By using a simple piecewise linear trend estimation algorithm, the PLM data was smoothed to separate random fluctuations from continuous symptom dynamics. Subtracting the trends from the original data revealed random fluctuations in symptom severity. The average magnitude of fluctuations versus time since diagnosis was modeled by using a gamma generalized linear model. Results: Distributions of ages at diagnosis and UPDRS in the PLM and PD-DOC databases were broadly consistent. The PLM patients were systematically younger than the PD-DOC patients and showed increased symptom severity in the PD off state. The average fluctuation in symptoms (UPDRS Parts I and II) was 2.6 points at the time of diagnosis, rising to 5.9 points 16 years after diagnosis. This fluctuation exceeds the estimated minimal and moderate clinically important differences, respectively. Not all patients conformed to the current clinical picture of gradual, smooth changes: many patients had regimes where symptom severity varied in an unpredictable manner, or underwent large rapid changes in an otherwise more stable progression. Conclusions: This information about short-term PD symptom dynamics contributes new scientific understanding about the disease progression, currently very costly to obtain without self-administered Internet-based reporting. This understanding should have implications for the optimization of clinical trials into new treatments and for the choice of treatment decision timescales.

The ‘situated functionality’ of accounting in private equity practices:a social ‘site’ analysis

This paper contributes to the recent ‘practice turn’ in management accounting literature in two ways: (1) by investigating the meshing and consequently the ‘situated functionality’ of accounting in various private equity (PE) practices, and (2) by experimenting with the application of Schatzki’s ‘site’ ontology. By identifying and describing the role and nature of accounting and associated calculative practices in different parts of the PE value chain, we note that the ‘situated functionality’ of accounting is ‘prefigured’ by its ‘dispersed’ nature. A particular contribution of experimenting with Schatzki’s ‘site’ ontology has been to identify theoretical concerns in relation to the meaning and role of the concept ‘general understandings’ and to clarify the definitional issues surrounding this concept. We also identify the close relationship between ‘general understandings’ and ‘teleoaffective structure’ and note their mutually constitutive nature.

Organised crime and the law:a comparative analysis, by Liz Campbell

Book review

A tudásérték meghatározása minőségügyi szempontból, hálózatelemzési módszerekkel (The knowledge value in terms of quality with network analysis methods)

A minőségügy egyik kulcsfeladata, hogy azonosítsa az értékteremtés szempontjából kritikus tényezőket, meghatározza ezek értékét, valamint intézkedjen negatív hatásuk megelőzése és csökkentése érdekében. Az értékteremtés sok esetben folyamatokon keresztül történik, amelyek tevékenységekből, elvégzendő feladatokból állnak. Ezekhez megfelelő munkatársak kellenek, akiknek az egyik legfontosabb jellemzője az általuk birtokolt tudás. Mindezek alapján a feladat-tudás-erőforrás kapcsolatrendszer ismerete és kezelése minőségügyi feladat is. A komplex rendszerek elemzésével foglalkozó hálózatkutatás eszközt biztosíthat ehhez, ezért indokolt a minőségügyi területen történő alkalmazhatóságának vizsgálata. Az alkalmazási lehetőségek rendszerezése érdekében a szerzők kategorizálták a minőségügyi hálózatokat az élek (kapcsolatok) és a csúcsok (hálózati pontok) típusai alapján. Ezt követően definiálták a multimodális (több különböző csúcstípusból álló) tudáshálózatot, amely a feladatokból, az erőforrásokból, a tudáselemekből és a közöttük lévő kapcsolatokból épül fel. A hálózat segítségével kategóriákba sorolták a tudáselemeket, valamint a fokszámok alapján meghatározták értéküket. A multimodális hálózatból képzett tudáselem-hálózatban megadták az összefüggő csoportok jelentését, majd megfogalmaztak egy összefüggést a tudáselem-elvesztés kockázatának meghatározására. _______ The aims of quality management are to identify those factors that have significant influence on value production, qualify or quantify them, and make preventive and corrective actions in order to reduce their negative effects. The core elements of value production are processes and tasks, along with workforce having the necessary knowledge to work. For that reason the task-resource-knowledge structure is pertinent to quality management. Network science provides methods to analyze complex systems; therefore it seems reasonable to study the use of tools of network analysis in association with quality management issues. First of all the authors categorized quality networks according to the types of nodes (vertices) and links (edges or arcs). Focusing on knowledge management, they defined the multimodal knowledge network, consisting of tasks, resources, knowledge items and their interconnections. Based on their degree, network nodes can be categorized and their value can be quantified. Derived from the multimodal network knowledge-item network is to be created, where the meaning of cohesive subgroups is defined. Eventually they proposed a formula for determining the risk of knowledge loss.

Social network structure as a critical success condition for open source software project communities

In recent years, a surprising new phenomenon has emerged in which globally-distributed online communities collaborate to create useful and sophisticated computer software. These open source software groups are comprised of generally unaffiliated individuals and organizations who work in a seemingly chaotic fashion and who participate on a voluntary basis without direct financial incentive. ^ The purpose of this research is to investigate the relationship between the social network structure of these intriguing groups and their level of output and activity, where social network structure is defined as (1) closure or connectedness within the group, (2) bridging ties which extend outside of the group, and (3) leader centrality within the group. Based on well-tested theories of social capital and centrality in teams, propositions were formulated which suggest that social network structures associated with successful open source software project communities will exhibit high levels of bridging and moderate levels of closure and leader centrality. ^ The research setting was the SourceForge hosting organization and a study population of 143 project communities was identified. Independent variables included measures of closure and leader centrality defined over conversational ties, along with measures of bridging defined over membership ties. Dependent variables included source code commits and software releases for community output, and software downloads and project site page views for community activity. A cross-sectional study design was used and archival data were extracted and aggregated for the two-year period following the first release of project software. The resulting compiled variables were analyzed using multiple linear and quadratic regressions, controlling for group size and conversational volume. ^ Contrary to theory-based expectations, the surprising results showed that successful project groups exhibited low levels of closure and that the levels of bridging and leader centrality were not important factors of success. These findings suggest that the creation and use of open source software may represent a fundamentally new socio-technical development process which disrupts the team paradigm and which triggers the need for building new theories of collaborative development. These new theories could point towards the broader application of open source methods for the creation of knowledge-based products other than software. ^

Reverse Engineering of Modified Genes by Bayesian Network Analysis Defines Molecular Determinants Critical to the Development of Glioblastoma

In this study we have identified key genes that are critical in development of astrocytic tumors. Meta-analysis of microarray studies which compared normal tissue to astrocytoma revealed a set of 646 differentially expressed genes in the majority of astrocytoma. Reverse engineering of these 646 genes using Bayesian network analysis produced a gene network for each grade of astrocytoma (Grade I–IV), and ‘key genes’ within each grade were identified. Genes found to be most influential to development of the highest grade of astrocytoma, Glioblastoma multiforme were: COL4A1, EGFR, BTF3, MPP2, RAB31, CDK4, CD99, ANXA2, TOP2A, and SERBP1. All of these genes were up-regulated, except MPP2 (down regulated). These 10 genes were able to predict tumor status with 96–100% confidence when using logistic regression, cross validation, and the support vector machine analysis. Markov genes interact with NFkβ, ERK, MAPK, VEGF, growth hormone and collagen to produce a network whose top biological functions are cancer, neurological disease, and cellular movement. Three of the 10 genes - EGFR, COL4A1, and CDK4, in particular, seemed to be potential ‘hubs of activity’. Modified expression of these 10 Markov Blanket genes increases lifetime risk of developing glioblastoma compared to the normal population. The glioblastoma risk estimates were dramatically increased with joint effects of 4 or more than 4 Markov Blanket genes. Joint interaction effects of 4, 5, 6, 7, 8, 9 or 10 Markov Blanket genes produced 9, 13, 20.9, 26.7, 52.8, 53.2, 78.1 or 85.9%, respectively, increase in lifetime risk of developing glioblastoma compared to normal population. In summary, it appears that modified expression of several ‘key genes’ may be required for the development of glioblastoma. Further studies are needed to validate these ‘key genes’ as useful tools for early detection and novel therapeutic options for these tumors.

Mining the online social network data: Influence, summarization, and organization

Online Social Network (OSN) services provided by Internet companies bring people together to chat, share the information, and enjoy the information. Meanwhile, huge amounts of data are generated by those services (they can be regarded as the social media ) every day, every hour, even every minute, and every second. Currently, researchers are interested in analyzing the OSN data, extracting interesting patterns from it, and applying those patterns to real-world applications. However, due to the large-scale property of the OSN data, it is difficult to effectively analyze it. This dissertation focuses on applying data mining and information retrieval techniques to mine two key components in the social media data — users and user-generated contents. Specifically, it aims at addressing three problems related to the social media users and contents: (1) how does one organize the users and the contents? (2) how does one summarize the textual contents so that users do not have to go over every post to capture the general idea? (3) how does one identify the influential users in the social media to benefit other applications, e.g., Marketing Campaign? The contribution of this dissertation is briefly summarized as follows. (1) It provides a comprehensive and versatile data mining framework to analyze the users and user-generated contents from the social media. (2) It designs a hierarchical co-clustering algorithm to organize the users and contents. (3) It proposes multi-document summarization methods to extract core information from the social network contents. (4) It introduces three important dimensions of social influence, and a dynamic influence model for identifying influential users.

Spitzer power-law active galactic nucleus candidates in the chandra deep field-north

We define a sample of 62 galaxies in the Chandra Deep Field-North whose Spitzer IRAC SEDs exhibit the characteristic power-law emission expected of luminous AGNs. We study the multiwavelength properties of this sample and compare the AGNs selected in this way to those selected via other Spitzer color-color criteria. Only 55% of the power-law galaxies are detected in the X-ray catalog at exposures of >0.5 Ms, although a search for faint emission results in the detection of 85% of the power-law galaxies at the ≥2.5 σ detection level. Most of the remaining galaxies are likely to host AGNs that are heavily obscured in the X-ray. Because the power-law selection requires the AGNs to be energetically dominant in the near- and mid-infrared, the power-law galaxies comprise a significant fraction of the Spitzer-detected AGN population at high luminosities and redshifts. The high 24 μm detection fraction also points to a luminous population. The power-law galaxies comprise a subset of color-selected AGN candidates. A comparison with various mid-infrared color selection criteria demonstrates that while the color-selected samples contain a larger fraction of the X-ray-luminous AGNs, there is evidence that these selection techniques also suffer from a higher degree of contamination by star-forming galaxies in the deepest exposures. Considering only those power-law galaxies detected in the X-ray catalog, we derive an obscured fraction of 68% (2 : 1). Including all of the power-law galaxies suggests an obscured fraction of <81% (4 : 1).

Mining the Online Social Network Data: Influence, Summarization, and Organization

Online Social Network (OSN) services provided by Internet companies bring people together to chat, share the information, and enjoy the information. Meanwhile, huge amounts of data are generated by those services (they can be regarded as the social media ) every day, every hour, even every minute, and every second. Currently, researchers are interested in analyzing the OSN data, extracting interesting patterns from it, and applying those patterns to real-world applications. However, due to the large-scale property of the OSN data, it is difficult to effectively analyze it. This dissertation focuses on applying data mining and information retrieval techniques to mine two key components in the social media data — users and user-generated contents. Specifically, it aims at addressing three problems related to the social media users and contents: (1) how does one organize the users and the contents? (2) how does one summarize the textual contents so that users do not have to go over every post to capture the general idea? (3) how does one identify the influential users in the social media to benefit other applications, e.g., Marketing Campaign? The contribution of this dissertation is briefly summarized as follows. (1) It provides a comprehensive and versatile data mining framework to analyze the users and user-generated contents from the social media. (2) It designs a hierarchical co-clustering algorithm to organize the users and contents. (3) It proposes multi-document summarization methods to extract core information from the social network contents. (4) It introduces three important dimensions of social influence, and a dynamic influence model for identifying influential users.