scholarly journals Eigenvector centrality defines hierarchy and predicts graduation in therapeutic community units

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0261405
Author(s):  
Benjamin Campbell ◽  
Keith Warren ◽  
Mackenzie Weiler ◽  
George De Leon
Keyword(s):  
Social Network ◽  
Criminal Behavior ◽  
Therapeutic Communities ◽  
Mutual Aid ◽  
Eigenvector Centrality ◽  
Length Of Treatment ◽  
Paired Samples ◽  
Lower Maximum ◽  
Clinical Records ◽  
Over Time

Introduction Therapeutic communities (TCs) are mutual aid based residential programs for the treatment of substance abuse and criminal behavior. While it is expected that residents will provide feedback to peers, there has been no social network study of the hierarchy through which feedback flows. Methods Data for this study was drawn from clinical records of peer corrections exchanged between TC residents in six units kept over periods of less than two to over eight years. Four of the units served men while two served women. Hierarchy position was measured using eigenvector centrality, on the assumption that residents who were more central in the network of corrections were lower in the hierarchy. It was hypothesized that residents would rise in the hierarchy over time. This was tested using Wilcoxon paired samples tests comparing the mean and maximum eigenvector centrality for time in treatment with those in the last month of treatment. It was also hypothesized that residents who rose higher in the hierarchy were more likely to graduate, the outcome of primary interest. Logistic regression was used to test hierarchy position as a predictor of graduation, controlling for age, race, risk of recidivism as measured by the Level of Services Inventory-Revised (LSI-R) and days spent in the program. Results Residents averaged a statistically significantly lower eigenvector centrality in the last month in all units, indicating a rise in the hierarchy over time. Residents with lower maximum and average eigenvector centrality both over the length of treatment and in the last month of treatment were more likely to graduate in four of the six units, those with lower maximum and average eigenvector centrality in the last month but not over the length of treatment were more likely to graduate in one of the six units, while eigenvector centrality did not predict graduation in one unit. However, this last unit was much smaller than the others, which may have influenced the results. Conclusion These results suggest that TC residents move through a social network hierarchy and that movement through the hierarchy predicts successful graduation.

scholarly journals Tracking sickness effects on social encounters via continuous proximity sensing in wild vampire bats

2020 ◽  
Vol 31 (6) ◽  
pp. 1296-1302 ◽  
Author(s):  
Simon P Ripperger ◽  
Sebastian Stockmaier ◽  
Gerald G Carter
Keyword(s):  
Social Network ◽  
High Resolution ◽  
Sickness Behavior ◽  
Time Of Day ◽  
Control Group ◽  
Eigenvector Centrality ◽  
Proximity Data ◽  
Vampire Bats ◽  
Network Connectedness ◽  
Over Time

Abstract Sickness behaviors can slow the spread of pathogens across a social network. We conducted a field experiment to investigate how sickness behavior affects individual connectedness over time using a dynamic social network created from high-resolution proximity data. After capturing adult female vampire bats (Desmodus rotundus) from a roost, we created “sick” bats by injecting a random half of bats with the immune-challenging substance, lipopolysaccharide, while the control group received saline injections. Over the next 3 days, we used proximity sensors to continuously track dyadic associations between 16 “sick” bats and 15 control bats under natural conditions. Compared to control bats, “sick” bats associated with fewer bats, spent less time near others, and were less socially connected to more well-connected individuals (sick bats had on average a lower degree, strength, and eigenvector centrality). High-resolution proximity data allow researchers to flexibly define network connections (association rates) based on how a particular pathogen is transmitted (e.g., contact duration of >1 vs. >60 min, contact proximity of <1 vs. <10 m). Therefore, we inspected how different ways of measuring association rates changed the observed effect of LPS. How researchers define association rates influences the magnitude and detectability of sickness effects on network centrality. When animals are sick, they often encounter fewer individuals. We tracked this unintentional “social distancing” effect hour-by-hour in a wild colony of vampire bats. Using bat-borne proximity sensors, we compared changes in the social network connectedness of immune-challenged “sick” bats versus “control” bats over time. “Sick” bats had fewer encounters with others and spent less time near others. Associations changed dramatically by time of day, and different measures of association influenced the sickness effect estimates.

Developing a sustainable Digital Health Innovation Ecosystem in a rural and resource-poor region: A monitored networking approach (Preprint)

2020 ◽  
Author(s):  
Arunangsu Chatterjee ◽  
Sebastian Stevens ◽  
Sheena Asthana ◽  
Ray B Jones
Keyword(s):  
Social Network ◽  
Network Analysis ◽  
Data Collection ◽  
Digital Health ◽  
Resource Poor Setting ◽  
Quadruple Helix ◽  
Innovation Ecosystem ◽  
Resource Poor ◽  
Quadruple Helix Model ◽  
Over Time

BACKGROUND Digital health (DH) innovation ecosystems (IE) are key to the development of new e-health products and services. Within an IE, third parties can help promote innovation by acting as knowledge brokers and the conduits for developing inter-organisational and interpersonal relations, particularly for smaller organisations. Kolehmainen’s quadruple helix model suggests who the critical IE actors are, and their roles. Within an affluent and largely urban setting, such ecosystems evolve and thrive organically with minimal intervention due to favourable economic and geographical conditions. Facilitating and sustaining a thriving DH IE within a resource-poor setting can be far more challenging even though far more important for such peripheral economics and the health and well-being of those communities. OBJECTIVE Taking a rural and remote region in the UK, as an instance of an IE in a peripheral economy, we adapt the quadruple helix model of innovation, apply a monitored social networking approach using McKinsey’s Three Horizons of growth to explore: • What patterns of connectivity between stakeholders develop within an emerging digital health IE? • How do networks develop over time in the DH IE? • In what ways could such networks be nurtured in order to build the capacity, capability and sustainability of the DH IE? METHODS Using an exploratory single case study design for a developing digital health IE, this study adopts a longitudinal social network analysis approach, enabling the authors to observe the development of the innovation ecosystem over time and evaluate the impact of targeted networking interventions on connectivity between stakeholders. Data collection was by an online survey and by a novel method, connection cards. RESULTS Self-reported connections between IE organisations increased between the two waves of data collection, with Small and Medium-sized Enterprises (SMEs) and academic institutions the most connected stakeholder groups. Patients involvement improved over time but still remains rather peripheral to the DH IE network. Connection cards as a monitoring tool worked really well during large events but required significant administrative overheads. Monitored networking information categorised using McKinsey’s Three Horizons proved to be an effective way to organise networking interventions ensuring sustained engagement. CONCLUSIONS The study reinforces the difficulty of developing and sustaining a DH IE in a resource-poor setting. It demonstrates the effective monitored networking approach supported by Social Network Analysis allows to map the networks and provide valuable information to plan future networking interventions (e.g. involving patients or service users). McKinsey’s Three Horizons of growth-based categorisation of the networking assets help ensure continued engagement in the DH IE contributing towards its long-term sustainability. Collecting ongoing data using survey or connection card method will become more labour intensive and ubiquitous ethically driven data collection methods can be used in future to make the process more agile and responsive.

scholarly journals Global Trigger Tool methodology to detect adverse events: pilot study at Academic Hospital of Udine

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
E Scarpis ◽  
S Degan ◽  
D De Corti ◽  
F Mellace ◽  
R Cocconi ◽  
...  
Keyword(s):  
Patient Safety ◽  
Adverse Events ◽  
Academic Hospital ◽  
Trigger Tool ◽  
Hospital Acquired Infections ◽  
Frequent Type ◽  
Global Trigger Tool ◽  
Clinical Records ◽  
Hospital Acquired ◽  
Over Time

Abstract Introduction Identification and measurement of adverse events (AEs) is crucial for patient safety in order to monitor them over time and to implement quality improvement programs, testing if they are effective. Global Trigger Tool (GTT) has been proposed as a low-cost method, being also the most effective to detect AEs. This study aims to describe the number of triggers, the rate and level of AEs identified by GTT and the most frequent type of AE. Methods The Italian version of the GTT was used. Ten paper-based clinical records (CRs) randomly selected every 2 weeks were reviewed from January to April 2019 by three independent reviewers (two nurses, one doctor) at the Academic Hospital of Udine. The AEs rates calculated are: AEs per 1,000 patient-days, AEs per 100 admissions, percentage of admissions with an AE. AEs were classified by harm levels according to National Coordinating Council for Medication Error Reporting and Prevention (NCC MERP). Results CRs reviewed were 80. Mean age of the patients was 69.3±16.4, women were 37.5%. Mean hospitalisation was 16.8±15.3. Nine were the cases of re-hospitalisation within 30 days (11.3%). The total number of trigger was 156. AEs were 31, with at least one AE on 27.5% of admissions, 38.8 AEs per 100 admissions and 23 AEs per 1,000 patient-days. AEs with harm level E, F and H were respectively 5 (16.1%), 24 (77.4%) and 2 (6.5%). The most frequent type of AE were hospital acquired infections with 15 cases (48.4%). Conclusions The most frequent type of AE was the hospital acquired infections. Rates and levels of AEs were higher than other international studies, probably because of the limited number of CRs reviewed. Key messages Global Trigger Tool is an effective method to detect adverse patient safety events in order to monitor them over time. The most frequent type of adverse events was the hospital acquired infections.

Exploring the Relationship Between Social Interaction, Cognitive Processing and Learning Achievements in a MOOC Discussion Forum

2021 ◽  
pp. 073563312110273
Author(s):  
Zhi Liu ◽  
Ning Zhang ◽  
Xian Peng ◽  
Sannyuya Liu ◽  
Zongkai Yang ◽  
...  
Keyword(s):  
Social Network ◽  
Social Interaction ◽  
Network Analysis ◽  
Cognitive Processing ◽  
Learning Analytics ◽  
Discussion Forum ◽  
Community Type ◽  
Eigenvector Centrality ◽  
Community Types ◽  
The Relationship

In the field of learning analytics, mining the regularities of social interaction and cognitive processing have drawn increasing attention. Nevertheless, in MOOCs, there is a lack of investigations on the combination of social and cognitive behavioral patterns. To fill in this gap, this study aimed to uncover the relationship between social interaction, cognitive processing, and learning achievements in a MOOC discussion forum. Specifically, we collected the 3925 participants’ forum data throughout 16 weeks. Social network analysis and epistemic network analysis were jointly adopted to investigate differences in social interaction, cognitive processing between two achievement groups, and the differences in cognitive processing networks between two types of communities. Finally, moderation analysis was employed to examine the moderating effect of community types between cognitive processing and learning achievements. Results indicated that: (1) the high- and low-achieving groups presented significant differences in terms of degree, betweenness, and eigenvector centrality; (2) the stronger cognitive connections were found within the high-achieving group and the instructor-led community; (3) the cognitive processing indicators including insight, discrepancy, and tentative were significantly negative predictors of learning achievements, whereas inhibition and exclusive were significantly positive predictors; (4) the community type moderated the relationship between cognitive processing and learning achievements.

A network case of knowledge brokering

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Joelle Rodway ◽  
Stephen MacGregor ◽  
Alan Daly ◽  
Yi-Hwa Liou ◽  
Susan Yonezawa ◽  
...  
Keyword(s):  
Social Network ◽  
Learning Community ◽  
Knowledge Exchange ◽  
Point Of View ◽  
Network Activity ◽  
Centrality Measures ◽  
Knowledge Brokering ◽  
Content Type ◽  
Knowledge Brokers ◽  
Over Time

PurposeThe purpose of this paper is two-fold: (1) to offer a conceptual understanding of knowledge brokering from a sociometric point-of-view; and (2) to provide an empirical example of this conceptualization in an education context.Design/methodology/approachWe use social network theory and analysis tools to explore knowledge exchange patterns among a group of teachers, instructional coaches and administrators who are collectively seeking to build increased capacity for effective mathematics instruction. We propose the concept of network activity to measure direct and indirect knowledge brokerage through the use of degree and betweenness centrality measures. Further, we propose network utility—measured by tie multiplexity—as a second key component of effective knowledge brokering.FindingsOur findings suggest significant increases in both direct and indirect knowledge brokering activity across the network over time. Teachers, in particular, emerge as key knowledge brokers within this networked learning community. Importantly, there is also an increase in the number of resources exchanged through network relationships over time; the most active knowledge brokers in this social ecosystem are those individuals who are exchanging multiple forms of knowledge.Originality/valueThis study focuses on knowledge brokering as it presents itself in the relational patterns among educators within a social ecosystem. While it could be that formal organizational roles may encapsulate knowledge brokering across physical structures with an education system (e.g. between schools and central offices), these individuals are not necessarily the people who are most effectively brokering knowledge across actors within the broader social network.

scholarly journals Temporal Activity Path Based Character Correction in Heterogeneous Social Networks via Multimedia Sources

2018 ◽  
Vol 2018 ◽  
pp. 1-16
Author(s):  
Jun Long ◽  
Lei Zhu ◽  
Zhan Yang ◽  
Chengyuan Zhang ◽  
Xinpan Yuan
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Large Scale ◽  
Multimedia Data ◽  
Attribute Information ◽  
Novel Method ◽  
Relationship Network ◽  
Heterogeneous Social Networks ◽  
The Relationship ◽  
Over Time

Vast amount of multimedia data contains massive and multifarious social information which is used to construct large-scale social networks. In a complex social network, a character should be ideally denoted by one and only one vertex. However, it is pervasive that a character is denoted by two or more vertices with different names; thus it is usually considered as multiple, different characters. This problem causes incorrectness of results in network analysis and mining. The factual challenge is that character uniqueness is hard to correctly confirm due to lots of complicated factors, for example, name changing and anonymization, leading to character duplication. Early, limited research has shown that previous methods depended overly upon supplementary attribute information from databases. In this paper, we propose a novel method to merge the character vertices which refer to the same entity but are denoted with different names. With this method, we firstly build the relationship network among characters based on records of social activities participating, which are extracted from multimedia sources. Then we define temporal activity paths (TAPs) for each character over time. After that, we measure similarity of the TAPs for any two characters. If the similarity is high enough, the two vertices should be considered as the same character. Based on TAPs, we can determine whether to merge the two character vertices. Our experiments showed that this solution can accurately confirm character uniqueness in large-scale social network.

Quantifying Virality of Information in Online Social Networks

2012 ◽  
Vol 4 (1) ◽  
pp. 32-45 ◽  
Author(s):  
Abhishek Vaish ◽  
Rajiv Krishna G. ◽  
Akshay Saxena ◽  
Dharmaprakash M. ◽  
Utkarsh Goel
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Online Social Networks ◽  
Online Social Network ◽  
Critical Factors ◽  
Asset Valuation ◽  
Valuation Technique ◽  
Information Item ◽  
Viral Nature ◽  
Over Time

The aim of this research is to propose a model through which the viral nature of an information item in an online social network can be quantified. Further, the authors propose an alternate technique for information asset valuation by accommodating virality in it which not only complements the existing valuation system, but also improves the accuracy of the results. They use a popularly available YouTube dataset to collect attributes and measure critical factors such as share-count, appreciation, user rating, controversiality, and comment rate. These variables are used with a proposed formula to obtain viral index of each video on a given date. The authors then identify a conventional and a hybrid asset valuation technique to demonstrate how virality can fit in to provide accurate results.The research demonstrates the dependency of virality on critical social network factors. With the help of a second dataset acquired, the authors determine the pattern virality of an information item takes over time.

scholarly journals Stress-related inflammation and social withdrawal in mothers after childhood cancer diagnosis

2021 ◽  
Author(s):  
Emily K Lindsay ◽  
Tristen K. Inagaki ◽  
Catherine Walsh ◽  
Berhane Messay ◽  
Linda Ewing ◽  
...  
Keyword(s):  
Social Network ◽  
Social Behavior ◽  
Systemic Inflammation ◽  
Cancer Diagnosis ◽  
Social Roles ◽  
Social Changes ◽  
Related Stress ◽  
Changes Over Time ◽  
Over Time

Objective: Acute inflammation-induced sickness behavior involves changes in social behavior that are believed to promote recovery. Whether chronic inflammation can influence social behaviors in ways that promote recovery is unknown. In a sample of mothers of a child with cancer, this report explores the relationship between inflammation that accompanies the stress of diagnosis and changes in social network, cancer-related stress, and inflammation across one year. Three hypotheses were tested, that (1) initial stress would associate with initial inflammation, (2) initial inflammation would predict social changes over time, and (3) social changes over time would buffer stress and inflammation over time. Methods: Cancer-related stress (Impact of Events Scale), social network (social roles and contacts from the Social Network Inventory), and systemic inflammation (circulating IL-6) were assessed in 120 mothers three times after their child’s cancer diagnosis: following diagnosis (T1), 6-month follow-up (T2), and 12-month follow-up (T3). Results: Consistent with predictions, greater cancer-related stress following diagnosis (T1) was associated with higher IL-6 following diagnosis (T1; b=.014, p=.008). In turn, higher IL-6 following diagnosis (T1) was associated with a decrease in social roles over time (T1-->T3; B=-.030, p=.041), particularly peripheral social roles. Finally, dropping social roles over time (T1-->T3) was associated with decreases in cancer-related stress (B=21.83, p=.040) and slower increases in IL-6 (B=.940, p=.036) over time.Conclusions: This study provides a first indication that chronic stress-related systemic inflammation may predict changes in social behavior that associate with stress recovery and slower increases in inflammation in the year following a major life stressor.

Mining Organizations’ Networks

2011 ◽  
pp. 205-230 ◽  
Author(s):  
James A. Danowski
Keyword(s):  
Social Network ◽  
Oil Spill ◽  
Organizational Hierarchy ◽  
Art And Design ◽  
Bp Oil Spill ◽  
Network Mining ◽  
Social Network Mining ◽  
Corporate Innovation ◽  
Semantic Associations ◽  
Over Time

This chapter presents six examples of organization-related social network mining: 1) interorganizational and sentiment networks in the Deepwater BP Oil Spill events, 2) intraorganizational interdepartmental networks in the Savannah College of Art and Design (SCAD), 3) who-to-whom email networks across the organizational hierarchy the Ford Motor Company’s automotive engineering innovation: “Sync® w/ MyFord Touch”, 4) networks of selected individuals who left that organization, 5) semantic associations across email for a corporate innovation in that organization, and 6) assessment of sentiment across its email for innovations over time. These examples are discussed in terms of motivations, methods, implications, and applications.

scholarly journals Online social network security awareness: mass interpersonal persuasion using a Facebook app

2019 ◽  
Vol 32 (5) ◽  
pp. 1276-1300
Author(s):  
Ehinome Ikhalia ◽  
Alan Serrano ◽  
David Bell ◽  
Panos Louvieris
Keyword(s):  
Social Network ◽  
Online Social Network ◽  
Social Engineering ◽  
Security Awareness ◽  
Structured Interviews ◽  
Experimental Conditions ◽  
Content Type ◽  
Paired Samples ◽  
Mip Model ◽  
System Usability Scale

Purpose Online social network (OSN) users have a high propensity to malware threats due to the trust and persuasive factors that underpin OSN models. The escalation of social engineering malware encourages a growing demand for end-user security awareness measures. The purpose of this paper is to take the theoretical cybersecurity awareness model TTAT-MIP and test its feasibility via a Facebook app, namely social network criminal (SNC). Design/methodology/approach The research employs a mixed-methods approach to evaluate the SNC app. A system usability scale measures the usability of SNC. Paired samples t-tests were administered to 40 participants to measure security awareness – before and after the intervention. Finally, 20 semi-structured interviews were deployed to obtain qualitative data about the usefulness of the App itself. Findings Results validate the effectiveness of OSN apps utilising a TTAT-MIP model – specifically the mass interpersonal persuasion (MIP) attributes. Using TTAT-MIP as a guidance, practitioners can develop security awareness systems that better leverage the intra-relationship model of OSNs. Research limitations/implications The primary limitation of this study is the experimental settings. Although the results testing the TTAT-MIP Facebook app are promising, these were set under experimental conditions. Practical implications SNC enable persuasive security behaviour amongst employees and avoid potential malware threats. SNC support consistent security awareness practices by the regular identification of new threats which may inspire the creation of new security awareness videos. Social implications The structure of OSNs is making it easier for malicious users to carry out their activities without the possibility of detection. By building a security awareness programme using the TTAT-MIP model, organisations can proactively manage security awareness. Originality/value Many security systems are cumbersome, inconsistent and non-specific. The outcome of this research provides organisations and security practitioners with a framework for designing and developing proactive and tailored security awareness systems.

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