Building Safety Partnerships Using Social Network Analysis

2013 ◽  
Vol 19 (2) ◽  
pp. 67-75 ◽  
Author(s):  
Julie A. Sorensen ◽  
Devon Brewer ◽  
Lynae Wyckoff ◽  
Melissa Horsman ◽  
Erika Scott ◽  
...  
Keyword(s):  
Public Health ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Marketing ◽  
Health And Safety ◽  
The United States ◽  
Tractor Safety ◽  
Public Health And Safety ◽  
Safety Initiative

Although public–private partnerships have been useful components in public health and safety initiatives, little has been published on how to cultivate effective public health and safety partnerships for upstream social marketing initiatives. Using the development of a U.S. tractor safety alliance as an example, we illustrate how social network analysis can be used to identify organizations that are likely to be strategic partners and targets for upstream social marketing. In our project, knowledgeable informants first identified members of a national agricultural stakeholder network in the United States. Then, we surveyed the representatives of these organizations about their organizations’ interest in joining a new U.S. tractor safety initiative, the connections between their own and other stakeholder organizations, and their perceptions of the organizations most able to advance a U.S. tractor safety initiative. From our analysis of these data, we identified 10 organizations that have the partnerships, resources, and interest necessary to lead an effective tractor safety partnership. These organizations will be the focus of an upstream social marketing initiative aimed at building a strategic tractor safety alliance.

Exploring Community Collaborations: Social Network Analysis as a Reflective Tool for Public Health

2012 ◽  
Vol 27 (2) ◽  
pp. 123-137 ◽  
Author(s):  
Anita Kothari ◽  
Nadia Hamel ◽  
Jo-Anne MacDonald ◽  
Mechthild Meyer ◽  
Benita Cohen ◽  
...  
Keyword(s):  
Public Health ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis

Social network analysis and data visualization of MIS international collaboration in Taiwan

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yung-Ting Chuang ◽  
Yi-Hsi Chen
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
International Collaboration ◽  
Research Productivity ◽  
Research Collaboration ◽  
Clustering Algorithms ◽  
The United States ◽  
Content Type ◽  
Collaboration Patterns

PurposeThe purpose of this paper is to apply social network analysis (SNA) to study faculty research productivity, to identify key leaders, to study publication keywords and research areas and to visualize international collaboration patterns and analyze collaboration research fields from all Management Information System (MIS) departments in Taiwan from 1982 to 2015.Design/methodology/approachThe authors first retrieved results encompassing about 1,766 MIS professors and their publication records between 1982 and 2015 from the Ministry of Science and Technology of Taiwan (MOST) website. Next, the authors merged these publication records with the records obtained from the Web of Science, Google Scholar, IEEE Xplore, ScienceDirect, Airiti Library and Springer Link databases. The authors further applied six network centrality equations, leadership index, exponential weighted moving average (EWMA), contribution value and k-means clustering algorithms to analyze the collaboration patterns, research productivity and publication patterns. Finally, the authors applied D3.js to visualize the faculty members' international collaborations from all MIS departments in Taiwan.FindingsThe authors have first identified important scholars or leaders in the network. The authors also see that most MIS scholars in Taiwan tend to publish their papers in the journals such as Decision Support Systems and Information and Management. The authors have further figured out the significant scholars who have actively collaborated with academics in other countries. Furthermore, the authors have recognized the universities that have frequent collaboration with other international universities. The United States, China, Canada and the United Kingdom are the countries that have the highest numbers of collaborations with Taiwanese academics. Lastly, the keywords model, system and algorithm were the most common terms used in recent years.Originality/valueThis study applied SNA to visualize international research collaboration patterns and has revealed some salient characteristics of international cooperation trends and patterns, leadership networks and influences and research productivity for faculty in Information Management departments in Taiwan from 1982 to 2015. In addition, the authors have discovered the most common keywords used in recent years.

Information access and sharing among prosthetics and orthotics faculty in Ghana and the United States

2020 ◽  
pp. 030936462095882
Author(s):  
Cody L McDonald ◽  
Henry Larbi ◽  
Sarah Westcott McCoy ◽  
Deborah Kartin
Keyword(s):  
United States ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Information Exchange ◽  
Information Access ◽  
The United States ◽  
Exchange Networks ◽  
Key Players ◽  
Internal Connections

Background: Information access is essential for quality healthcare provision and education. Despite technological advances, access to prosthetics and orthotics information in low- and middle-income countries is not ubiquitous. The current state of information access, availability, and exchange among prosthetics and orthotics faculty is unknown. Objectives: Describe information exchange networks and access at two prosthetics and orthotics programs in Ghana and the United States. Study design: Cross-sectional survey, social network analysis. Methods: An online survey of faculty at two prosthetics and orthotics programs using REDCap. The survey included a social network analysis, demographics, and prosthetics and orthotics information resources and frequency of use. Descriptive statistics were calculated. Results: Twenty-one faculty members completed the survey (84% response). Ghanaian faculty were on average younger (median Ghana: 27 years, United States: 43 years), had less teaching experience, and had less education than US faculty. Textbooks were the most commonly used resource at both programs. The Ghanaian network had more internal connections with few outside sources. The US network had fewer internal connections, relied heavily upon four key players, and had numerous outside contacts. Conclusion: Ghana and US faculty have two distinct information exchange networks. These networks identify key players and barriers to dissemination among faculty to promote successful knowledge translation of current scientific literature and technology development. Social network analysis may be a useful method to explore information sharing among prosthetics and orthotics faculty, and identify areas for further study.

scholarly journals Individuals’ Use of Twitter to Discuss Freedom of Information in the United States: A Social Network Analysis

2019 ◽  
Vol 1 (2) ◽  
pp. 45-59
Author(s):  
Kayla Schwoerer
Keyword(s):  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
The United States ◽  
Freedom Of Information ◽  
Public Agencies ◽  
Freedom Of Information Act ◽  
Advocacy Organizations ◽  
The U.S

This study employs social network analysis to examine more than 10,000 Twitter interactions that include the U.S. Freedom of Information Act hashtag (#FOIA) to understand who is engaging online, and to what extent. The analysis finds evidence of a dynamic conversation online among citizens, journalists, advocates, and public agencies. Findings offer insights into how citizens are using social media to engage with government and one another in conversations around important public policies, such as government transparency, as well as how technologies such as social media can be leveraged to better understand citizens’ interest. The study also found a significant increase in tweets during national Sunshine Week, a vehicle that increases national dialogue about FOI, and highlights effective social media strategies employed by MuckRock and other advocacy organizations.

scholarly journals Social Network Analysis across Healthcare Entities, Orange County, FL, 2016

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Danielle Rankin
Keyword(s):  
Public Health ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Disease Control ◽  
Acute Care ◽  
Orange County ◽  
Acute Care Hospitals ◽  
Patient Movement

Objective: To create a baseline social network analysis to assess connectivity of healthcare entities through patient movement in Orange County, Florida.Introduction: In the realm of public health, there has been an increasing trend in exploration of social network analyses (SNAs). SNAs are methodological and theoretical tools that describe the connections of people, partnerships, disease transmission, the interorganizational structure of health systems, the role of social support, and social capital1. The Florida Department of Health in Orange County (DOH-Orange) developed a reproducible baseline social network analysis of patient movement across healthcare entities to gain a county-wide perspective of all actors and influences in our healthcare system. The recognition of the role each healthcare entity contributes to Orange County, Florida can assist DOH-Orange in developing facility-specific implementations such as increased usage of personal protective equipment, environmental assessments, and enhanced surveillance.Methods: DOH-Orange received Centers for Medicare and Medicaid Services data from the Centers for Disease Control and Prevention Division of Health Care Quality Promotion. The dataset contains the frequency of patients transferred across Medicare accepting healthcare entities during 2016. We constructed a directional sociogram using R package statnet version 2016.9, built under R version 3.3.3. Node colors are categorized by the type of healthcare entity represented (e.g., long-term care facilities, acute care hospitals, post-acute care hospitals, and other) and depict the frequency of patients transferred with weighted edges. Node sizes are proportional to the log reduction of the total degree of patients transferred, and are arranged with the Fruchterman-Reingold layout. We calculated standard network indices to assess the magnitude of connectedness across healthcare entities in Orange County, Florida. Additionally, we calculated node-level indices to gain a perspective of the strength of each individual entity.Results: A total of 48 healthcare entities were included in the sociogram, with 44% representing Orange County, Florida. Although the majority of the healthcare entities are located in nearby counties, 90% of patient movement occurred across Orange County entities. The range of patient movement was 1 to 5196 with a median of 15 patients transferred in 2016. The network in Orange County is sparse with a density of 0.05, but the movement of patients across the healthcare entities is predominately symmetric (reciprocity=97%). The sociogram is centralized (degree centrality= 0.70) and contains a vast amount of entities that serve as connectors (betweenness centrality=0.53). The node-level indices identified our acute care hospitals and long term acute care hospitals are the connectors of our county health system.Conclusions: The SNA of patient movement across healthcare entities in Orange County, Florida provides public health with knowledge of the influences entities contribute to the county healthcare system. This will contribute to identifying changes in the network in future research on the transmission risks of specific diseases/conditions, which will enhance prioritization of targeted interventions within healthcare entities. In addition, SNAs can assist in targeting disease control efforts during outbreak investigations and support health communication. A SNA toolkit will be distributed to other local county health departments for reproduction to determine baseline data and integrate county-specific SNAs.

scholarly journals Effects of Laws Expanding Civilian Rights to Use Deadly Force in Self-Defense on Violence and Crime: A Systematic Review

2021 ◽  
pp. e1-e14
Author(s):  
Alexa R. Yakubovich ◽  
Michelle Degli Esposti ◽  
Brittany C. L. Lange ◽  
G. J. Melendez-Torres ◽  
Alpa Parmar ◽  
...  
Keyword(s):  
Public Health ◽  
United States ◽  
Health And Safety ◽  
The United States ◽  
Risk Of Bias ◽  
Deadly Force ◽  
Us States ◽  
Self Defense ◽  
Social Inequities ◽  
Public Health And Safety

Background. Since 2005, most US states have expanded civilian rights to use deadly force in self-defense outside the home. In most cases, legislation has included removing the duty to retreat anywhere one may legally be, commonly known as stand-your-ground laws. The extent to which these laws affect public health and safety is widely debated in public and policy discourse. Objectives. To synthesize the available evidence on the impacts and social inequities associated with changing civilian rights to use deadly force in self-defense on violence, injury, crime, and firearm-related outcomes. Search Methods. We searched MEDLINE, Embase, PsycINFO, Scopus, Web of Science, Sociological Abstracts, National Criminal Justice Reference Service Abstracts, Education Resources Information Center, International Bibliography of the Social Sciences, ProQuest Dissertations and Theses, Google Scholar, National Bureau of Economic Research working papers, and SocArXiv; harvested references of included studies; and consulted with experts to identify studies until April 2020. Selection Criteria. Eligible studies quantitatively estimated the association between laws that expanded or restricted the right to use deadly force in self-defense and population or subgroup outcomes among civilians with a comparator. Data Collection and Analysis. Two reviewers extracted study data using a common form. We assessed study quality using the Risk of Bias in Nonrandomized Studies of Interventions tools adapted for (controlled) before–after studies. To account for data dependencies, we conducted graphical syntheses (forest plots and harvest plots) to summarize the evidence on impacts and inequities associated with changing self-defense laws. Main Results. We identified 25 studies that estimated population-level impacts of laws expanding civilian rights to use deadly force in self-defense, all of which focused on stand-your-ground or other expansions to self-defense laws in the United States. Studies were scored as having serious or critical risk of bias attributable to confounding. Risk of bias was low across most other domains (i.e., selection, missing data, outcome, and reporting biases). Stand-your-ground laws were associated with no change to small increases in violent crime (total and firearm homicide, aggravated assault, robbery) on average across states. Florida-based studies showed robust increases (24% to 45%) in firearm and total homicide while self-defense claims under stand-your-ground law were more often denied when victims were White, especially when claimants were racial minorities. Author’s Conclusions. The existing evidence contradicts claims that expanding self-defense laws deters violent crime across the United States. In at least some contexts, including Florida, stand-your-ground laws are associated with increases in violence, and there are racial inequities in the application of these laws. Public Health Implications. In some US states, most notably Florida, stand-your-ground laws may have harmed public health and safety and exacerbated social inequities. Our findings highlight the need for scientific evidence on both population and equity impacts of self-defense laws to guide legislative action that promotes public health and safety for all. Trial Registration. Open Science Framework ( https://osf.io/uz68e ). (Am J Public Health. Published online ahead of print February 23, 2021: e1–e14. https://doi.org/10.2105/AJPH.2020.306101 )

scholarly journals COVID-19 and the “Film Your Hospital” Conspiracy Theory: Social Network Analysis of Twitter Data (Preprint)

2020 ◽  
Author(s):  
Wasim Ahmed ◽  
Francesc López Seguí ◽  
Josep Vidal-Alaball ◽  
Matthew S Katz
Keyword(s):  
Public Health ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Information Source ◽  
Conspiracy Theory ◽  
Sources Of Information ◽  
Data Set ◽  
Conspiracy Theories ◽  
Twitter Data

BACKGROUND During the COVID-19 pandemic, a number of conspiracy theories have emerged. A popular theory posits that the pandemic is a hoax and suggests that certain hospitals are “empty.” Research has shown that accepting conspiracy theories increases the likelihood that an individual may ignore government advice about social distancing and other public health interventions. Due to the possibility of a second wave and future pandemics, it is important to gain an understanding of the drivers of misinformation and strategies to mitigate it. OBJECTIVE This study set out to evaluate the #FilmYourHospital conspiracy theory on Twitter, attempting to understand the drivers behind it. More specifically, the objectives were to determine which online sources of information were used as evidence to support the theory, the ratio of automated to organic accounts in the network, and what lessons can be learned to mitigate the spread of such a conspiracy theory in the future. METHODS Twitter data related to the #FilmYourHospital hashtag were retrieved and analyzed using social network analysis across a 7-day period from April 13-20, 2020. The data set consisted of 22,785 tweets and 11,333 Twitter users. The Botometer tool was used to identify accounts with a higher probability of being bots. RESULTS The most important drivers of the conspiracy theory are ordinary citizens; one of the most influential accounts is a Brexit supporter. We found that YouTube was the information source most linked to by users. The most retweeted post belonged to a verified Twitter user, indicating that the user may have had more influence on the platform. There was a small number of automated accounts (bots) and deleted accounts within the network. CONCLUSIONS Hashtags using and sharing conspiracy theories can be targeted in an effort to delegitimize content containing misinformation. Social media organizations need to bolster their efforts to label or remove content that contains misinformation. Public health authorities could enlist the assistance of influencers in spreading antinarrative content.

scholarly journals COVID-19 and the “Film Your Hospital” Conspiracy Theory: Social Network Analysis of Twitter Data

10.2196/22374 ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. e22374 ◽  
Author(s):  
Wasim Ahmed ◽  
Francesc López Seguí ◽  
Josep Vidal-Alaball ◽  
Matthew S Katz
Keyword(s):  
Public Health ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Information Source ◽  
Conspiracy Theory ◽  
Sources Of Information ◽  
Data Set ◽  
Conspiracy Theories ◽  
Twitter Data

Background During the COVID-19 pandemic, a number of conspiracy theories have emerged. A popular theory posits that the pandemic is a hoax and suggests that certain hospitals are “empty.” Research has shown that accepting conspiracy theories increases the likelihood that an individual may ignore government advice about social distancing and other public health interventions. Due to the possibility of a second wave and future pandemics, it is important to gain an understanding of the drivers of misinformation and strategies to mitigate it. Objective This study set out to evaluate the #FilmYourHospital conspiracy theory on Twitter, attempting to understand the drivers behind it. More specifically, the objectives were to determine which online sources of information were used as evidence to support the theory, the ratio of automated to organic accounts in the network, and what lessons can be learned to mitigate the spread of such a conspiracy theory in the future. Methods Twitter data related to the #FilmYourHospital hashtag were retrieved and analyzed using social network analysis across a 7-day period from April 13-20, 2020. The data set consisted of 22,785 tweets and 11,333 Twitter users. The Botometer tool was used to identify accounts with a higher probability of being bots. Results The most important drivers of the conspiracy theory are ordinary citizens; one of the most influential accounts is a Brexit supporter. We found that YouTube was the information source most linked to by users. The most retweeted post belonged to a verified Twitter user, indicating that the user may have had more influence on the platform. There was a small number of automated accounts (bots) and deleted accounts within the network. Conclusions Hashtags using and sharing conspiracy theories can be targeted in an effort to delegitimize content containing misinformation. Social media organizations need to bolster their efforts to label or remove content that contains misinformation. Public health authorities could enlist the assistance of influencers in spreading antinarrative content.

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