Relationships between public health faculty and decision makers at four governmental levels: a social network analysis

Author(s):  
Nasreen S Jessani ◽  
Carly Babcock ◽  
Sameer Siddiqi ◽  
Melissa Davey-Rothwell ◽  
Shirley Ho ◽  
...  
2012 ◽  
Vol 27 (2) ◽  
pp. 123-137 ◽  
Author(s):  
Anita Kothari ◽  
Nadia Hamel ◽  
Jo-Anne MacDonald ◽  
Mechthild Meyer ◽  
Benita Cohen ◽  
...  

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Danielle Rankin

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.


2020 ◽  
Author(s):  
Wasim Ahmed ◽  
Francesc López Seguí ◽  
Josep Vidal-Alaball ◽  
Matthew S Katz

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.


Author(s):  
Mohammad Reza Amir Esmaili ◽  
Behzad Damari ◽  
Ahmad Hajebi ◽  
Noora Rafiee ◽  
Reza Goudarzi ◽  
...  

Background: In this study, the basic criteria, models, and indicators of intersectoral collaboration in health promotion were investigated to facilitate the implementation of collaboration. Methods: This scoping review was conducted using datasets of Embase, Web of Science, Scopus, and PubMed, and search engines of Google, Google Scholar, and ProQuest. Results: 52 studies were included, and 32 codes in Micro, Meso, and Macro level, were obtained. Micro-level criteria had the highest frequency. Among the models used in the reviewed studies, social network analysis, Diagnosis of Sustainable Collaboration, Bergen, and logic models had the highest frequency. Among the indicators studied, the number of participants and the level of collaboration as well as its sustainability were the most frequent indicators. Conclusion: The findings identified the most important and widely used criteria, models, and indicators of intersectoral collaboration in health promotion which can be useful for decision-makers and planners in the domain of health promotion, in designing, implementing, and evaluating collaborative programs.


10.2196/22374 ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. e22374 ◽  
Author(s):  
Wasim Ahmed ◽  
Francesc López Seguí ◽  
Josep Vidal-Alaball ◽  
Matthew S Katz

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.


2013 ◽  
Vol 19 (2) ◽  
pp. 67-75 ◽  
Author(s):  
Julie A. Sorensen ◽  
Devon Brewer ◽  
Lynae Wyckoff ◽  
Melissa Horsman ◽  
Erika Scott ◽  
...  

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.


2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Tzu-Yi Fang

The study considers the semiconductor industry’s business process to be made up of two stages. In the business development process, a company generates profit and consumes energy while polluting the environment. After the two-stage data envelopment analysis approach was employed for calculating the operational efficiency and environmental efficiency, social network analysis was used to compare the manner in which the internal advantages or individual process factors of 28 semiconductor companies contribute to efficiency. A network graph was plotted to visualize relationships, with each node in the network graph representing a company. This graph was plotted to help decision-makers and manufacturers understand information communication among companies and the importance of the company in the network and help companies develop a mutual understanding to improve operational efficiency. The results of the study indicated that having an efficient company does not necessarily mean that the company plays a key role in the entire industry. The results provide decision-makers with references for improvements and information for learning from these references.


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