scholarly journals Research Expertise and the Framework of Harms: Social Network Analysis, Phase One

2020 ◽  
Author(s):  
Margo Hilbrecht ◽  
◽  
David Baxter ◽  
Alexander V. Graham ◽  
Maha Sohail
Keyword(s):  
Public Health ◽  
Mental Health ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Evidence Base ◽  
Public Health Approach ◽  
National Strategy ◽  
The Uk ◽  
Gambling Harms

In 2019, the Gambling Commission announced a National Strategy to Reduce Gambling Harms. Underlying the strategy is the Framework of Harms, outlined in Measuring gambling-related harms: A framework for action. "The Framework" adopts a public health approach to address gambling-related harm in Great Britain across multiple levels of measurement. It comprises three primary factors and nine related subfactors. To advance the National Strategy, all componentsneed to be supported by a strong evidence base. This report examines existing research expertise relevant to the Framework amongacademics based in the UK. The aim is to understand the extent to which the Framework factors and subfactors have been studied in order to identify gaps in expertise and provide evidence for decision making thatisrelevant to gambling harms research priorities. A social network analysis identified coauthor networks and alignment of research output with the Framework. The search strategy was limited to peer-reviewed items and covered the 12-year period from 2008 to 2019. Articles were selected using a Web of Science search. Of the 1417 records identified in the search, the dataset was refined to include only those articles that could be assigned to at least one Framework factor (n = 279). The primary factors and subfactors are: Resources:Work and Employment, Money and Debt, Crime;Relationships:Partners, Families and Friends, Community; and Health:Physical Health, Psychological Distress, and Mental Health. We used Gephi software to create visualisations reflecting degree centrality (number of coauthor networks) so that each factor and subfactor could be assessed for the density of research expertise and patterns of collaboration among coauthors. The findings show considerable variation by framework factor in the number of authors and collaborations, suggesting a need to develop additional research capacity to address under-researched areas. The Health factor subcategory of Mental Health comprised almost three-quarters of all citations, with the Resources factor subcategory of Money and Debt a distant second at 12% of all articles. The Relationships factor, comprised of two subfactors, accounted for less than 10%of total articles. Network density varied too. Although there were few collaborative networks in subfactors such as Community or Work and Employment, all Health subfactors showed strong levels of collaboration. Further, some subfactors with a limited number of researchers such as Partners, Families, and Friends and Money and debt had several active collaborations. Some researchers’ had publications that spanned multiple Framework factors. These multiple-factor researchers usually had a wide range of coauthors when compared to those who specialised (with the exception of Mental Health).Others’ collaborations spanned subfactors within a factor area. This was especially notable forHealth. The visualisations suggest that gambling harms research expertise in the UK has considerable room to grow in order to supporta more comprehensive, locally contextualised evidence base for the Framework. To do so, priority harms and funding opportunities will need further consideration. This will require multi-sector and multidisciplinary collaboration consistent with the public health approach underlying the Framework. Future research related to the present analysis will explore the geographic distribution of research activity within the UK, and research collaborations with harms experts internationally.

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

scholarly journals Nature-Based Interventions for Mental Health Care: Social Network Analysis as a Tool to Map Social Farms and their Response to Social Inclusion and Community Engagement

2019 ◽  
Vol 16 (18) ◽  
pp. 3501 ◽  
Author(s):  
Marta Borgi ◽  
Mario Marcolin ◽  
Paolo Tomasin ◽  
Cinzia Correale ◽  
Aldina Venerosi ◽  
...  
Keyword(s):  
Mental Health ◽  
Health Care ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Mental Health Care ◽  
Rural Areas ◽  
Social Inclusion ◽  
Local Communities ◽  
Social Farming

Social farming represents a hybrid governance model in which public bodies, local communities, and economic actors act together to promote health and social inclusion in rural areas. Although relational variables are crucial to foster social farm performance, the relational system in which farms are embedded has still not been fully described. Using social network analysis, here we map the nature of the links of a selected sample of social farms operating in Northern Italy. We also explore possible network variations following specific actions taken to potentiate local social farming initiatives. The results show a certain degree of variability in terms of the extension and features of the examined networks. Overall, the actions taken appear to be significant to enlarge and diversify farms’ networks. Social farming has the potential to provide important benefits to society and the environment and to contrast vulnerability in rural areas. Being able to create social and economic networks of local communities, social farming may also represent an innovative way to respond to the cultural shift from institutional psychiatry to community-based mental health care. This study emphasizes the critical role played by network facilitation in diversifying actors, promoting heterogeneous relationships, and, in turn, system complexity.

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.

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