Using Social Network Analysis for Assessing Mental Health and Social Services Inter-Organisational Collaboration: Findings in Deprived Areas in Brussels and London

2012 ◽  
Vol 40 (4) ◽  
pp. 331-339 ◽  
Author(s):  
Pablo Nicaise ◽  
Simon Tulloch ◽  
Vincent Dubois ◽  
Aleksandra Matanov ◽  
Stefan Priebe ◽  
...  
Keyword(s):  
Mental Health ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Services ◽  
Health And Social Services ◽  
Deprived Areas

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 Big Data Analysis of the Key Attributes Related to Stress and Mental Health in Korean Taekwondo Student Athletes

2022 ◽  
Vol 14 (1) ◽  
pp. 477
Author(s):  
Sung-Un Park ◽  
Jung-Woo Jeon ◽  
Hyunkyun Ahn ◽  
Yoon-Kwon Yang ◽  
Wi-Young So
Keyword(s):  
Mental Health ◽  
Big Data ◽  
Social Network ◽  
Data Analysis ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Correlation Analysis ◽  
Student Athletes ◽  
Big Data Analysis ◽  
Degree Centrality

In the present study, we used big data analysis to examine the key attributes related to stress and mental health among Korean Taekwondo student-athletes. Keywords included “Taekwondo + Student athlete + Stress + Mental health”. Naver and Google databases were searched to identify research published between 1 January 2010 and 31 December 2019. Text-mining analysis was performed on unstructured texts using TEXTOM 4.5, with social network analysis performed using UCINET 6. In total, 3149 large databases (1.346 MB) were analyzed. Two types of text-mining analyses were performed, namely, frequency analysis and term frequency-inverse document frequency analysis. For the social network analysis, the degree centrality and convergence of iterated correlation analysis were used to deduce the node-linking degree in the network and to identify clusters. The top 10 most frequently used terms were “stress”, “Taekwondo”, “health”, “player”, “student”, “mental”, “exercise”, “mental health”, “relieve”, and “child.” The top 10 most frequently occurring results of the TF-IDF analysis were “Taekwondo”, “health”, “player”, “exercise”, “student”, “mental”, “stress”, “mental health”, “child” and “relieve”. The degree centrality analysis yielded similar results regarding the top 10 terms. The convergence of iterated correlation analysis identified six clusters: student, start of dream, diet, physical and mental, sports activity, and adult Taekwondo center. Our results emphasize the importance of designing interventions that attenuate stress and improve mental health among Korean Taekwondo student-athletes.

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.

Interprofessional collaboration in mental health settings: a social network analysis

2018 ◽  
Vol 33 (5) ◽  
pp. 497-503 ◽  
Author(s):  
Chiara Pomare ◽  
Janet C Long ◽  
Louise A Ellis ◽  
Kate Churruca ◽  
Jeffrey Braithwaite
Keyword(s):  
Mental Health ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Interprofessional Collaboration ◽  
Mental Health Settings

scholarly journals Mapping global Muslim mental health research: analysis of trends in the English literature from 2000 to 2015

2019 ◽  
Vol 6 ◽  
Author(s):  
H. H. Altalib ◽  
K. Elzamzamy ◽  
M. Fattah ◽  
S. S. Ali ◽  
R. Awaad
Keyword(s):  
Mental Health ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Health Research ◽  
Global Mental Health ◽  
English Literature ◽  
Mental Health Research ◽  
Post Traumatic Stress ◽  
The Impact

Background.By 2030, the global Muslim population is expected to reach 2.2 billion people. The representations of Islam and Muslims in the media and academic literature may unconsciously impact how clinicians perceive and approach their Muslim patients. Our study focuses on the emerging Muslim mental health (MMH) literature using bibliometric analysis, specifically social network analysis of word co-occurrence and co-authorship networks of academic publications, to describe how the content of MMH discourse is evolving.Methods.We conducted an Ovid search (including Medline and PsycInfo databases) to identify articles written in English from 2000 to 2015 that had the terms ‘Islam’ and/or ‘Muslim’ in the abstract as well as research conducted in Muslim-majority countries and among Muslim minorities in the rest of the world.Results.Of the 2652 articles on MMH, the majority (65.6%) focused on describing psychopathology; the minority (11.2%) focused on issues around stigma, religiosity, spirituality, identity, or acculturation. Among the top 15 most frequent terms in abstracts were ‘post-traumatic stress disorder’, ‘violence’, ‘fear’, ‘trauma’, and ‘war’. Social network analysis showed there was little collaborative work across regions.Conclusions.The challenges of producing MMH research are similar to the challenges faced across global mental health research. Much of the MMH research reflects regional challenges such as the impact of conflict and violence on mental health. Continued efforts to develop global mental health researchers through cross-cultural exchanges, academic journals' dedicated sections and programs for global mental health recruitment, and online training are needed to address the gap in research and collaborations.

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