The network theory of psychological disorders posits that systems of symptoms cause, or are associated with, the expression of other symptoms. Substantial literature on symptom networks has been published to date, although no systematic review has been conducted exclusively on symptom networks of schizophrenia, schizoaffective disorder, and schizophreniform (people diagnosed with schizophrenia; PDS). This study aims to compare statistics of the symptom network publications on PDS in the last 21 years and identify congruences and discrepancies in the literature. More specifically, we will focus on centrality statistics. Thirty-two studies met the inclusion criteria. The results suggest that cognition, and social, and occupational functioning are central to the network of symptoms. Positive symptoms, particularly delusions were central among participants in many studies that did not include cognitive assessment. Nodes representing cognition were most central in those studies that did. Nodes representing negative symptoms were not as central as items measuring positive symptoms. Some studies that included measures of mood and affect found items or subscales measuring depression were central nodes in the networks. Cognition, and social, and occupational functioning appear to be core symptoms of schizophrenia as they are more central in the networks, compared to variables assessing positive symptoms. This seems consistent despite heterogeneity in the design of the studies.

Background: Achieving the United Nations Sustainable Development Goals (SDGs) is beyond the capacity of any single organisation. The model for Responsible Research and Innovation (RRI) includes principles of engaging stakeholders and suggests that an engaged, multi-sectoral approach hold promise to mobilise humanity to solve complex and urgent global issues. Methods: This scoping review explores the characteristics of effective and sustainable inter-organisational networks for fostering RRI in service of the SDGs. An inductive-deductive search of prior studies (1990-2020), with the exception of Benson\'s (1975) seminal work was conducted, which focused on strategies to initiate and maintain inter-organisational networks relevant to the implementation of RRI and/or SDGs. The search began with themes derived from prior network theory, focusing on: (a) the type and function of networks; (b) the aims and vision; and (c) the relationships between networks and network members. In total, 55 articles on inter-organisational network theory were included for the final analysis. Results: Results are reported under themes of: (1) Effectiveness, Sustainability, and Success; (2) Governance and Management; and (3) Network Relationship. Network structures, forms of management and funding are linked to sustainable networks. Potential threats include power imbalances within networks, and internal and external factors that may affect relationships at network and community levels. Few studies examine diversity or cultural viewpoints. Studies highlight the benefits of networks such as enhancing knowledge sharing among researchers, practitioners, and other stakeholders. Conclusions: The effectiveness of the managerial structure may be observed as outputs of the intention and values of an inter-organisational network. Our review demonstrates that a global inter-organisational network approach is achievable. Such a network would have many benefits, including allowing organisations to be responsive and flexible towards change and innovation. Keywords RRI, SDGs, network theory, inter-organisational networks, governance, trust.

BACKGROUND: Interorganizational networks in healthcare do not always attain their goals. Existing models outline the factors that could explain poor network performance: governance; structure; and the alignment of professional, organizational and network levels. However, these models are very generic and assume a functional approach. We investigate available empirical knowledge on how network structure and governance relate to each other and to network performance in a multilevel context, to get deeper insight, supported with empirics, of why networks (fail to) achieve their goals.
METHODS: A systematic literature review based on a search of Web of Science, Business Source Complete and PubMed was executed in May 2021 and repeated in January 2022. Full papers were included if they were written in English and reported empirical data in a healthcare interorganizational setting. Included papers were coded for the topics of governance, structure, performance and multilevel networks. Papers from the scientific fields of management, administration and healthcare were compared. Document citation and bibliographic coupling networks were visualized using Vosviewer, and network measures were calculated with UCINET.
RESULTS: Overall, 184 papers were included in the review, most of which were from healthcare journals. Research in healthcare journals is primarily interested in the quality of care, while research in management and administration journals tend to focus on efficiency and financial aspects. Cross-citation is limited across different fields. Networks with a brokered form of governance are the most prevalent. Network performance is mostly measured at the community level. Only a few studies employed a multilevel perspective, and interaction effects were not usually measured between levels.
CONCLUSIONS: Research on healthcare networks is fragmented across different scientific fields. The current review revealed a range of positive, negative and mixed effects and points to the need for more empirical research to identify the underlying reasons for these outcomes. Hardly any empirical research is available on the effects of different network structures and governance modes on healthcare network performance at different levels. We find a need for more empirical research to study healthcare networks at multiple levels while acknowledging hybrid governance models that may apply across different levels.

The network theory of psychopathology proposes that mental disorders arise from direct interactions between symptoms. This theory provides a promising framework to understand the development and maintenance of mental disorders such as depression. In this narrative review, we summarize the literature on network studies in the field of depression. Four methodological network approaches are distinguished: (i) studies focusing on symptoms at the macro-level vs. (ii) on momentary states at the micro-level, and (iii) studies based on cross-sectional vs. (iv) time-series (dynamic) data. Fifty-six studies were identified. We found that different methodological approaches to network theory yielded largely inconsistent findings on depression. Centrality is a notable exception: the majority of studies identified either positive affect or anhedonia as central nodes. To aid future research in this field, we outline a novel complementary network theory, the momentary affect dynamics (MAD) network theory, to understand the development of depression. Furthermore, we provide directions for future research and discuss if and how networks might be used in clinical practice. We conclude that more empirical network studies are needed to determine whether the network theory of psychopathology can indeed enhance our understanding of the underlying structure of depression and advance clinical treatment.

The network approach to psychopathology posits that mental disorders can be conceptualized and studied as causal systems of mutually reinforcing symptoms. This approach, first posited in 2008, has grown substantially over the past decade and is now a full-fledged area of psychiatric research. In this article, we provide an overview and critical analysis of 363 articles produced in the first decade of this research program, with a focus on key theoretical, methodological, and empirical contributions. In addition, we turn our attention to the next decade of the network approach and propose critical avenues for future research in each of these domains. We argue that this program of research will be best served by working toward two overarching aims: (a) the identification of robust empirical phenomena and (b) the development of formal theories that can explain those phenomena. We recommend specific steps forward within this broad framework and argue that these steps are necessary if the network approach is to develop into a progressive program of research capable of producing a cumulative body of knowledge about how specific mental disorders operate as causal systems.

Network analysis (NA) is an analytical tool that allows one to explore the map of connections and eventual dynamic influences among symptoms and other elements of mental disorders. In recent years, the use of NA in psychopathology has rapidly grown, which calls for a systematic and critical analysis of its clinical utility.
Following PRISMA guidelines, a systematic review of published empirical studies applying NA in psychopathology, between 2010 and 2017, was conducted. We included the literature published in PubMed and PsycINFO using as keywords any combination of \"network analysis\" with the terms \"anxiety,\" \"affective disorders,\" \"depression,\" \"schizophrenia,\" \"psychosis,\" \"personality disorders,\" \"substance abuse\" and \"psychopathology.\"
The review showed that NA has been applied in a plethora of mental disorders in adults (i.e., 13 studies on anxiety disorders; 19 on mood disorders; 7 on psychosis; 1 on substance abuse; 1 on borderline personality disorder; 18 on the association of symptoms between disorders), and 6 on childhood and adolescence.
A critical examination of the results of each study suggests that NA helps to identify, in an innovative way, important aspects of psychopathology like the centrality of the symptoms in a given disorder as well as the mutual dynamics among symptoms. Yet, despite these promising results, the clinical utility of NA is still uncertain as there are important limitations on the analytic procedures (e.g., reliability of indices), the type of data included (e.g., typically restricted to secondary analysis of already published data), and ultimately, the psychometric and clinical validity of the results.