The network approach to psychopathology: a review of the literature 2008–2018 and an agenda for future research

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

Psychological Perturbation Data on Attitudes Towards the Consumption of Meat

We present a dataset on participants’ attitudes towards the consumption of meat (N = 30). Participants were presented with a baseline questionnaire entailing 11 statements. After a baseline measurement, we perturbed the participant’s opinion on one of the 11 items, after which the participant completed the same questionnaire. By repeating this procedure for each of the 11 items, we measured to what extent the perturbation changed the participant’s baseline score. In addition, we asked participants to draw the influence of a specific item onto the other items in a network format. The data are suitable for various purposes, like causal inference and the malleability of attitudes

Reporting standards for psychological network analyses in cross-sectional data

Stress and Psychopatholog

Reporting standards for psychological network analyses in cross-sectional data

Stress and Psychopatholog

Team dynamics and clinician's experience influence decision-making during Upper-GI multidisciplinary team meetings: A multiple case study

BACKGROUND: The probability of undergoing treatment with curative intent for esophagogastric cancer has been shown to vary considerately between hospitals of diagnosis. Little is known about the factors that attribute to this variation. Since clinical decision making (CDM) partially takes place during an MDTM, the aim of this qualitative study was to assess clinician's perspectives regarding facilitators and barriers associated with CDM during MDTM, and second, to identify factors associated with CDM during an MDTM that may potentially explain differences in hospital practice. METHODS: A multiple case study design was conducted. The thematic content analysis of this qualitative study, focused on 16 MDTM observations, 30 semi-structured interviews with clinicians and seven focus groups with clinicians to complement the collected data. Interviews were transcribed ad verbatim and coded. RESULTS: Factors regarding team dynamics that were raised as aspects attributing to CDM were clinician's personal characteristics such as ambition and the intention to be innovative. Clinician's convictions regarding a certain treatment and its outcomes and previous experiences with treatment outcomes, and team dynamics within the MDTM influenced CDM. In addition, a continuum was illustrated. At one end of the continuum, teams tended to be more conservative, following the guidelines more strictly, versus the opposite in which hospitals tended towards a more invasive approach maximizing the probability of curation. CONCLUSION: This study contributes to the awareness that variation in team dynamics influences CDM during an MDTM

Network Models to Organize a Dispersed Literature: The Case of Misunderstanding Analysis of Covariance

We outline a network method to synthesize a literature overview from search results obtained by multiple team members. Several network statistics are used to create a single representativeness ranking. We illustrate the method with the dispersed literature on a common misinterpretation of analysis of covariance (ANCOVA). The network method yields a top ten list of the most relevant articles that students and researchers can take as a point of departure for a more detailed study on this topic. The proposed methodology is implemented in Shiny, an open-source R package