Using network models in person-centered care in psychiatry: How perspectivism could help to draw boundaries

Abstract

In this paper, we explore the conceptual problems that arise when using network analysis in person-centered care (PCC) in psychiatry. Personalized network models are potentially helpful tools for PCC, but we argue that using them in psychiatric practice raises boundary problems, i.e., problems in demarcating what should and should not be included in the model, which may limit their ability to provide clinically-relevant knowledge. Models can have explanatory and representational boundaries, among others. We argue that perspectival reasoning can make more explicit what questions personalized network models can address in PCC, given their boundaries.

Keywords: boundary problem; network analysis; person-centered care; personalized models; perspectivism; psychiatry; topological explanation.

Figure 1

A hypothetical example to illustrate the influence of node selection on local topological properties in a network. In (A), we see a hypothetical network that consists of six nodes. (B) demonstrates that node 3 has the highest node degree, closeness centrality, and betweenness centrality. (C) shows the same network in which node 3 is removed. (D) shows the influence of this removal on the network's centrality measures. Now, nodes 4–6 have the highest node degree, and node 4 has the highest closeness and betweenness centrality. Moreover, the betweenness centrality values of nodes 5 and 6 have strongly increased.