Nina S. de Boer

Dominant psychiatric explanations, i.e., explanations of why mental disorders or specific collections of psychiatric symptoms occur that are popular and widely adopted in a given community or society, can influence the self-narratives of people with psychiatric diagnoses. In this article, I resort to the literature on _master narratives_ to systematically examine how dominant psychiatric explanations shape the self-narratives of people with psychiatric diagnoses and how these self-narratives are valued and perceived. To illustrate my claims, I use biomedical explanations of depression as a case study. When we experience mental ill-health, we may formulate a _psychiatric self-narrative thread_ to understand these experiences, do justice to the changes in our embodied and affective experiences, and make our experiences understandable to others. In daily life, psychiatric explanations are frequently extended into _psychiatric explanatory narratives_ that support claims about the lives of people with psychiatric diagnoses. The explanatory narratives of dominant psychiatric explanations can be characterized as master narratives that shape psychiatric self-narrative threads and how we and others value and perceive them. This characterization provides new insights into the relationship between dominant psychiatric explanatory narratives and two narrative values: the coherence of self-narratives and our ability to imagine possibilities for reducing our mental suffering. Hence, this article sheds new light on how dominant psychiatric explanations in general, and biomedical explanations of depression specifically, influence self-understanding.

Limits of the Numerical calls for the (re)contextualization of the numerical in the social domain and emphasizes that using quantitative data has epistemic and practical/moral considerations that may not align. In this review essay, we evaluate these claims using a case study, viz. the personalized, clinical experience sampling method (ESM) in mental health care. This case study (1) nuances claims made in Limits of the Numerical regarding the generality and non-contextuality of numerical data, and (2) highlights two additional dimensions to the numerical that have been underexposed in the book (i.e., reactivity and Ballung concepts).

Borsboom and colleagues have recently proposed a “network theory” of psychiatric disorders that conceptualizes psychiatric disorders as relatively stable networks of causally interacting symptoms. They have also claimed that the network theory should include non-symptom variables such as environmental factors. How are environmental factors incorporated in the network theory, and what kind of explanations of psychiatric disorders can such an “extended” network theory provide? The aim of this article is to critically examine what explanatory strategies the network theory that includes both symptoms and environmental factors can accommodate. We first analyze how proponents of the network theory conceptualize the relations between symptoms and between symptoms and environmental factors. Their claims suggest that the network theory could provide insight into the causal mechanisms underlying psychiatric disorders. We assess these claims in light of network analysis, Woodward’s interventionist theory, and mechanistic explanation, and show that they can only be satisfied with additional assumptions and requirements. Then, we examine their claim that network characteristics may explain the dynamics of psychiatric disorders by means of a topological explanatory strategy. We argue that the network theory could accommodate topological explanations of symptom networks, but we also point out that this poses some difficulties. Finally, we suggest that a multilayer network account of psychiatric disorders might allow for the integration of symptoms and non-symptom factors related to psychiatric disorders and could accommodate both causal/mechanistic and topological explanations.

In this paper, we explore the conceptual problems arising 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 we can make more explicit what kind of questions personalized network models can address in PCC, given their representational and explanatory boundaries, using perspectival reasoning.