ABSTRACT: Abilify MyCite was granted regulatory approval in 2017, becoming the world’s first “smart pill” that could digitally track whether patients had taken their medication. The new technology was introduced as one that had gained the support of patients and ethicists alike, and could contribute to solving the widespread and costly problem of patient nonadherence. Here, we offer an in-depth exploration of this narrative, through an examination of the origins and development of Abilify, the drug that would later become MyCite. (...) This history illuminates how an antipsychotic can become a top-selling drug and maintain its blockbuster status for more than a decade. It also provides a detailed case study for how knowledge is constructed within the logic of biomedical capitalism, providing impetus to reexamine claims regarding how MyCite addresses patient nonadherence, engenders patient support, and is ethicist-approved. (shrink)

The last few years have seen a marked increase of interest in the ethics of psychotherapy. As the field of mental health has recently taken on a new level of prominence, renewed commitment to the ethical analysis of psychiatric and psychological treatment is clearly required. In this review essay, we survey recent work on psychotherapy ethics, taking a critical yet generally sympathetic view of the new literature. There are important considerations that remain neglected or overlooked entirely, in our view, and (...) several substantive and methodological trends are in need of diagnosis and critical response. We structure the discussion around several themes, including the inherently moral nature of psychotherapy, the heterogeneity of such practices, the significance of social, cultural, and historical context, and, finally, questions concerning prevailing methodological assumptions. (shrink)

This article identifies and examines a tension in mental health researchers’ growing enthusiasm for the use of computational tools powered by advances in artificial intelligence and machine learning (AI/ML). Although there is increasing recognition of the value of participatory methods in science generally and in mental health research specifically, many AI/ML approaches, fueled by an ever-growing number of sensors collecting multimodal data, risk further distancing participants from research processes and rendering them as mere vectors or collections of data points. The (...) imperatives of the “participatory turn” in mental health research may be at odds with the (often unquestioned) assumptions and data collection methods of AI/ML approaches. This article aims to show why this is a problem and how it might be addressed. (shrink)