This article examines the revision of the Diagnostic and Statistical Manual of Mental Disorders and its claim of incorporating a “greater cultural sensitivity.” The analysis reveals that the manual conveys mixed messages as it explicitly addresses the critique of being ethnocentric and having a static notion of culture yet continues in a similar fashion when culture is applied in diagnostic criteria. The analysis also relates to current trends in psychiatric nosology that emphasize neurobiology and decontextualize distress and points to how (...) the DSM-5 risks serving as an ethnic dividing line in psychiatry by making sociocultural context relevant only for some patients. (shrink)

Machine learning (ML) has emerged as a promising tool in psychiatry, revolutionising diagnostic processes and patient outcomes. In this paper, I argue that while ML studies show promising initial results, their application in mimicking clinician-based judgements presents inherent limitations (Shatte et al. in Psychol Med 49:1426–1448. https://doi.org/10.1017/S0033291719000151, 2019). Most models still rely on DSM (the Diagnostic and Statistical Manual of Mental Disorders) categories, known for their heterogeneity and low predictive value. DSM's descriptive nature limits the validity of psychiatric diagnoses, which (...) leads to overdiagnosis, comorbidity, and low remission rates. The application in psychiatry highlights the limitations of supervised ML techniques. Supervised ML models inherit the validity issues of their training data set. When the model's outcome is a DSM classification, this can never be more valid or predictive than the clinician’s judgement. Therefore, I argue that these models have little added value to the patient. Moreover, the lack of known underlying causal pathways in psychiatric disorders prevents validating ML models based on such classifications. As such, I argue that high accuracy in these models is misleading when it is understood as validating the classification. In conclusion, these models will not will not offer any real benefit to patient outcomes. I propose a shift in focus, advocating for ML models to prioritise improving the predictability of prognosis, treatment selection, and prevention. Therefore, data selection and outcome variables should be geared towards this transdiagnostic goal. This way, ML can be leveraged to better support clinicians in personalised treatment strategies for mental health patients. (shrink)

The concept of a “validator” as a unit of evidence for the validity of a psychiatric category has been important for more than fifty years. Validator evidence is aggregated by expert committees (for the Diagnostic and Statistical Manual of Mental Disorders (DSM), these are referred to as “workgroups”), which use the results to make nosological decisions. Through an examination of the recent history of psychiatric research, this paper argues that it is time to reassess this traditional practice. It concludes with (...) specific suggestions for going forward. (shrink)

In current mental health care psychiatric conditions are defined as compilations of symptoms. These symptom-based disease categories have been severely criticised as contingent and boundless, facilitating the rise to epidemic proportions of such conditions as depression. In this article we look beyond symptoms and stress the role of epidemiology in explaining the current situation. By analysing the parallel development of cardiovascular disease and depression management in Finland, we argue, firstly, that current mental health care shares with the medicine of chronic (...) somatic conditions an attachment to risk factor epidemiology, which accentuates risk and prevention in disease management. However, secondly, due to the symptom-based definitions of psychiatric conditions, depression management cannot differentiate properly between symptoms, signs and risk factors such as, for example, cardiovascular medicine, but treats symptoms as signs or risk factors in contexts of treatment and prevention. Consequently, minor at-risk conditions have become difficult to separate from proper cases of depression. (shrink)

L'importance que les défenseurs du Research Domain Criteria (RDoC) accordent aux circuits du cerveau dans la compréhension des désordres mentaux a conduit certains chercheurs à suspecter qu'on avait affaire à un programme de recherche foncièrement réductionniste. Puisque le RDoC devrait éventuellement affecter la psychiatrie clinique, on a craint qu'elle ne se transforme en une science neuro-comportementale appliquée sans tenir compte de l'esprit (mindless). Cet article montre en quoi le projet du RDoC s'éloigne du réductionnisme classique et comment il en évite (...) les écueils. Pour cette raison, du moins en principe, le RDoC pourrait enrichir plutôt qu'appauvrir la psychiatrie clinique. Pour conclure, les effets néfastes produits par le réductionnisme de façade des défenseurs du RDoC seront soulignés. (shrink)