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  1. The normativity in psychiatric nosology. An analysis of how the DSM-5’s psychopathology conceptualisation can be integrated.Fredrik D. Moe & Paola de Cuzzani - 2024 - Philosophical Psychology 37 (3):707-732.
    The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) uses the conceptualization of psychopathology to make psychiatric diagnoses operational. The use of explicit operational criteria appears to be based on an implicit neo-positivist epistemology. Operationalism involves an excessive focus on quantitative descriptions of behavior manifestations, contesting that psychopathology is understood as a deviation from the normal or the average in a given population. Consequently, the normal and the psychopathological become homogeneous. Our analysis investigates if this neo-positivist epistemology narrows (...)
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  • Is There an App for That?: Ethical Issues in the Digital Mental Health Response to COVID-19.Joshua August Skorburg & Josephine Yam - 2022 - American Journal of Bioethics Neuroscience 13 (3):177-190.
    As COVID-19 spread, clinicians warned of mental illness epidemics within the coronavirus pandemic. Funding for digital mental health is surging and researchers are calling for widespread adoption to address the mental health sequalae of COVID-19. -/- We consider whether these technologies improve mental health outcomes and whether they exacerbate existing health inequalities laid bare by the pandemic. We argue the evidence for efficacy is weak and the likelihood of increasing inequalities is high. -/- First, we review recent trends in digital (...)
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  • Persons or datapoints?: Ethics, artificial intelligence, and the participatory turn in mental health research.Joshua August Skorburg, Kieran O'Doherty & Phoebe Friesen - 2024 - American Psychologist 79 (1):137-149.
    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 (...)
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