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        In Defence of Psychiatric Diagnoses

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        Author(s)
        Fellowes, Sam
        Language
        English
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        Abstract
        This open access book makes a distinctive contribution by providing a novel defence of psychiatric diagnoses. It defends psychiatric diagnoses by portraying them as idealised models understood in a neo-Kantian sense. It reject accounts which see psychiatric diagnoses as biomedical entities or as natural kinds. Drawing upon this neo-Kantian approach to scientific models, the book provides a novel metaphysical account of what psychiatric diagnoses are and novel epistemological guidelines for constructing psychiatric diagnoses. Psychiatric diagnoses are portrayed as models which abstract away from particular aspects of particular people to create generalised models that are applicable to multiple people. In Defence of Psychiatric Diagnoses is essential reading for all scholars and researchers of the philosophy of science especially those focussing on the philosophy of psychiatry.
        URI
        https://library.oapen.org/handle/20.500.12657/99865
        Keywords
        Psychiatric diagnoses; Philosophy of psychiatry; Neo-Kantianism; Scientific models; Symptoms
        DOI
        10.1007/978-3-031-74478-5
        ISBN
        9783031744785, 9783031744785, 9783031744778
        Publisher
        Springer Nature
        Publisher website
        https://www.springernature.com/gp/products/books
        Publication date and place
        Cham, 2025
        Grantor
        • Lancaster University - [...]
        Imprint
        Palgrave Macmillan
        Classification
        Philosophy
        Medical ethics and professional conduct
        Philosophy of science
        Psychiatry
        Pages
        251
        Rights
        http://creativecommons.org/licenses/by/4.0/
        • Imported or submitted locally

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        License

        • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

        Credits

        • logo EU
        • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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