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Titlebook: India’s Mental Healthcare Act, 2017; Building Laws, Prote Richard M. Duffy,Brendan D. Kelly Book 2020 Springer Nature Singapore Pte Ltd. 20

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書(shū)目名稱India’s Mental Healthcare Act, 2017
副標(biāo)題Building Laws, Prote
編輯Richard M. Duffy,Brendan D. Kelly
視頻videohttp://file.papertrans.cn/464/463558/463558.mp4
概述Provides an in-depth analysis of the Indian Mental Healthcare Act 2017.Offers a healthcare perspective rather than a legal one.Serves as a guide for anyone using the Indian Mental Healthcare Act in a
圖書(shū)封面Titlebook: India’s Mental Healthcare Act, 2017; Building Laws, Prote Richard M. Duffy,Brendan D. Kelly Book 2020 Springer Nature Singapore Pte Ltd. 20
描述.This book comprehensively discusses the background to the passing of India‘s revolutionary Mental Healthcare Act, 2017, offering a detailed description of the Act itself and a rigorous analysis in the context of the CRPD and the World Health Organization (WHO) standards for mental health law. It examines the fine balance, between complying with the CRPD while still delivering practical, humane, and implementable legislation. It explores how this legislation was shaped by the WHO standards and provides insights into areas where the Indian legislators deviated from these guidelines and why. Taking India as an example, it highlights what is possible in other low- and middle-income countries. Further it covers key issues in mental health, identifying potential competing interests and exploring the difficulties and limitations of international guidelines..The book is a valuable resource for psychiatrists, nurses, social workers, non-governmental organizations and all mental healthcareworkers in India and anyone studying human rights law..
出版日期Book 2020
關(guān)鍵詞Indian Mental Healthcare Act 2017; mental health law; Human rights; Rights of Persons with Disabilities
版次1
doihttps://doi.org/10.1007/978-981-15-5009-6
isbn_softcover978-981-15-5011-9
isbn_ebook978-981-15-5009-6
copyrightSpringer Nature Singapore Pte Ltd. 2020
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tive function obtains its most extreme value while satisfying the constraints. Some methods try to make the solution feasible by using penalty function methods, but the performance is not always satisfactory since the selection of the penalty parameters for the problem at hand is not a straightforwa
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Richard M. Duffy,Brendan D. Kelly challenges in the current conception of RL stems from the theory’s way to define states. Whereas states under laboratory conditions are tractable (due to the Markov property), states in real-world RL are high-dimensional, continuous and partially observable. Hence, effective learning and generaliza
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