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The British Journal of Psychiatry
Article . 2019 . Peer-reviewed
License: Cambridge Core User Agreement
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Pragmatic neuroscience for clinical psychiatry

Authors: J. Douglas Steele; Martin P. Paulus;

Pragmatic neuroscience for clinical psychiatry

Abstract

SummaryMental health and substance use disorders are the leading cause of long-term disability and a cause of significant mortality, worldwide. However, it is widely recognised that clinical practice in psychiatry has not fundamentally changed for over half a century. The Royal College of Psychiatrists is reviewing its trainee curriculum to identify neuroscience that relates to psychiatric practice. To date though, neuroscience has had very little impact on routine clinical practice. We discuss how a pragmatic approach to neuroscience can address this problem together with a route to implementation in National Health Service care. This has implications for altered funding priorities and training future psychiatrists. Five training recommendations for psychiatrists are identified.Declaration of interestJ.D.S. receives direct funding from MRC Program Grant MR/S010351/1 aimed at developing machine learning-based methods for routinely acquired NHS data and indirect funding from the Wellcome Trust STRADL study. M.P.P. receives payments for an UpToDate chapter on methamphetamine and is principal investigator on the following grants: NIGMS P20GM121312 and NIDA U01 DA041089 and receives support from the William K. Warren Foundation.

Country
United Kingdom
Keywords

Psychiatry, Neurosciences, 610, Pragmatic psychiatry, /dk/atira/pure/subjectarea/asjc/2700/2738, machine learning, name=Psychiatry and Mental health, Education, Medical, Graduate, Humans, Curriculum, education and training

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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
13
Top 10%
Top 10%
Top 10%
Green
bronze