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Threshold Assessment Grid (TAG): the development of a valid and brief scale to assess the severity of mental illness.

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Title: Threshold Assessment Grid (TAG): the development of a valid and brief scale to assess the severity of mental illness.
Authors: Slade, M., Powell, R., Rosen, A., Strathdee, G.
Source: Social Psychiatry & Psychiatric Epidemiology. 2000, Vol. 35 Issue 2, p78-85. 8p.
Subjects: Mental illness, Mental health services
Abstract: Background: Lack of consensus about the meaning of severe mental illness makes it difficult to prioritise the severely mentally ill for specialist mental health care. The goal of this study was to develop a valid and brief assessment of severity of mental illness.Method: Six search workshops (n = 57) using consensus techniques developed a draft assessment acceptable to users, carers, practitioners and policy makers. A two-round Delphi consultation (n = 58) was held to identify consensus on this instrument.Results: Search workshops agreed seven domains relevant to identifying the severely mentally ill: intentional and unintentional self-harm, risk from and to others, and survival, psychological, and social needs and disabilities. The Delphi consultation indicated at least agreement with all aspects in both rounds.Conclusions: The Threshold Assessment Grid (TAG) is a brief method of identifying the severely mentally ill, which has adequate face, concurrent, construct and content validity. [ABSTRACT FROM AUTHOR]
Copyright of Social Psychiatry & Psychiatric Epidemiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Threshold Assessment Grid (TAG): the development of a valid and brief scale to assess the severity of mental illness.
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  Data: <searchLink fieldCode="AR" term="%22Slade%2C+M%2E%22">Slade, M.</searchLink><br /><searchLink fieldCode="AR" term="%22Powell%2C+R%2E%22">Powell, R.</searchLink><br /><searchLink fieldCode="AR" term="%22Rosen%2C+A%2E%22">Rosen, A.</searchLink><br /><searchLink fieldCode="AR" term="%22Strathdee%2C+G%2E%22">Strathdee, G.</searchLink>
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  Data: <searchLink fieldCode="JN" term="%22Social+Psychiatry+%26+Psychiatric+Epidemiology%22">Social Psychiatry & Psychiatric Epidemiology</searchLink>. 2000, Vol. 35 Issue 2, p78-85. 8p.
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  Data: <bold>Background: </bold>Lack of consensus about the meaning of severe mental illness makes it difficult to prioritise the severely mentally ill for specialist mental health care. The goal of this study was to develop a valid and brief assessment of severity of mental illness.<bold>Method: </bold>Six search workshops (n = 57) using consensus techniques developed a draft assessment acceptable to users, carers, practitioners and policy makers. A two-round Delphi consultation (n = 58) was held to identify consensus on this instrument.<bold>Results: </bold>Search workshops agreed seven domains relevant to identifying the severely mentally ill: intentional and unintentional self-harm, risk from and to others, and survival, psychological, and social needs and disabilities. The Delphi consultation indicated at least agreement with all aspects in both rounds.<bold>Conclusions: </bold>The Threshold Assessment Grid (TAG) is a brief method of identifying the severely mentally ill, which has adequate face, concurrent, construct and content validity. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Social Psychiatry & Psychiatric Epidemiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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