Linked Life Data

18245162

Source:http://linkedlifedata.com/resource/pubmed/id/18245162

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2008-2-4
pubmed:abstractText
Using clinical judgment alone, mental health professionals cannot predict individual patient violence much more accurately than chance. Clinicians could improve their prediction of violence if they routinely used structured risk assessment instruments, but they don't; the use of such tools for screening is not currently the standard of care in the United States and is not commonly reimbursed by insurance. The author argues, however, that clinicians actually can predict and prevent violence if they consider their patients as a group from the perspective of public-health epidemiology. Optimizing treatment for all patients will help prevent violence by the few who pose a risk of violence, even when such patients are not identified in advance.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Feb
pubmed:issn
1075-2730
pubmed:author
pubmed:issnType
Print
pubmed:volume
59
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
191-3
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
Preventing the unpredicted: managing violence risk in mental health care.
pubmed:affiliation
Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, DUMC 3071, Durham, NC 27710, USA. jeffrey.swanson@duke.edu
pubmed:publicationType
Journal Article, Research Support, N.I.H., Extramural