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Psychologists Develop Two Potent New Predictors of Suicide Risk

ScienceDaily (July 28, 2010) — Two powerful new tests developed by psychologists at Harvard University show great promise in predicting patients' risk of attempting suicide.

The work may help clinicians overcome their reliance on self-reporting by at-risk individuals, information that often proves misleading when suicidal patients wish to hide their intentions. Both new tests are easily administered within minutes on a computer, giving quick insight into how patients are thinking about suicide, as well as their propensity to attempt suicide in the near future.

"Experts have long sought a clear behavioral marker of suicide risk," says Harvard Professor of Psychology Matthew K. Nock, an author of two papers describing the new assessments of suicidal behavior. "The current approach, based on self-reporting, leads to predictions that are scarcely better than chance, since suicidal patients are often motivated to conceal or misrepresent their mental state. We sought to develop more sophisticated, objective measures of how psychiatric patients are thinking about suicide. Our work provides two important new tools clinicians can use in deciding how to treat potentially suicidal patients."

Nock and colleagues report on the tests in two papers, one in the current Journal of Abnormal Psychology and a second published in Psychological Science. Unlike many previous efforts focused on biological markers of suicidal behavior, their work identifies two behavioral markers: subjects' attention to suicide-related stimuli, and the extent to which they associate death or suicide with themselves.

In one study by Nock's group, 124 patients in a psychiatric emergency department were administered a modified Stroop test measuring speed in articulating the color of words on a computer screen. Suicidal individuals were found to pay more attention to suicide-related words than to neutral words.

"Suicide Stroop scores predicted six-month follow-up suicide attempts above and beyond well-known risk factors such as a history of suicide attempts, patients' reported likelihood of attempt, and clinicians' predictions regarding patients' likelihood of attempt," says co-author Christine B. Cha, a doctoral student in psychology at Harvard.

A second study adapted the Implicit Association Test developed by Harvard psychologist Mahzarin R. Banaji, using reaction times to semantic stimuli to measure 157 subjects' automatic mental associations -- in this case, the strength of associations between words related to "self" and words related to either "life" or "death/suicide." Participants were shown pairs of words on a screen, with response speed revealing unconscious associations between the terms. For instance, a rapid response to stimuli associating self with death/suicide suggests a strong unconscious association between the two.

Nock and his colleagues found that those participants with strong associations between self and death/suicide were six times more likely to attempt suicide within the next six months than those holding stronger associations between self and life.

"These findings suggest that a person's implicit cognition may guide which behavior he or she chooses to cope with extreme distress," Nock says. "More specifically, an implicit association with death/suicide may represent one of the final steps in the pathway to suicide."

Nock and Cha were joined on the two recent papers by co-authors Jennifer M. Park and Christine T. Finn of Harvard Medical School and Massachusetts General Hospital and Sadia Najmi, Tara L. Deliberto, Halina J. Dour, and Mahzarin R. Banaji of Harvard's Department of Psychology. The work was funded by the National Institute of Mental Health and the Norlien Foundation.

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The above story is reprinted from materials provided by Harvard University. The original article was written by Steve Bradt, Harvard Staff Writer.

Note: Materials may be edited for content and length. For further information, please contact the source cited above.


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Disclaimer: This article is not intended to provide medical advice, diagnosis or treatment. Views expressed here do not necessarily reflect those of ScienceDaily or its staff.

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