Suicide is a low frequency, high impact behavior. By extension, assessment of suicide is a critical component to effective mental health intervention. Research on suicide has expanded (see Bryan’s  Rethinking Suicide: Why Prevention Fails, and how We Can Do Better, for instance) to suggest that future prediction of risk is also more complicated that the historical linear pathway we prescribe (ideation leads to planning and planning leads to attempts). In short, suicide is a high impact behavior which we are poorly able to predict.
Further complicating the assessment of suicide risk is the robust research base which has found clinical judgement limited. Consistently, clinical decision making underperforms relative to statistical/actuarial methods (see Meehl, 1954 or Ægisdóttir et al., 2006 for a meta-analytic review). Such findings reflect that we are poor prognosticators of future behavior based on our understanding of past behavior.
We wondered about the over-simplification of the type of prediction. In short, are all tasks (regardless of seriousness) equally poor at being predicted by clinical judgement OR are all actuarial measures created equal within the framework of measurement based care (Meehl’s original work was in support of the MMPI, rather than brief and less robustly validated measures which dominate clinical monitoring). Thus, our study looks at suicide risk in a sample of outpatient mental health Veteran patients, including both agreement between actuarial/judgement at intake and risk assessment over time. Big shoutout to Keegan Deihl (a recently adopted grad student) and Tristan Herring (undergrad lab member) for their work on this project, which is being presented at the 2021 Combat PTSD Conference.
Click here to download the poster PDF.
Click here to see the pre-recorded video presented to the 2021 Combat PTSD Conference (with presentation by PATS’ very own Tristan Herring)
As an aside on this project, I’m super proud of Tristan. He has been with the lab for a little under a year as an undergraduate research assistant and he is a rock star on this poster and one another paper currently submitted for publication. He has tackled learning confusion matrixes and classification statistics that have played a critical role on both projects. Grad programs, you can have him next year – if you’re lucky! A big hats off to Keegan as well. This is my first project working with him as my graduate student and I’m looking forward to more.