TY  - JOUR
PY  - 2019//
TI  - Predicting Imminent Suicidal Thoughts and Nonfatal Attempts: The Role of Complexity
JO  - Clinical psychological science
A1  - Ribeiro, J.D.
A1  - Huang, X.
A1  - Fox, K.R.
A1  - Walsh, C.G.
A1  - Linthicum, K.P.
SP  - 941
EP  - 957
VL  - 7
IS  - 5
N2  - For decades, our ability to predict suicidal thoughts and behaviors (STBs) has been at near-chance levels. The objective of this study was to advance prediction by addressing two major methodological constraints pervasive in past research: (a) the reliance on long follow-ups and (b) the application of simple conceptualizations of risk. Participants were 1,021 high-risk suicidal and/or self-injuring individuals recruited worldwide. Assessments occurred at baseline and 3, 14, and 28 days after baseline using a range of implicit and self-report measures. Retention was high across all time points (> 90%). Risk algorithms were derived and compared with univariate analyses at each follow-up. <br><br>RESULTS indicated that short-term prediction alone did not improve prediction for attempts, even using commonly cited "warning signs"; however, a small set of factors did provide fair-to-good short-term prediction of ideation. Machine learning produced considerable improvements for both outcomes across follow-ups. <br><br>RESULTS underscore the importance of complexity in the conceptualization of STBs. © The Author(s) 2019.<p /> <p>Language: en</p>
LA  - en
SN  - 2167-7026
UR  - http://dx.doi.org/10.1177/2167702619838464
ID  - ref1
ER  -