@article{ref1,
title="Predicting Imminent Suicidal Thoughts and Nonfatal Attempts: The Role of Complexity",
journal="Clinical psychological science",
year="2019",
author="Ribeiro, J.D. and Huang, X. and Fox, K.R. and Walsh, C.G. and Linthicum, K.P.",
volume="7",
number="5",
pages="941-957",
abstract="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 &quot;warning signs&quot;; 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>",
language="en",
issn="2167-7026",
doi="10.1177/2167702619838464",
url="http://dx.doi.org/10.1177/2167702619838464"
}