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How do explicit, implicit, and sociodemographic measures relate to concurrent suicidal ideation? A comparative

René Freichel1,2, Sercan Kahveci3,4, Brian O'Shea2,5

  • 1Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands.

Suicide & Life-Threatening Behavior
|November 14, 2023
PubMed
Summary

Implicit measures of suicide cognitions did not improve prediction of current suicidal thoughts when combined with explicit factors. Mood and past behaviors were the strongest predictors in this machine learning study.

Keywords:
explicit suicide cognitionsimplicit suicide cognitionsmachine learningpredictive utilityself-harmsuicidal ideation

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Area of Science:

  • Psychiatry
  • Psychology
  • Computational Social Science

Background:

  • Suicide is a significant public health concern with numerous identified risk factors.
  • Recent research explored implicit self-harm and suicide cognitions as potential predictors of suicidal behavior.
  • Limited understanding exists regarding the combined effects of implicit and explicit risk factors on concurrent suicidal ideation.

Purpose of the Study:

  • To evaluate the utility of implicit self-harm and suicide cognitions in predicting concurrent desire to self-harm or die.
  • To assess the predictive power of implicit measures in conjunction with explicit factors using machine learning.

Main Methods:

  • Utilized machine learning techniques on an online community sample of 6855 participants.
  • Investigated the predictive accuracy of implicit and explicit suicide cognitions for suicidal ideation.
  • Employed gradient boosting for prediction modeling.

Main Results:

  • Gradient boosting achieved 83% accuracy in predicting the desire to self-harm.
  • Key predictors identified were mood, explicit associations, and history of suicidal thoughts/behaviors.
  • Implicit measures offered minimal to no additional predictive value for concurrent suicidal ideation.

Conclusions:

  • The study focused on concurrent prediction of explicit suicidal ideation.
  • Future research should explore the prospective predictive utility of implicit suicide cognitions for suicidal behavior using machine learning.
  • The findings highlight the current importance of explicit factors and mood in predicting immediate suicidal ideation.