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Understanding Suicide over the Life Course Using Data Science Tools within a Triangulation Framework.

Lily Johns1, Chuwen Zhong1, Briana Mezuk1

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Understanding suicide risk across the lifespan is crucial for prevention. This review proposes a life course framework and data science tools to identify modifiable suicide risk factors.

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Area of Science:

  • Public Health
  • Epidemiology
  • Mental Health Research

Background:

  • Suicide and suicidal behaviors represent significant global health challenges.
  • Current understanding of suicide risk variation over the lifespan is limited by methodological and conceptual issues.
  • Bridging the gap between suicide research and clinical practice necessitates novel approaches.

Purpose of the Study:

  • To present the life course framework as a model for studying suicide risk.
  • To explore the application of data science tools in identifying suicide risk factors.
  • To advocate for triangulation as a method to enhance rigor in suicide research.

Main Methods:

  • Review of the life course framework applied to suicide risk.
  • Discussion of data science methodologies for risk factor identification.
  • Emphasis on triangulation for research validity.

Main Results:

  • The life course framework organizes suicide risk across social relationships, health, housing, and employment.
  • Data science tools offer potential for discovering novel, modifiable suicide risk factors.
  • Triangulation can strengthen the rigor and address knowledge gaps in suicide research.

Conclusions:

  • A life course perspective is essential for a comprehensive understanding of suicide risk.
  • Integrating data science and triangulation can advance suicide prevention research.
  • Addressing existing knowledge gaps requires innovative methodological approaches.