A machine learning analysis of suicidal ideation and suicide attempt among U.S. youth and young adults from multilevel, longitudinal survey data
The distinct factors associated with SI and SA across diagnostic subgroups highlight the importance of targeted, subgroup-specific suicide prevention interventions. These findings emphasize the value of precise, data-driven approaches for suicide prevention among diverse populations and individuals with disabilities across the life-course.
Authors
- Jessica M. Kramer (ORCID: https://orcid.org/0000-0002-3433-2842)
- Anne V. Kirby (ORCID: https://orcid.org/0000-0003-0395-6684)
- Molly Jacobs (ORCID: https://orcid.org/0000-0001-5943-8507)
- Nicole M. Marlow (ORCID: https://orcid.org/0000-0001-8593-2090)
Institutions
- University of Utah (US)
- University of Florida (US)
Publication Details
- Journal
- Frontiers in Psychiatry
- Published
- 2025-02-24
- DOI
- https://doi.org/10.3389/fpsyt.2025.1511966
- Citations
- 3
- Primary Topic
- Suicide and Self-Harm Studies
- Type
- article
- Field-Weighted Citation Impact
- 6.92
Funders
- National Institute of Mental Health