Dataset Information
Using recursive partitioning to predict presence and severity of suicidal ideation amongst college students.
Ontology highlight
ABSTRACT: Objective
Predicting the presence and severity of suicidal ideation in college students is important, as deaths by suicide amongst young adults have increased in the past 20 years.Participants
We recruited college students (N = 5494) from ten universities across eight states.Method
Participants answered three questionnaires related to lifetime and past month suicidal ideation, and an indicator of suicidal ideation in a DSM-5 symptom measure. We used recursive partitioning to predict the presence, absence, and severity, of suicidal ideation.Results
Recursive partitioning models varied in their accuracy and performance. The best-performing model consisted of predictors and outcomes measured by the DSM-5 Level 1 Cross-Cutting Symptom Measure. Sexual orientation was also an important predictor in most models.Conclusions
A single measure of DSM-5 symptom severity may help universities understand suicide severity to promote targeted interventions. Though further work is needed, as similar scaling amongst predictors could have influenced the model.
SUBMITTER: McCool MW
PROVIDER: S-EPMC11550263 | biostudies-literature | 2025 Oct
REPOSITORIES: biostudies-literature
Publications
Using recursive partitioning to predict presence and severity of suicidal ideation amongst college students.
Journal of American college health : J of ACH 20240510 8
<h4>Objective</h4>Predicting the presence and severity of suicidal ideation in college students is important, as deaths by suicide amongst young adults have increased in the past 20 years.<h4>Participants</h4>We recruited college students (<i>N</i> = 5494) from ten universities across eight states.<h4>Method</h4>Participants answered three questionnaires related to lifetime and past month suicidal ideation, and an indicator of suicidal ideation in a DSM-5 symptom measure. We used recursive parti ...[more]