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Using Exploratory Data Mining to Identify Important Correlates of Nonsuicidal Self-Injury Frequency
Brooke A Ammerman1, Ross Jacobucci2, Michael S McCloskey1
1Temple University.
Psychology of Violence
|November 6, 2018
Summary
The number of non-suicidal self-injury (NSSI) methods used is key to understanding NSSI frequency. Depression and suicide plans also significantly correlate with NSSI severity.
Area of Science:
- Psychology
- Psychiatry
- Behavioral Science
Background:
- Non-suicidal self-injury (NSSI) is associated with adverse outcomes, including increased impairment and suicidality.
- Understanding factors predicting NSSI frequency is crucial for assessing behavioral severity.
Purpose of the Study:
- Identify key correlates predicting non-suicidal self-injury (NSSI) frequency.
- Utilize exploratory data mining to uncover influential factors in NSSI severity.
Main Methods:
- Surveyed 712 undergraduate students with a history of NSSI.
- Employed lasso regression and random forests for data mining analysis.
Main Results:
- The number of NSSI methods was the primary predictor of NSSI frequency.
- Following the removal of NSSI methods, suicide plans and depressive symptoms emerged as significant correlates.
Conclusions:
- Findings confirm the link between NSSI frequency and methods used.
- Suicide plans and depression are implicated as significant factors in NSSI severity.
Keywords:
NSSI frequencyNSSI methodsexploratory data mininglasso regressionnon-suicidal self-injuryrandom forestsMore Related Videos
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