ArticleYonsei medical journal2024
The Prediction Model of Non-Suicidal Self-Injury in Psychiatric Patients Using Decision Tree Analysis.
Article in Yonsei medical journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it, 4 citations in OpenAlex.
- Can machine learning predict non-suicidal self-injury? A systematic review and meta-analysis.Frontiers in public health · 2026Pooled it
- A pilot randomized controlled trial of mentalization-based treatment and structured clinical management for outpatients with borderline personality disorder in South Korea.Borderline personality disorder and emotion dysregulation · 2026Article
- Article
- Cognitive Profile Discrepancy as a Possible Predictor of Emotion Dysregulation in a Clinical Sample of Female Adolescents with Suicidal Behavior.European journal of investigation in health, psychology and education · 2024Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
purposeThe prevalence rate of non-suicidal self-injury (NSSI) in the clinical population is higher than that in the community sample, necessitating the need to investigate the predicting factors of NSSI in this group. The present study aimed to develop a prediction model of NSSI among psychiatric patients in Korea. MATERIALS AND
methodsDecision tree analysis was conducted on a sample of 224 psychiatric patients. Emotion regulation strategies (rumination, cognitive reappraisal, and expressive suppression), impulsivity, problematic alcohol use, working memory, depressive mood, and gender were included in the model as predictors of NSSI.
resultsResults indicated that rumination, problematic alcohol use, and working memory predicted lifetime NSSI engagement among psychiatric patients. The best predictor of lifetime NSSI engagement was rumination. Specifically, when the level of rumination was high, the level of working memory was lower, and the risk of NSSI was higher. In the case of low levels of rumination, the higher the level of problematic alcohol use, the higher the risk of NSSI. The highest prevalence of lifetime NSSI engagement was found in a subgroup of patients with high levels of rumination and low levels of working memory.
conclusionThe major contribution of this study is finding a combination of factors to predict the high-risk group of NSSI among psychiatric patients in Korea. This study provides evidence on the effect of rumination, working memory, and problematic alcohol use on NSSI. It is suggested that clinicians and researchers should pay more attention to emotion regulation and related vulnerabilities in preventing and treating NSSI.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.