This study uniquely combines machine learning with structural equation modeling to investigate how self-criticism affects non-suicidal self-injury (NSSI) behaviors in adolescents. Using a Support Vector Machine (SVM) for binary classification (distinguishing NSSI presence/absence) and XGBRegressor for regression (predicting NSSI severity), with self-criticism, emotional regulation difficulties, and public self-consciousness as key predictors, the study accurately classified and forecasted NSSI behaviors. The K-fold cross-validation confirmed the robustness of these predictors. The SVM classifier achieved an accuracy of 85.3% (AUC = 0.89, 95% CI 0.87, 0.91), while the regression model explained 50.9% of variance ( R 2 = 0.509). Structural equation modeling revealed that self-criticism directly influences NSSI ( β = 0.32, p < .001) and indirectly affects it through both emotional regulation difficulties ( β = 0.18, 95% CI 0.14, 0.22) and public self-consciousness ( β = −0.11, 95% CI −0.15, −0.07), with a significant serial mediation pathway (self-criticism → emotional regulation difficulties → public self-consciousness → NSSI). These findings demonstrate that self-criticism, difficulty in emotional regulation, and public self-consciousness can predict NSSI risk/severity and differentiate between groups with and without NSSI behaviors in our sample. Additionally, adolescents' public self-consciousness can directly and negatively predict non-suicidal self-injurious behaviour, revealing its protective role in the pattern of NSSI behaviour and providing support for the assumptions in the benefit and hindrance models of NSSI.
Huo et al. (Wed,) studied this question.