Mental Health data with HiTOP model

This dataset was collected as part of a large-scale mental health screening initiative conducted between 2019 and 2024 among postsecondary students in China. The sample includes 34,689 university and vocational college students , who completed the Symptom Checklist-90-Revised (SCL-90-R). The dataset includes demographic information (e.g., gender, age, education type, intake year) and symptom scores across nine SCL-90-R domains: depression, anxiety, somatization, obsessive-compulsive symptoms, phobic anxiety, interpersonal sensitivity, hostility, paranoid ideation, and psychoticism.The data were used to identify latent internalizing mental health profiles using Gaussian Mixture Modeling (GMM) and to build a Random Forest classifier for predicting profile membership. The dataset supports research on transdiagnostic symptom patterns, person-centered profiling, and machine learning applications in non-clinical educational contexts.All personally identifying information was removed. The dataset is anonymized and suitable for secondary analysis. Documentation and replication code (Python scripts) are provided to facilitate reproducibility.