We present an initial study of robot-mediated multimodal mental-health screening for Chinese adolescents aged 12–15. MAPS (Multimodal Adolescent Psychological Screening) combines facial, acoustic, textual, and remotely estimated physiological signals collected through a standardized protocol of questionnaires, emotionally evocative tasks, and question answering. From 3,783 participants, 968 records were retained after consent, completeness, and quality-control filtering. We introduce GAME (Generalized Model with Attention and Multimodal EmbraceNet), a fusion model combining eight unimodal representations with relation-graph and attention features. Separate models are trained for 12 questionnaire- and expert-derived screening targets. Stratified 10-fold cross-validation yields 73.04–92.77% accuracy and 71.32–91.06% weighted F1. Feature ablations and descriptive contribution analyses suggest that representation use varies across targets. This initial study connects a portable interaction protocol with task-specific multimodal modeling in a Chinese school setting and provides a basis for further research on screening-label prediction. Future work will expand data collection, refine the fusion and training procedures, and evaluate generalization and screening utility through external and clinically supervised validation.