Low-cost wrist-worn sensing for scalable, low-burden assessment of mental-health symptoms is a central topic in mobile health. In this paper, we propose MentalCare, a multi-class psychiatric disorder (PD) detection system that integrates ubiquitous multi-source sensors, including Pulse sensor, LMT70, Grove GSR, and Max30102. These sensors capture physiological signals such as photoplethysmography (PPG), skin temperature (SKT), galvanic skin response (GSR), and blood oxygen (OXY). To address physiological heterogeneity among individuals, we design a contrastive disentanglement with a positive transfer framework (CDPT), which consists of a stacked convolutional neural network module (SCNN), a cross-modal attention interaction module (CMAI), a subject-level contrastive disentanglement module (SCD), and an adaptive feature selector module (AFS). In particular, the SCD module employs supervised contrastive learning to promote the separation of heterogeneous features among different subjects in the latent subspace. Furthermore, the proposed AFS module selects features from the abundant source-domain heterogeneous representations that are most similar to the target domain, thereby facilitating feature alignment and enhancing detection performance. We collect a multi-class PD dataset comprising 74 participants diagnosed with depression, bipolar disorder, anxiety, or verified as healthy controls. The experiment result demonstrates that CDPT achieves 83.96% overall accuracy under the leave-one-subject-out cross-validation protocol, significantly outperforming existing state-of-the-art methods. At the system level, we further validate the effectiveness and rationality of the proposed system when deployed on edge devices.