Accurately modeling physicians’ emotional states from self-reflection texts remains challenging due to the lowresource, domain-specific nature of medical corpora. The proposed workflow performs Retrieval-Augmented Generation (RAG) and multi-teacher pseudo-labeling to generate high-quality augmented data. This workflow enables effective crossdomain adaptation from general text corpora to professional medical texts. Evaluations on the ROCLING 2025 test set demonstrate substantial improvements over the best-performing baseline in Valence–Arousal prediction accuracy and model stability. Importantly, the workflow is domain-agnostic and provides a generalizable methodology for systematically transferring models to new, low-resource domains, making it applicable beyond medical text analysis.