Background: Large language models (LLMs) are increasingly applied in healthcare. However, concerns remain that their nursing care recommendations may reflect patients’ sociodemographic attributes rather than clinical needs. Objective: To investigate potential biases in nursing care plans generated by LLMs, we focused on whether outputs differ systematically based on patients’ sociodemographic characteristics and assessed the implications for equitable nursing care. Methods: We utilized a standardized clinical scenario with GPT to generate care plans for 96 sociodemographic identity combinations, drawing on 9,600 tests. We conducted statistical analyses (t-tests and ANOVA) to analyze how text length and the frequency of physiological and psychological nursing terms varied across sociodemographic factors. Additionally, we utilized Python for data processing and visualization to ensure methodological rigor throughout the study. Results: The analysis revealed significant sociodemographic biases in LLMs-generated nursing care plans. Female patients received shorter care plans (t = 4.864, P

arXiv:2507.05177v3 Announce Type: replace-cross Abstract: Empathetic interaction is a cornerstone of human-machine communication, due to the need for understanding speech enriched with paralinguistic cues and generating emotional and expressive responses. However, the most powerful empathetic LSLMs are increasingly closed off, leaving the crucial details about the architecture, data and development opaque to researchers. Given the critical need for transparent research into the LSLMs and empathetic behavior, we present OpenS2S, a fully open-source, transparent and end-to-end LSLM designed to enable empathetic speech interactions. Based on our empathetic speech-to-text model BLSP-Emo, OpenS2S further employs a streaming interleaved decoding architecture to achieve low-latency speech generation. To facilitate end-to-end training, OpenS2S incorporates an automated data construction pipeline that synthesizes diverse, high-quality empathetic speech dialogues at low cost. By leveraging large language models to generate empathetic content and controllable text-to-speech systems to introduce speaker and emotional variation, we construct a scalable training corpus with rich paralinguistic diversity and minimal human supervision. We release the fully open-source OpenS2S model, including the dataset, model weights, pre-training and fine-tuning codes, to empower the broader research community and accelerate innovation in empathetic speech systems. The project webpage can be accessed at https://casia-lm.github.io/OpenS2S