Employee well-being is a critical concern in the contemporary workplace, as highlighted by the American Psychological Association's 2021 report, indicating that 71% of employees experience stress or tension. This stress contributes significantly to workplace attrition and absenteeism, with 61% of attrition and 16% of sick days attributed to poor mental health. A major challenge for employers is that employees often remain unaware of their mental health issues until they reach a crisis point, resulting in limited utilization of corporate well-being benefits. This research addresses this challenge by presenting a groundbreaking stress detection algorithm that provides real-time support preemptively. Leveraging automated chatbot technology, the algorithm objectively measures mental health levels by analyzing chat conversations, offering personalized treatment suggestions in real-time based on linguistic biomarkers. The study explores the feasibility of integrating these innovations into practical learning applications within real-world contexts and introduces a chatbot-style system integrated into the broader employee experience platform. This platform, encompassing various features, aims to enhance overall employee well-being, detect stress in real time, and proactively engage with individuals to improve support effectiveness, demonstrating a 22% increase when assistance is provided early. Overall, the study emphasizes the importance of fostering a supportive workplace environment for employees' mental health.

翻译：员工福祉是当代职场中的关键问题，正如美国心理学会2021年报告所强调，71%的员工经历压力或紧张。这种压力显著导致职场人员流失和缺勤，其中61%的人员流失和16%的病假归因于心理健康状况不佳。雇主面临的一大挑战在于，员工往往直到危机爆发时才意识到自己的心理健康问题，导致企业福利项目利用率有限。本研究通过提出一种开创性的压力检测算法来应对这一挑战，该算法能够预先提供实时支持。借助自动化聊天机器人技术，该算法通过分析聊天对话客观测量心理健康水平，并基于语言生物标记实时提供个性化治疗建议。研究探讨了将这些创新整合到实际学习应用中在现实场景中的可行性，并引入了一个集成至更广泛员工体验平台的聊天机器人风格系统。该平台涵盖多种功能，旨在提升员工整体福祉、实时检测压力并主动与个体互动以提高支持效果，研究表明，早期提供援助时效果提升了22%。总体而言，本研究强调了营造支持性工作环境对于员工心理健康的重要性。