Niu Shuhua, Li Weihua
Nantong University, Nantong, China.
Taizhou Vocation College of Science Technology, School of Accounting Finance, Taizhou, Zhejiang, China.
Front Public Health. 2025 Jul 10;13:1597381. doi: 10.3389/fpubh.2025.1597381. eCollection 2025.
The relationship between social security systems and public health outcomes has garnered significant attention due to its impact on improving health welfare and promoting economic stability. Social security systems, including pension schemes, healthcare benefits, and unemployment support, are essential for shaping societal wellbeing by influencing healthcare access, labor market participation, and overall economic resilience. However, traditional methods for evaluating these systems often fail to capture the complex dynamics of policy interventions over time.
To address this, we propose an advanced economic policy modeling framework that integrates dynamic optimization techniques with machine translation applications. Machine translation applications refer to the use of automated translation tools to facilitate communication in multilingual contexts, ensuring equal access to healthcare and social services.
These applications contribute to the evaluation of social security systems by improving the accessibility and efficiency of service delivery, particularly in linguistically diverse populations.
By incorporating both economic policy modeling and machine translation technology, our framework offers a comprehensive analysis of social security interventions, demonstrating how well-optimized policies can enhance public health outcomes while ensuring fiscal sustainability.
社会保障体系与公共卫生结果之间的关系因其对改善健康福利和促进经济稳定的影响而备受关注。社会保障体系,包括养老金计划、医疗福利和失业支持,对于通过影响医疗保健获取、劳动力市场参与和整体经济复原力来塑造社会福祉至关重要。然而,评估这些体系的传统方法往往无法捕捉政策干预随时间推移的复杂动态。
为解决这一问题,我们提出了一个先进的经济政策建模框架,该框架将动态优化技术与机器翻译应用相结合。机器翻译应用是指使用自动翻译工具来促进多语言环境下的交流,确保平等获取医疗保健和社会服务。
这些应用通过提高服务提供的可及性和效率,特别是在语言多样化人群中,有助于对社会保障体系进行评估。
通过将经济政策建模和机器翻译技术相结合,我们的框架对社会保障干预措施进行了全面分析,展示了优化良好的政策如何在确保财政可持续性的同时提高公共卫生结果。