Word embeddings represent a transformative technology for analyzing text data in social work research, offering sophisticated tools for understanding case notes, policy documents, research literature, and other text-based materials. This methodological paper introduces word embeddings to social work researchers, explaining how these mathematical representations capture meaning and relationships in text data more effectively than traditional keyword-based approaches. We discuss fundamental concepts, technical foundations, and practical applications, including semantic search, clustering, and retrieval augmented generation. The paper demonstrates how embeddings can enhance research workflows through concrete examples from social work practice, such as analyzing case notes for housing instability patterns and comparing social work licensing examinations across languages. While highlighting the potential of embeddings for advancing social work research, we acknowledge limitations including information loss, training data constraints, and potential biases. We conclude that successfully implementing embedding technologies in social work requires developing domain-specific models, creating accessible tools, and establishing best practices aligned with social work's ethical principles. This integration can enhance our ability to analyze complex patterns in text data while supporting more effective services and interventions.

翻译：词嵌入代表了社会工作研究中分析文本数据的一项变革性技术，为理解案例记录、政策文件、研究文献及其他文本材料提供了先进工具。这篇方法论论文向社会工作研究者介绍了词嵌入技术，阐释了这些数学表示如何比传统基于关键词的方法更有效地捕捉文本数据中的意义与关联。我们讨论了基本概念、技术基础及实际应用，包括语义搜索、聚类和检索增强生成。本文通过社会工作实践中的具体案例（例如分析住房不稳定模式的案例记录、跨语言比较社会工作执业资格考试）展示了嵌入技术如何优化研究流程。在强调嵌入技术对推进社会工作研究潜力的同时，我们也认识到其局限性，包括信息损失、训练数据约束及潜在偏见。我们得出结论：在社会工作中成功实施嵌入技术需要开发领域专用模型、创建易用工具，并建立符合社会工作伦理准则的最佳实践。这种融合将提升我们分析文本数据中复杂模式的能力，同时支持更有效的服务与干预措施。