Worker safety attitudes are key determinants of whether protective practices are applied or bypassed on construction sites. Yet measuring them at scale has remained out of reach. Safety attitudes are multidimensional, vary across topics, and surface most candidly in workers' own conversations. This study created and validated the Construction Safety Attitude Framework (CSAF), which integrates two components: a theory-grounded structure that characterizes safety attitudes along eight dimensions, and an operational codebook for measuring them in worker naturalistic discourse. Applying CSAF to 250 posts and comments from the r/Construction community on Reddit, trained coders reached strong agreement (Krippendorff's α = 0.85). Pairwise lift and conditional probability confirmed that the eight dimensions are related yet distinct. To apply the framework across large volumes of discourse, CSAF was operationalized through a large language model (LLM) classifier. On 450 r/Construction contributions, the classifier reproduced expert human coding (Cohen's \k{appa} = 0.90, precision = 0.98, recall = 0.98), and on 400 contributions from r/Roofing it retained that accuracy after transfer to a different trade community (\k{appa} = 0.89, precision = 0.98, recall = 0.97). A proof-of-value case study then applied the validated classifier to 10,346 contributions from r/Roofing, demonstrating that CSAF can distinguish multidimensional attitudes by safety topic, track how they shift over time, and trace the reasoning behind unfavorable ones. The study therefore provides a theoretically grounded, empirically vetted instrument for examining safety attitudes, offering a basis for targeted interventions that address the attitudes underlying unsafe practices.

翻译：工人安全态度是决定施工现场防护措施是否被采用或规避的关键因素，然而大规模测量安全态度始终是未竟之业。安全态度具有多维性，随主题而异，且最直接地体现在工人的真实对话中。本研究构建并验证了建筑安全态度框架（CSAF），该框架整合两大要素：其一是以理论为基础、从八个维度刻画安全态度的结构体系；其二是用于测量工人自然话语中安全态度的操作化编码手册。将CSAF应用于Reddit平台r/Construction社区的250条帖子及评论后，经过训练的编码者达成了高度一致性（Krippendorff‘s α=0.85）。成对提升度与条件概率分析证实八个维度之间既相互关联又彼此独立。为实现对大容量话语数据的框架应用，本研究通过大语言模型（LLM）分类器对CSAF进行操作性转换。在450条r/Construction社区内容上，该分类器重现了专家人工编码结果（Cohen’s κ=0.90，精确率=0.98，召回率=0.98）；在400条r/Roofing社区内容上，该分类器在迁移至不同工种社区后仍保持原有精度（κ=0.89，精确率=0.98，召回率=0.97）。随后开展的实证价值案例研究将该验证后的分类器应用于10,346条r/Roofing社区内容，证明CSAF能够按安全主题区分多维态度、追踪其随时间演变的趋势，并追溯负面态度背后的推理逻辑。因此，本研究提供了兼具理论根基与实证检验的安全态度测量工具，为针对不安全行为背后的态度制定精准干预措施奠定了坚实基础。