Recognizing and navigating client resistance is critical for effective mental health counseling, yet detecting such behaviors is particularly challenging in text-based interactions. Existing NLP approaches oversimplify resistance categories, ignore the sequential dynamics of therapeutic interventions, and offer limited interpretability. To address these limitations, we propose PsyFIRE, a theoretically grounded framework capturing 13 fine-grained resistance behaviors alongside collaborative interactions. Based on PsyFIRE, we construct the ClientResistance corpus with 23,930 annotated utterances from real-world Chinese text-based counseling, each supported by context-specific rationales. Leveraging this dataset, we develop RECAP, a two-stage framework that detects resistance and fine-grained resistance types with explanations. RECAP achieves 91.25% F1 for distinguishing collaboration and resistance and 66.58% macro-F1 for fine-grained resistance categories classification, outperforming leading prompt-based LLM baselines by over 20 points. Applied to a separate counseling dataset and a pilot study with 62 counselors, RECAP reveals the prevalence of resistance, its negative impact on therapeutic relationships and demonstrates its potential to improve counselors' understanding and intervention strategies.

翻译：识别并应对来访者阻抗是心理健康咨询有效开展的关键，然而在文本交互中检测此类行为尤为困难。现有自然语言处理方法过度简化阻抗类别，忽视治疗干预的序列动态性，且可解释性有限。为突破这些局限，我们提出PsyFIRE理论框架，该框架能捕捉13种细粒度阻抗行为及协作性互动。基于PsyFIRE，我们构建了包含23,930条真实中文文本心理咨询标注语段的ClientResistance语料库，每条语段均附有情境化标注依据。利用该数据集，我们开发了RECAP双阶段框架，该框架可检测阻抗行为、细粒度阻抗类型并提供解释。RECAP在区分协作与阻抗时达到91.25%的F1值，在细粒度阻抗分类中取得66.58%的宏观F1值，较领先的基于提示的大语言模型基线提升超过20个百分点。在独立咨询数据集及62名咨询师的试点研究中，RECAP揭示了阻抗的普遍性及其对治疗关系的负面影响，并证明了其提升咨询师理解与干预策略的潜力。