RECAP: Resistance Capture in Text-based Mental Health Counseling with Large Language Models

2026-01-21

AI Summary

This study addresses key challenges in identifying client resistance behaviors in text-based psychological counseling, including coarse-grained categorization, neglect of intervention dynamics, and limited interpretability. To overcome these limitations, the authors propose PsyFIRE, a theory-driven framework that introduces a fine-grained coding scheme encompassing 13 distinct resistance and collaboration behaviors. They annotate 23,930 real-world Chinese counseling utterances with contextual justifications and develop a two-stage RECAP model for joint resistance detection and explanation generation. The approach achieves an F1 score of 91.25% on binary collaboration–resistance classification and a macro F1 of 66.58% on fine-grained categorization, substantially outperforming prompt-engineering baselines by over 20 percentage points. Clinical validation by 62 practicing counselors confirms the method’s practical utility and interpretability in real-world therapeutic settings.

Technology Category

• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Natural Language Processing: Discourse, Pragmatics & Argument Mining
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search
• Responsible Web: Measurement, analysis, and circumvention of Web censorship

Abstract

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.

Problem

• client resistance
• text-based counseling
• mental health
• NLP
• therapeutic interaction

Innovation

• resistance detection
• fine-grained annotation
• interpretable NLP
• mental health counseling
• LLM-based framework