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

TL;DR

This paper introduces RECAP, a novel two-stage NLP framework that detects and classifies resistance behaviors in text-based mental health counseling, enhancing interpretability and outperforming existing models.

Contribution

It presents PsyFIRE, a theoretically grounded resistance behavior framework, and constructs the ClientResistance corpus, enabling improved detection and understanding of resistance in counseling texts.

Findings

RECAP achieves 91.25% F1 in resistance detection.

It outperforms prompt-based LLM baselines by over 20 points.

RECAP reveals resistance's negative impact on therapy and aids counselor strategies.

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.