Common factors and microcounseling skills are critical to the effectiveness of psychotherapy. Understanding and measuring these elements provides valuable insights into therapeutic processes and outcomes. However, automatic identification of these change principles from textual data remains challenging due to the nuanced and context-dependent nature of therapeutic dialogue. This paper introduces CFiCS, a hierarchical classification framework integrating graph machine learning with pretrained contextual embeddings. We represent common factors, intervention concepts, and microcounseling skills as a heterogeneous graph, where textual information from ClinicalBERT enriches each node. This structure captures both the hierarchical relationships (e.g., skill-level nodes linking to broad factors) and the semantic properties of therapeutic concepts. By leveraging graph neural networks, CFiCS learns inductive node embeddings that generalize to unseen text samples lacking explicit connections. Our results demonstrate that integrating ClinicalBERT node features and graph structure significantly improves classification performance, especially in fine-grained skill prediction. CFiCS achieves substantial gains in both micro and macro F1 scores across all tasks compared to baselines, including random forests, BERT-based multi-task models, and graph-based methods.

翻译：共同因素与微咨询技能对心理治疗的有效性至关重要。理解和测量这些要素能为治疗过程与结果提供有价值的洞见。然而，由于治疗对话具有微妙且依赖语境的特性，从文本数据中自动识别这些改变原则仍具挑战性。本文提出CFiCS，一种将图机器学习与预训练上下文嵌入相结合的层次化分类框架。我们将共同因素、干预概念和微咨询技能表示为异质图，其中来自ClinicalBERT的文本信息丰富了每个节点。该结构同时捕捉了层次关系（例如技能级节点与广泛因素的关联）和治疗概念的语义特性。通过利用图神经网络，CFiCS学习归纳式节点嵌入，可泛化至缺乏显式连接的未见文本样本。实验结果表明，整合ClinicalBERT节点特征与图结构能显著提升分类性能，尤其在细粒度技能预测方面。与随机森林、基于BERT的多任务模型及基于图的方法等基线相比，CFiCS在所有任务上的微观与宏观F1分数均取得显著提升。