Introduction
Psychotherapy is a complex process that, despite diverse theories and techniques—from cognitive-behavioral and psychodynamic methods to humanistic approaches—shares common change principles that reinforce its effectiveness. These universal elements, known as common factors (CFs), include the therapeutic relationship, expectancy factors, corrective experiencing, insight, and self-efficacy Bailey and Ogles ( 2023 ) and account for around 30% of therapy outcomes Lambert ( 1992 ) , with the therapeutic relationship being a particularly strong predictor of positive change Nahum et al. ( 2019 ) . Intertwined with these CFs are microcounseling skills—discrete, teachable behaviors introduced by Ivey et al. ( 1968 ) , such as reflective listening and the strategic use of open-ended questions. These skills enable therapists to evoke change principles in practice. For example, fostering the therapeutic bond (an element of the CF therapeutic relationship) by using reflective listening (a microcounseling skill) to convey an empathic and validating attitude (an intervention concept) will likely improve client involvement and therapeutic effectiveness.

Monitoring therapists’ behaviors in relation to therapeutic processes can provide deeper insights into how these processes, in turn, contribute to improved treatment outcomes. Moreover, automating skill and CF identification in place of human-based coding or post-session questionnaires improves scalability, lowers costs, and enables the analysis of within-session micro-processes Falkenström and Larsson ( 2017 ) . This approach also offers targeted, session-by-session feedback, enabling clinicians to refine their techniques and adapt interventions to individual client needs.

One effective way to structure change principles systematically is by modeling CFs and microcounseling skills as a graph-based taxonomy. In this taxonomy, CF elements serve as higher-level categories, while microcounseling skills act as specific subcategories or methods used to elicit these factors. The hierarchical relationships within the graph illustrate how particular skills are applied in the context of broader factors. Moreover, this graph can be further enriched by incorporating node attributes, such as detailed descriptions and contextual examples, that clarify how each skill functions within its corresponding CF.

Building on this structured representation, we can leverage graph machine learning (ML) models to classify text by encoding these relationships. These models can learn embeddings for each node, effectively capturing both the structural and feature-based information embedded in the taxonomy. For example, when analyzing a therapeutic interaction, the model can identify relevant microcounseling skills (like reflective listening or validation) and link them to higher-level CFs (such as the therapeutic bond).
Based on this framework, we propose a classification method CFiCS 1 1 1 Code available on GitHub that employs graph ML to aggregate information from the interconnected network of c ommon f actors, i ntervention c oncepts, s kills, and examples. This approach allows us to inductively predict associations between previously unseen text and the corresponding CFs (e.g., therapeutic relationship), CF elements (e.g., therapeutic bond), intervention categories (e.g., collaboration and partnership), and skills (e.g., reflective listening), ultimately enhancing our ability to assess and improve therapeutic interactions. We demonstrate through experiments that integrating the graph outperforms baselines. The most accurate configuration combines ClinicalBERT embeddings with GraphSage.