The Language of Attachment: Modeling Attachment Dynamics in Psychotherapy
This work addresses the resource-intensive and complex manual assessment of attachment style in psychotherapy, potentially benefiting mental healthcare through more personalized and targeted approaches, though it is incremental as it applies existing NLP methods to a new domain.
The authors tackled the problem of manually assessing patient attachment style in psychotherapy by proposing the first exploratory analysis using NLP classification models on therapy transcripts, aiming to enable scalable adoption of attachment-informed treatment and research.
The delivery of mental healthcare through psychotherapy stands to benefit immensely from developments within Natural Language Processing (NLP), in particular through the automatic identification of patient specific qualities, such as attachment style. Currently, the assessment of attachment style is performed manually using the Patient Attachment Coding System (PACS; Talia et al., 2017), which is complex, resource-consuming and requires extensive training. To enable wide and scalable adoption of attachment informed treatment and research, we propose the first exploratory analysis into automatically assessing patient attachment style from psychotherapy transcripts using NLP classification models. We further analyze the results and discuss the implications of using automated tools for this purpose -- e.g., confusing `preoccupied' patients with `avoidant' likely has a more negative impact on therapy outcomes with respect to other mislabeling. Our work opens an avenue of research enabling more personalized psychotherapy and more targeted research into the mechanisms of psychotherapy through advancements in NLP.