Assessing the Status Quo of Research on Translational Psychological Treatment

Psychotherapy researchers have emphasized the importance of a paradigm shift towards translational psychological treatment. However, the publication landscape on this topic is uncharted. This makes it difficult to assess the state of translational psychotherapy research. Hence, we developed a workflow that leverages natural language processing and machine learning to find relevant studies. Based on this, we bibliometrically analyzed 7,146 publications to provide insights for the integration and development of the research field. Specifically, citation and network analyses were performed to examine the strengths and weaknesses of the research field. Five main findings emerged: 1. Machine learning proved valuable in finding eligible publications and generating an open dataset. 2. Regarding basic psychological subfields, translation comes primarily from physiological psychology/neuroscience, with a focus on fear, posttraumatic stress, and anxiety. 3. Translational research is characterized by international collaborations. 4. It has an impact within and beyond academia. 5. The lack of standardized terminology might threaten scientific progress. To foster a paradigm shift towards translational psychological treatment, a consistent terminology would greatly facilitate its development and dissemination.

Bibliometric analyses of emerging fields with inconsistent terminology and porous boundaries are challenging: When precise terms for search queries are not available, compiling a comprehensive dataset requires screening a large number of database records to prevent false positives. In this study, we leverage Machine Learning (ML) to identify and include publications that are relevant to the field but differ in their terminology. ML is employed to semi-automate the necessary screening process of the emerging research landscape of translational psychotherapy as a use case. Compared to a typical database search with terms of known terminology only, the dataset generated by the ML-augmented approach differs clearly in various bibliometrically relevant aspects, such as top authors, journals, countries and impact. Our study emphasizes the importance of consistent terminology of research fields and, in its absence, the merits and benefits of ML.

Analyzing emerging fields using traditional bibliometric techniques can be difficult due to inconsistent terminology and vague or porous boundaries. The absence of precise terms for database search queries can hinder efforts to fully cover a given construct of interest and may result in biased or distorted representations. The common solution of widening the scope of the search can lead to the inclusion of a large number of false positives, making it difficult to distinguish eligible data from noise. As a consequence, the drastically increased amount of records makes screening an absolute requirement for building a complete and accurate dataset.