Assessing the Status Quo of Research on Translational Psychological Treatment

To uncover barriers to translating basic research into psychological treatments, we systematically identified and bibliometrically analyzed 683 translational publications. Network analysis techniques were used to examine the strengths and weaknesses of the field. Six main findings emerged: 1. Machine learning proved valuable in generating an open dataset of translational publications. 2. Translation comes primarily from physiological psychology/neuroscience and experimental psychology, with a focus on fear 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. 6. There is thematic fragmentation in the field. We highlight the potential of translational research to improve psychological treatments, to inform each other, and to break down barriers in psychological science in general. 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.