Supplementary Materials for: Research on Translational Psychological Treatment: A Comprehensive Bibliometric Analysis

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.

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.

The field of translational psychological treatment has significant impact, but its full potential is yet to be realized. To better understand potential barriers, we conduct a comprehensive literature review. Based on Ehring’s et al. (2022) proposed framework for translational research in clinical psychology (Steps 1 - 6) and we categorize the previously mapped out dataset to identify the exact translational stage of every publication. Translational research is characterized by fluid transitions or overlaps between its various stages, making it challenging to neatly distinguish between them. In such cases, we begin by determining the direction of translation: when the knowledge transfer is from basic science to implementation then assign the highest step, and when the movement is towards basic principles assign the lowest one. Additionally, we categorize the dataset into three distinctive stages: “pre-clinical trials, clinical trials and implementation” and subsequently, we compare it to the categorization based on Ehring’s model. This high resolution categorization allows us to specify the object of translation, the targeted disorder, the proposed intervention and the direction of translation, shedding light on possible barriers and enabling their resolution (facilitating their elimination). Additionally, we analyze temporal delay in translation to identify both hiccups and untapped potential for further advancement in translational psychological treatment research.

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.

Thematic fragmentation within emerging fields can be a sign of positive diversity and dynamic heterogeneity, or a threat to scientific progress and collaboration. Especially a low self-awareness of the field may be crucial, as it could relate to thematic fragments with low exchange of knowledge. We address this issue by analyzing the effects of two measures of fragmentation, self-referentiality and modularity in the citation network, on productivity and collaboration in the research field. Using the emerging field of translational psychotherapy as an example, we apply predictive modeling and examine a time span of 40 years (1982-2021). Results show that normalized self-referentiality (the number of direct citations divided by the number of possible citations within the research field) is positively related to the publication volume over time. In conclusion, the analysis of direct citations between thematic fragments in the research field seems promising for distinguishing between malignant disintegration and benign forms of fragmentation.

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.

This dataset contains records of 7,146 publications on translational psychological treatment. The metadata were provided by the OpenAlex database.