The Merits of Screening Automation for Bibliometric Analyses: The Case of Translational Psychotherapy

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.

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.