Computational Methods for Characterizing Research Outputs, Collaborative Networks and Thematic Concentration: a Case Study in Primary Care Research Evaluation

Abstract

AbstractObjectiveResearch impact is difficult to measure, evaluate and report. This study aims to demonstrate how computational scientometric methods, including bibliometric, network analytic, and thematic summary measures can efficiently characterize complex scientific disciplines, such as primary care research.MethodsWe used a retrospective cohort design. The study included N=17 international academic primary care research departments. A scientometric database was curated using a bottom-up methodology, which included peer-reviewed research articles/reviews, and associated meta-data, published between 01/01/2017 and 31/12/2022. Publication-level bibliometric information was queried from the Scopus application programming interface (API). The Altmetrics API was used to extract publication-level indicators of social engagement. Network analytic visualizations and statistics characterized research collaboration. Topic models and keyword mining characterized the main thematic areas of primary care research. At an author-level, we investigated correlations between bibliometric, altmetric, network analytic and topical summary measures.ResultsOur analysis included N=591 primary care researchers (from 17 institutions) who produced 13,047 unique peer-reviewed articles over the study timeframe. These 13,047 research articles were published in 2,237 unique journal titles; cited 231,121 times; and received broad social uptake (605,349 Twitter tweets, 36,982 mainstream media mentions, 884 Wikipedia references, and 1,127 policy document citations). The 591 researchers collaborated with 35,585 unique co-authors resulting in 20,808,886 pair-wise collaborations. The median number of authors per publication was 7 (IQR: 4-10; min=1; max=3,391). Frequently occurring keywords/n-grams and latent topical vectors, highlighted the diversity of primary care research. Clinical research themes included: physical/mental health conditions, disease prevention and screening, issues in primary/obstetric/emergency/palliative-care, and public health. Methodological research themes included: research synthesis/appraisal, statistical/epidemiological inference, study design, qualitative research, mixed methods, health economics, medical education, and quality improvement. Many themes were stable over the study timeframe. COVID-19 emerged as an important research theme from 2020 through 2022. Topic vectors encoding clinical medicine were positively correlated with bibliometric, altmetric and network centrality measures, whereas, vectors encoding qualitative methods, medical education, and public health were negatively correlated with these same metrics.ConclusionsMulti-metric, computational scientometric methods offer an efficient, transparent, and reproducible means for characterizing the research output of complex scientific disciplines, such as primary care research.