Visualising the scientific landscape of global research in self-compassion: A bibliometric analysis

Chinun Boonroungrut, Kanyarat Muangkaew, Kamol Phoyen, Thitima Vechpong, Wananya Punyakitphokin, Gunchanon Khawda, Gallayaporn Nantachai, Nuttawut Eiamnate, Puangsoy Worakul, Chilungamo M’manga, Wulan Patria Saroinsong, | |

Abstract


Research in self-compassion has rapidly increased. Providing a comprehension of the scientific knowledge landscape will be helpful for researchers to identify the gaps and develop future research ideas in this field without subjectivity. Thus, overarching structures in self-compassion Scopus-indexed research were sought to be analysed here. Bibliometric techniques were used to extract documents indexed in Scopus under the domain of self-compassion (N = 1,764 articles) using bibliophily, a web interface of the Bibliometrix 3.0 and VOSviewer to conduct analysis and visualisation. The number of published articles shows an upward trend; the United States occupies the leading position in publication volumes and citations. Undoubtedly, K. D. Neff and her sustained cited model were reported as a prolific author and influential article in self-compassion literature. The top affiliation was the University of Texas at Austin, which was the leading university in both production and citation. Mindfulness was a key journal that was considered in publication volume and received citations; however, authors trended to publish from diverse selected sources. The top 50 most frequently used terms were related to individual differences and psychological status. Finally, the thematic mapping provided a comprehensive illustration showing significant themes and knowledge gaps in self-compassion research, which categorised individual differences as basic, measurement validation as niche and PTSD as emerging themes. The current findings presented scientific mappings and extensive tables containing information that usefully proposes future directions and the critical broader scope of research in self-compassion.

https://doi.org/10.34105/j.kmel.2024.16.009


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Laboratory for Knowledge Management & E-Learning, The University of Hong Kong