Self-Compassion in an Academic Setting: A Big Data-Driven Systematic Literature Review - Diani Akmalia Apsari; Adi Atmoko; Ninik Setyowati (Universitas PGRI Yogyakarta, 2026)
Psychological pressure can hinder students from performing optimally in academic settings. In response, self-compassion (SC) emerges as a crucial skill to help students manage stress and maintain mental health. While previous research has predominantly examined SC in clinical contexts, this study offers a novel perspective by investigating its application within an academic setting. The aim is to develop a theoretical model that explains the role of SC in addressing psychological academic challenges. A systematic literature review was conducted using the PRISMA model, supported by big data and visualized through VOS viewer. Using the Publish or Perish application, 505 articles were identified from Scopus- and Sinta-indexed journals (Elsevier, PubMed, Crossref, Google Scholar) published between 2019 and 2023. Nineteen articles were selected for in-depth analysis. The results reveal two academic categories: positive and negative. Self-compassion enhances variables in positive settings and moderates the effects of negative academic stressors. This research contributes to the design of psychological intervention strategies for students. Keywords: psychological academic challenges, big data, self-compassion, students