Mapping the Scholarly Landscape of Self-Compassion and Mental Health (2010–2025): A Scopus-Based Bibliometric Analysis
Bibliographic record
Abstract
Objectives: We aimed to map the global research landscape on self-compassion and mental health by analysing publications retrieved from the Scopus database with a focus on publication trends, prolific authors, leading journals, geographic distribution, and the matic developments. Methods: We conducted a bibliometric analysis using relevant Scopus-indexed literature from inception through July 2025. We specifically focused on articles from 2010 to 2025. We employed the terms “self-compassion” OR “self com-passion” AND “mental health”. Results: We retrieved a total of 2,437 documents, re-vealing a significant increase in publication output over the past decade, peaking be-tween 2020 and 2024. The most prolific authors during this period included Paul Gilbert and Yasuhiro Kotera, with 41 publications each. The United States, the United Kingdom, Australia, Canada, and China emerged as the leading countries in terms of research output. Keyword analysis highlighted recurring themes around mindfulness, resilience, depression, and emotional regulation. Influential articles by MacBeth & Gumley (2012) and Gilbert & Procter (2006) demonstrated foundational impact with over 1,200 citations each. Conclusion: The field of self-compassion and mental health research is expanding rapidly and is characterised by strong theoretical foundations, growing global interest, and interdisciplinary relevance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.263 | 0.314 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".