Mapping the Mind–Body Connections: A Two-decade Bibliometric Exploration of Yoga and Psychoneuroimmunology
Bibliographic record
Abstract
Background: Over the past two decades, the intersection of yoga and psychoneuroimmunology (PNI) has received significant attention as a promising field for promoting mental and immune well-being. This bibliometric analysis explores global research trends, prolific authors, influential journals and collaboration patterns from 2003 to 2023. The study underscores the need to integrate traditional knowledge with modern science, positioning yoga as a validated tool in mainstream healthcare for disease prevention and management. Purpose: This study aims to explore global contributions, impact trends in yoga and PNI research, prolific authors, journals and spotlight leading countries, emerging opportunities, influential research hubs and collaboration gaps. Additionally, to conduct thematic analysis aimed at integrating yoga into mainstream healthcare as a scientifically validated tool for promoting mental and immune well-being within the context of PNI. Methods: Articles published between 2003 and 2023 were retrieved from Scopus and analysed using Biblioshiny version 4.0.0 (via R-Studio) and VOSviewer. Results: = 239) involve international collaboration. The Canadian Institutes of Health Research and National Institute of Health dominate the funding landscape, whereas Indian funding agencies (Ayurveda, Yoga and Naturopathy, Unani, Siddha and Homeopathy, Indian Council of Medical Research and Department of Science and Technology) play a relatively minor role in the global context. Conclusion: This analysis provides an updated perspective for understanding the field's hotspot, which facilitates future research to uncover the mechanisms underlying the effects of yoga on the psycho-neuro-immuno-endocrine systems in disease prevention and management. Integration of traditional knowledge with modern scientific practices, with a focus on high-impact publishing, will help position India as a leader in this research domain.
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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 | 0.011 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.149 | 0.206 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".