Half a Century of Research for Mind–Body Interventions: A Scientometric Analysis
Bibliographic record
Abstract
Abstract Objectives We conducted a scientometric analysis on mind–body interventions to assess themes and trends in recent decades, providing insights for prospective research directions. Our systematic search, completed on 1 November, 2023, encompassed the Web of Science Core Collection and focused on scientific publications related to contemplative practices: Mindfulness, Yoga, Qi-gong, Tai-chi, Vipassana, Zen, Loving-kindness, and Transcendental meditation. Method Integration of network analyses and bibliometrics using Bibliometrix and CiteSpace allowed us to identify evolving research themes. Our primary objective was to measure the evolution of research trends, while secondary aims involved uncovering influence networks tied to countries, publications, institutions, and authors. Co-citation reference networks specific to each mind–body practice were extracted. Results Our analysis incorporated 16,310 documents (389,632 references) spanning the years 1973 to 2023, forming a well-structured network with credible clustering. The overarching dataset highlighted the dominance of mindfulness practice in the realm of mind–body interventions. To further explore research patterns, we explored individual co-citation reference networks for each practice, revealing varying sizes for Mindfulness (n = 2278), Yoga (n = 1303), Qi-gong (n = 582), Tai-chi (n = 1000), Vipassana (n = 344), Zen (n = 454), Loving-kindness (n = 552), and Transcendental meditation (n = 834). Each practice exhibited distinct clusters, focusing on applications for diverse mental disorders and physical health issues, ranging from substance abuse to schizophrenia, and from back pain to dementia. Conclusions While research on mind–body interventions has been predominantly influenced by mindfulness in recent decades, each type of mind–body intervention contributes uniquely to both theoretical and clinical contexts. These insights have significant implications for funding agencies and research groups, guiding future directions in the field. Preregistration The preregistered protocol can be found online ( https://osf.io/qzb3c/ ).
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.060 | 0.236 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.125 | 0.172 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".