Yoga Therapy for Non-Communicable Diseases: A Bibliometric Analysis of Published Research Studies from 1995 to 2024
Bibliographic record
Abstract
Abstract Bibliometric analysis is a widely used technique for analyzing large quantities of academic literature and evaluating its impact in a particular academic field. This paper used bibliometric analysis to analyze the academic research on yoga therapy for non-communicable diseases from 1995 to 2024. This study used SCOPUS to find related publications on yoga therapy for non-communicable diseases. “Yoga Therapy”, “Therapeutic Yoga”, “Pranayama”, “Yoga”, “NCDs”, “Non-Communicable Diseases”, and keywords related to various Non-Communicable Diseases were used for gathering the relevant articles. 2313 publications in total were selected for this research. In this study, four different bibliometric parameters, performance analysis, trend analysis, citation analysis, and network analysis, were used to evaluate the performance of these articles. According to this analysis, the three countries with the highest number of publications and citations regarding Yoga Therapy for Non-Communicable Diseases are the USA, India, and Canada. The three most significant researchers in this field are Nagendra, H.R., Cohen L. and Nagarathna, R. 'Yoga,' 'cancer,' 'breast cancer,' and ‘quality of life,' and 'exercise are the three most frequently used keywords. A further finding of the study indicates that the popular topics for Yoga Therapy for Non-Communicable Diseases are mind-body therapy, COVID-19, and Psycho-oncology. This research provides insight into the origins, current status, and future direction of Yoga Therapy for Non-Communicable Diseases research.
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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 | 0.008 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.130 | 0.191 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".