The psychosomatic impact of Yoga in medical education: a systematic review and meta-analysis
Bibliographic record
Abstract
Non-clinical approaches such as meditation, yoga, and mindfulness are popular traditional therapeutical interventions adopted by many educational institutions to improve the physical and mental well-being of learners. This study aimed to evaluate the effectiveness of yoga intervention in improving cardiopulmonary parameters such as blood pressure, heart rate, pulmonary function tests and psychosomatic symptoms such as depression, anxiety and stress in medical and dental students. Using the PRISMA protocol, a search from databases such as PubMed, Scopus, and Embase resulted in 304 relevant articles. After screening the title and abstracts, 47 papers were analyzed thoroughly and included in the qualitative analysis. 18 articles with homogenous statistical data on physiology and psychological parameters were included for meta-analysis. In comparison to the control group, the study showed a significant reduction of systolic blood pressure (SBP: 6.82 mmHg, z = -3.06, p = 0.002), diastolic blood pressure (DBP: 2.92 mmHg, z = -2.22, p = 0.03), and heart rate (HR: 2.55 beats/min, z = -2.77, p = 0.006). Additionally, data from 4 studies yielded a significant overall effect of a stress reduction of 0.77 on standardized assessments due to the yoga intervention (z = 5.29, p < 0.0001). Lastly, the results also showed a significant (z = -2.52, p = 0.01) reduction of 1.2 in standardized anxiety tests in intervention group compared to the control. The findings offer promising prospects for medical educators globally, encouraging them to consider reformation and policymaking in medical curricula to enhance academic success and improve the overall quality of life for medical students worldwide.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.035 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".