The effects of Sahaja Yoga meditation on mental health: a systematic review
Bibliographic record
Abstract
Abstract Objectives To determine the efficacy of Sahaja Yoga (SY) meditation on mental health among clinical and healthy populations. Methods All publications on SY were eligible. Databases were searched up to November 2017, namely PubMed, MEDLINE (NLM), PsychINFO, and Scopus. An internet search (Google Scholar) was also conducted. The quality of the randomized controlled trails was assessed using the Cochrane Risk Assessment for Bias. The quality of cross-sectional studies, a non-randomized controlled trial and a cohort study was assessed with the Newcastle-Ottawa Quality Assessment Scale. Results We included a total of eleven studies; four randomized controlled trials, one non-randomized controlled trial, five cross-sectional studies, and one prospective cohort study. The studies included a total of 910 participants. Significant findings were reported in relation to the following outcomes: anxiety, depression, stress, subjective well-being, and psychological well-being. Two randomized studies were rated as high quality studies, two randomized studies as low quality studies. The quality of the non-randomized trial, the cross-sectional studies and the cohort study was high. Effect sizes could not be calculated in five studies due to unclear or incomplete reporting. Conclusions After reviewing the articles and taking the quality of the studies into account, it appears that SY may reduce depression and possibly anxiety. In addition, the practice of SY is also associated with increased subjective wellbeing and psychological well-beng. However, due to the limited number of publications, definite conclusions on the effects of SY cannot be made and more high quality randomized studies are needed to justify any firm conclusions on the beneficial effects of SY on mental health.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".