Effect of Yoga among Children and Adolescents Diagnosed with Psychiatric Disorders: A Scoping Review
Bibliographic record
Abstract
Background: Depression has been expected to be the second-leading cause of disability, followed by autism, attention and hyperactivity disorder, and learning disorder. Yoga therapy has found to be beneficial in managing psychiatric disorders. Aim: The present study undertakes a scoping review of research on yoga therapy in psychiatric disorders among children and adolescents. Methods: Online database was used to identify papers published 2004-2023, from which we selected 11 publications from the United States, Canada, Iran, India, and Australia that used yoga therapy as a primary outcome variable among participants aged 3 years or older. Results: The papers reviewed were randomized controlled trials. All studies examined yoga therapy, but one study used mindfulness-based therapy and used few techniques of yoga therapy. The studies examined the effect of yoga therapy on early childhood and adolescence on various psychiatric symptoms such as stress, inattention, hyperactivity, anxiety, depression, and many more. Conclusion: While the quality of studies is generally high, research on yoga therapy among children and adolescents with psychiatric disorders would benefit from careful selection of methods and reference standards, suitability for yoga therapy, and prospective cohort studies linking early childhood exposures with yoga therapy outcomes throughout childhood and adolescence.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".