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Record W4396838158 · doi:10.4103/ijoy.ijoy_227_23

Effect of Yoga among Children and Adolescents Diagnosed with Psychiatric Disorders: A Scoping Review

2024· review· en· W4396838158 on OpenAlexaboutno aff
Bichitra Nanda Patra, Kanika Khandelwal, Rajesh Sagar, Gautam Sharma

Bibliographic record

VenueInternational Journal of Yoga · 2024
Typereview
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsDepression (economics)MedicineAnxietyPsychiatryMindfulnessClinical psychology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.924
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.016
GPT teacher head0.398
Teacher spread0.382 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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