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Record W3011225929 · doi:10.1007/s10560-020-00657-5

A Scoping Review of Arts-Based Mindfulness Interventions for Children and Youth

2020· review· en· W3011225929 on OpenAlexafffund
Diana Coholic, Nicholas Schwabe, Kenneth Lander

Bibliographic record

VenueChild and Adolescent Social Work Journal · 2020
Typereview
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsLaurentian University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsMindfulnessPsychological interventionPsychologyThe artsPsychological resilienceApplied psychologyDramaPsychotherapistVisual artsArt

Abstract

fetched live from OpenAlex

Abstract Mindfulness-Based Interventions (MBIs) hold promise for building resilience in children/youth. We were interested in understanding why some MBIs incorporate arts-based methods, and what key findings were identified from the study of these MBIs. We used a scoping review to address our research questions. Scoping reviews can help us better understand how different types of evidence can inform practice, policy, and research. Steps include identifying research questions and relevant studies, selecting studies for analysis, charting data, and summarizing results. We identified 27 research articles for analysis. MBIs included the use of drawing, painting, sculpting, drama, music, poetry, and karate. Rationales included both the characteristics of children/youth, and the benefits of the methods. Arts-based MBIs may be more relevant and engaging especially for youth with serious challenges. Specific focus should be paid to better understanding the development and benefits of these MBIs.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.013
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.033
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.005
Bibliometrics0.0130.011
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.113
GPT teacher head0.427
Teacher spread0.314 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations33
Published2020
Admission routes2
Has abstractyes

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