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Record W4386020247 · doi:10.1080/17533015.2023.2243288

Music interventions with children, adolescents and emerging adults in mental health settings: a scoping review

2023· review· en· W4386020247 on OpenAlexaff
Melissa Romano, Kim Archambault, Patricia Garel, Nathalie Gosselin

Bibliographic record

VenueArts & Health · 2023
Typereview
Languageen
FieldPsychology
TopicMusic Therapy and Health
Canadian institutionsCentre Hospitalier de l’Université de MontréalCentre Hospitalier Universitaire Sainte-JustineUniversité de MontréalCentre for Interdisciplinary Research in RehabilitationInternational Laboratory for Brain, Music and Sound ResearchCentre for Research on Brain Language and Music
Fundersnot available
KeywordsCINAHLPsychological interventionPsycINFOPsychosocialMental healthMusic therapyMEDLINEInclusion (mineral)Intervention (counseling)PsychologyMedicineClinical psychologyApplied psychologyPsychotherapistPsychiatrySocial psychology

Abstract

fetched live from OpenAlex

BACKGROUND: Music is increasingly used with youths in health and psychosocial interventions. We conducted a scoping review with the aim to provide an overview of the current available evidence on music intervention for youth in mental health settings, to inform practice and further research. METHODS: Three databases (PsycINFO, PubMed and CINAHL) were surveyed. Using the PRISMA review method, 23 studies met inclusion criteria. RESULTS: Young people aged from 4 to 25 years old with various mental health conditions participated in music interventions. Music therapy was the most investigated (71%). Improving social skills was the most frequent therapeutic aim addressed. Music interventions are mostly appreciated by the participants, but it is difficult to make conclusions about their effectiveness because of the heterogeneity of research designs and the limited current state of research. CONCLUSION: Music interventions appear to represent a promising complementary approach to usual psychiatric care, but further standardised research is necessary to continue investigating their therapeutic effects.

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)0.006
metaresearch head score (Gemma)0.023
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 score0.008
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.023
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0080.007
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.128
GPT teacher head0.475
Teacher spread0.347 · 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

Citations10
Published2023
Admission routes1
Has abstractyes

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