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Record W4388491106 · doi:10.1177/03057356231203697

Music and young people with intellectual disability: A scoping review

2023· review· en· W4388491106 on OpenAlexaff
Jean‐Philippe Després, Francine Julien‐Gauthier, Flavie Bédard-Bruyère, Marie‐Claude Mathieu

Bibliographic record

VenuePsychology of Music · 2023
Typereview
Languageen
FieldPsychology
TopicMusic Therapy and Health
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsPsychological interventionPsychologyMusic therapyCreativityThe artsIntellectual disabilityMusic educationApplied psychologyPedagogySocial psychologyPsychotherapistVisual arts

Abstract

fetched live from OpenAlex

Despite recent reviews of the effects of arts therapies and music therapy on people with intellectual disabilities (IDs), significant knowledge gaps remain in this field, notably concerning informal music activities and the role that young participants play in music interventions. A scoping review was conducted in January 2021 to explore music interventions implemented in youth with ID and their effects. In total, 74 studies were retained, including 12 reviews and 62 empirical studies. We apply a bibliometric analysis to identify the evolution of publications and research trends in the field. We then attempt to answer the question: “What is the state of knowledge on music education for youth with ID?”. To do so, we describe the music interventions examined in the research to date and the main measured effects. Overall, the findings show that music interventions in youth with ID facilitate overall development in terms of a range of functional skills, that technology-assisted music training has excellent educational promise, and that learner voice merits greater attention in the music research. Nevertheless, studies have largely neglected to consider self-determination and creativity, qualities that are likely to foster youth engagement, and in the longer term, promote social participation.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.776
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.219
GPT teacher head0.468
Teacher spread0.250 · 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 designOther design
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

Citations8
Published2023
Admission routes1
Has abstractyes

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