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Record W4401762109 · doi:10.1111/1460-6984.13104

The use of music and music‐related elements in speech‐language therapy interventions for adults with neurogenic communication impairments: A scoping review

2024· review· en· W4401762109 on OpenAlexaff
Antonette Ong, Ashwini Namasivayam‐MacDonald, Sunny Kim, Sophia Werden Abrams

Bibliographic record

VenueInternational Journal of Language & Communication Disorders · 2024
Typereview
Languageen
FieldPsychology
TopicMusic Therapy and Health
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMusic therapyPsychological interventionPsychologySingingSpeech-Language PathologyMEDLINEIntervention (counseling)RehabilitationMedicinePsychotherapistPhysical therapyPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: A growing body of research indicates that music-based interventions show promising results for adults with a wide range of speech, language and communication disorders. AIMS: The purpose of this scoping review is to summarize the evidence on how speech-language therapists (SLTs) use music and music-related elements in therapeutic interventions for adults with acquired neurogenic communication impairments. METHODS: This scoping review was completed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines. A systematic search of three databases (Allied and Complementary Medicine Database, Cumulative Index to Nursing and Allied Health Literature and OVID Medline) was conducted and articles were included if they (1) incorporated adult human participants; (2) received an assessment or intervention facilitated by an SLT; (3) incorporated techniques and interventions which included music-related elements (e.g., rhythm, melody, harmony and dynamics); (4) were written in the English language; and (5) were peer-reviewed full-text articles. Data were extracted using the Rehabilitation Treatment Specification System framework. MAIN CONTRIBUTION: A total of 25 studies met the inclusion criteria. The studies included participants with neurogenic communication impairments secondary to stroke, Parkinson's disease, dementia and traumatic brain injury. Musical interventions identified in the studies were Melodic Intonation Therapy, Modified Melodic Intonation Therapy, choral singing, singing therapy and songwriting. The majority of the studies reported interprofessional collaboration between SLTs and at least one other healthcare clinician and/or musician. Many studies also included music-based interventions lead and facilitated by musically trained SLTs. CONCLUSION: The results of the studies included in this review indicate that SLTs are using music-based interventions to target therapeutic goals to improve speech, language, voice and quality of life in collaboration with other clinicians and professional musicians. WHAT THIS PAPER ADDS: What is already known on this subject A growing body of research indicates that interventions using music (i.e., choirs and songwriting) and musical elements (i.e., rhythm and dynamics) show promising results for adults with neurogenic communication impairments. Currently, however, there is no clear indication of how speech-language therapists (SLTs) are using music in their clinical practice. What this study adds This scoping review collates the current evidence on how SLTs use music and musical elements in their clinical practice. SLTs are using music and musical elements for individuals with neurogenic communication impairments in populations such as Parkinson's disease, dementia and traumatic brain injury. Common interventions described in the literature include Melodic Intonation Therapy, choral singing, singing therapy and songwriting. What are the clinical implications of this work? Many SLTs collaborate when delivering music-based interventions, particularly with music therapists (MTs). This scoping review suggests that SLTs should continue to explore music-based interventions in collaboration with MTs and professional musicians to target therapeutic goals to improve speech, language, voice and quality of life.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.979
Threshold uncertainty score0.782

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.125
GPT teacher head0.470
Teacher spread0.345 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2024
Admission routes1
Has abstractyes

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