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Record W4409955735 · doi:10.56883/aijmt.2025.605

Finding common ground: Exploring speech language pathologists’ experiences of collaboration with music therapists in treating people living with aphasia

2025· article· en· W4409955735 on OpenAlexafffund
Christine Hudson, Heidi Ahonen

Bibliographic record

VenueApproaches An Interdisciplinary Journal of Music Therapy · 2025
Typearticle
Languageen
FieldPsychology
TopicMusic Therapy and Health
Canadian institutionsWilfrid Laurier University
FundersWilfrid Laurier University
KeywordsAphasiaPsychologyMusic therapyCommon groundLinguisticsCommunicationPsychotherapistCognitive psychology

Abstract

fetched live from OpenAlex

The purpose of this phenomenological study was to investigate speechlanguage pathologists’ (SLPs’) experiences of collaboration with music therapists (MTs) in treating people with aphasia. Our analysis of the data yielded mixed outcomes, highlighting/identifying aspects that support and challenge collaboration. Data was collected using semi-structured interviews with three participants. The participants were SLPs who had experience treating people with aphasia in hospital and community-based settings. Thematic analysis was used to identify components of MT-SLP collaborations in treating aphasia. Results revealed the following themes: personal and clinical aspects, and systemic challenges of MT-SLP collaborations. Participants’ feedback on the thematic analysis was incorporated into the discussion which presents insights into the overarching qualities of successful MT-SLP collaboration and the contributions of music in aphasia treatment. This research provides a list of music interventions which may be a resource for SLPs and MTs in treating aphasia. Additionally, topics discussed in this research may assist SLPs and MTs in advocating for collaborative care of people living with aphasia.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.098
Threshold uncertainty score0.862

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.100
GPT teacher head0.367
Teacher spread0.267 · 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 designQualitative
Domainnot available
GenreEmpirical

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

Citations0
Published2025
Admission routes2
Has abstractyes

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