Finding common ground: Exploring speech language pathologists’ experiences of collaboration with music therapists in treating people living with aphasia
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.019 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".