Exploring Music-Based Interventions for Executive Functioning and Emotional Well-Being in Stroke Rehabilitation: A Scoping Review
Bibliographic record
Abstract
PURPOSE: Stroke is one of the leading causes of disability with life-long implications requiring assessment and treatment of several functional domains. This review identifies the results from research into music-based interventions (MBIs), including music therapy (MT), for executive functions (EFs) and emotional well-being (EWB) in adults with stroke and highlights opportunities for clinical practice and future research. METHODS: APA PsycInfo (EBSCOhost), and CINAHL (EBSCOhost) were searched, in addition to grey literature. RESULTS: A total of 49 studies were included and encompassed experimental, analytic, and descriptive observational studies, and case reports, involving a total of 1663 participants. In total, 32 studies included MT interventions, and 17 were MBIs. EFs were an outcome in 20.41%, and EWB in 61.22% of studies, for which active interventions were the most utilized. Overall, 73.47% of the studies reported positive results. CONCLUSIONS: This scoping review indicates that music interventions can be beneficial for the improvement of different aspects of EFs and EWB at different stages of stroke recovery. Further research may benefit clinical practice by including standardized protocols, outcome and self-reported measures, and brain imaging data to determine the effects of interventions and support evidence-based decisions for treatment policies for stroke survivors.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".