Improvised active music therapy for clients with Parkinson’s disease: A feasibility study
Bibliographic record
Abstract
The purpose of this study was to test the feasibility of delivering Improvised Active Music Therapy sessions in measuring the impact of acquisition of rhythmic complexity levels on gait performance in individuals with Parkinson’s disease. In this single subject multiple baseline design, the study measured the ability of three right-handed participants with Parkinson’s disease to acquire greater density of syncopation, as a measure of rhythmic complexity levels, while playing uninterrupted improvised music on a simplified electronic drum-set. The music content of the sessions was transformed into digital music data in real-time using Musical Instrument Digital Interface. The Musical Instrument Digital Interface data were analyzed to determine the participants’ and the Music Therapist’s density of syncopation (on acoustic guitar) during baseline and treatment conditions. Results from visual analyses and Pearson’s correlations on the outcomes indicated conflicting and inconclusive outcomes about whether higher acquisition of rhythmic complexity levels improves gait performance in individuals with Parkinson’s disease. Despite this, evidence was found to support the overall value of Improvised Active Music Therapy sessions on gait performance. The study design, the intervention, and outcome measures were found to be feasible and could be scaled-up into a larger trial.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".