Community Choir Improves Vocal Production Measures in Individuals Living with Parkinson's Disease
Bibliographic record
Abstract
OBJECTIVES: Parkinson's disease (PD) is a neurodegenerative disease leading to motor impairments and dystonia across diverse muscle groups including vocal muscles. The vocal production challenges associated with PD have received considerably less research attention than the primary gross motor symptoms of the disease despite having a substantial effect on quality of life. Increasingly, people living with PD are discovering group singing as an asset-based approach to community building that is purported to strengthen vocal muscles and improve vocal quality. STUDY DESIGN/METHODS: The present study investigated the impact of community choir on vocal production in people living with PD across two sites. Prior to and immediately following a 12-week community choir at each site, vocal testing included a range of vocal-acoustic measures, including lowest and highest achievable pitch, duration of phonation, loudness, jitter, and shimmer. RESULTS: Results showed that group singing significantly improved some, though not all, measures of vocal production. Group singing improved lowest pitch (both groups), duration (both groups), intensity (one group), jitter (one group), and shimmer (both groups). CONCLUSIONS: These findings support community choir as a feasible and scalable complementary approach to managing vocal production challenges associated with PD.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".