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Record W4416976883 · doi:10.3389/frym.2025.1479828

How Singing Helped the Voices of People Living With Parkinson’s

2025· article· W4416976883 on OpenAlexfundno aff
Tara Raessi, Arla Good, Frank Russo

Bibliographic record

VenueFrontiers for Young Minds · 2025
Typearticle
Language
FieldPsychology
TopicMusic Therapy and Health
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSingingChoirGroup livingOlder peopleLarge group

Abstract

fetched live from OpenAlex

Parkinson’s is an illness involving damage to the parts of the brain that help people move their muscles, including the muscles used to speak. Researchers are beginning to discover how group singing can help people living with Parkinson’s to communicate better, and we wanted to learn more. In this study, we had groups of people living with Parkinson’s sing in a choir program every week for 12 weeks. By the end of the program, we found that group singing improved how low participants could sing and how long they could sing one note. We also found that group singing helped the participants sing one note with more stability. Overall, the results of our study show the benefits of group singing and its potential to help people living with Parkinson’s.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0030.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.013
GPT teacher head0.277
Teacher spread0.264 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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 routes1
Has abstractyes

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