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Record W3116857933 · doi:10.1093/geroni/igaa057.2802

One Song, Many Voices: Dementia and The Power of Music

2020· article· en· W3116857933 on OpenAlexaffabout
Debra Sheets, Stuart MacDonald, André Smıth

Bibliographic record

VenueInnovation in Aging · 2020
Typearticle
Languageen
FieldPsychology
TopicMusic Therapy and Health
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsChoirDementiaDignitySingingStigma (botany)PsychologySocial isolationLonelinessQuality of life (healthcare)ShameGerontologyDevelopmental psychologySocial psychologyPsychiatryPsychotherapistMedicinePedagogyPolitical scienceDisease

Abstract

fetched live from OpenAlex

Abstract Choral singing is a novel approach to reduce dementia stigma and social isolation while offering participants a sense of purpose, joy and social connection. The pervasiveness of stigma surrounding dementia remains one of the biggest barriers to living life with dignity following a diagnosis (Alzheimer Society of Canada, 2018). This paper examines how a social inclusion model of dementia care involving an intergenerational choir for people living with dementia, their care partners and high school students can reduce stigma and foster social connections. Multiple methodologies are used to investigate the effects of choir participation on cognition, stress levels, social connections, stigma, and quality of life. Results demonstrate the positive impact of choir participation and indicate that this socially inclusive intervention offers an effective, non-pharmacological alternative for older adults living with dementia in the community. Discussion focuses on the importance of instituting meaningful and engaging dementia-friendly activities at the community level.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · 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
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.003
Scholarly communication0.0040.001
Open science0.0000.003
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 head0.064
GPT teacher head0.337
Teacher spread0.273 · 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 designQualitative
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

Citations1
Published2020
Admission routes2
Has abstractyes

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