Choral singing with dementia: Examining the experience and impact of embodied and relational musical performance
Bibliographic record
Abstract
Abstract Background Promising research points to the potential of singing as a novel intervention to improve cognitive function and reduce levels of stress, anxiety, loneliness, and depression in persons with dementia (PwD) (Elliott and Gardner, 2018; Unadkat et al., 2016). This study explores the impact of participation in Voices in Motion (ViM), an intergenerational community choir program designed to engage and support persons with dementia (PwD), their care partners, and students in Victoria, British Columbia, Canada. Combining narrative interviews, focus groups and observations, the study examined care partners and PwD’s experiences of ViM and their views of the benefits of choir involvement and considered the impact of ViM on social inclusion and social relationships. Method Over an 18‐month period, the study initiated and followed two ViM choirs—one active in research for three 3‐month sessions and the other for two sessions of the same time length. Data came from interviews with 23 dyads of PwD and care partners and focus groups with 29 high school students across the two ViM choirs. The interviews and focus groups were conducted at the end of each choir season over the course of the study. Result The thematic analysis revealed that PwD maintained an embodied ability to learn, experience, and perform songs in the choir despite considerable deficits in cognition, language, and memory. Results indicate that choral participation, as a type of embodied activity, effectively engages PwD and allows them to meaningfully express themselves as both singers and human beings. The ViM choirs facilitated the emergence of strong social relationships between PwD and care partners with the students who reported gaining a deeper and sympathetic understanding of PwD as individuals with rich life stories. Choral singing also served as an inspiring avenue for PwD to develop a strong sense of self and identity as choristers and performers. Conclusion ViM facilitates the re‐humanization of dementia through well‐attended concerts and performances at public events while also challenging social narratives of decline which remain prevalent in society and stigmatize individuals living with dementia.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".