Music Connects Us: Development of a Music-Based Group Activity Intervention to Engage People Living with Dementia and Address Loneliness
Bibliographic record
Abstract
There is a need for intervention research to understand how music-based group activities foster engagement in social interactions and relationship-building among care home residents living with moderate to severe dementia. The purpose of this conceptual paper is to describe the design of 'Music Connects Us', a music-based group activity intervention. Music Connects Us primarily aims to promote social connectedness and quality of life among care home residents living with moderate to severe dementia through engagement in music-making, supporting positive social interactions to develop intimate connections with others. To develop Music Connects Us, we adapted the 'Music for Life' program offered by Wigmore Hall in the United Kingdom, applying an intervention mapping framework and principles of engaged scholarship. This paper describes in detail the Music Connects Us program, our adaptation approach, and key adaptations made, which included: framing the project to focus on the engagement of the person living with dementia to ameliorate loneliness; inclusion of student and other community-based musicians; reduced requirements for care staff participation; and the development of a detailed musician training approach to prepare musicians to deliver the program in Canada. Description of the development, features, and rationale for Music Connects Us will support its replication in future research aimed to tests its effects and its use in clinical practice.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".