Duet playing in dementia care: a new therapeutic music technology
Bibliographic record
Abstract
PURPOSE: Supporting the relational worlds of people living with dementia, especially the spousal dyad, is a growing focus in dementia care as is advancing the therapeutic use of music in dementia care. This paper describes a mixed-methods, multi-phase, iterative research study designed to develop the Music Memory Makers (MMM) Duet System, a novel therapeutic music technology, that allows non-musicians to play a personalized repertoire of songs arranged as duets. METHODS: Following a pilot phase to iteratively assess and refine the MMM Duet System for recreational and therapeutic purposes, multiple sources of data were used to investigate five older spousal dyads' experiences with the system, two couples living with dementia and three who were not. We assessed perceptions of task difficulty, joint agency, and enjoyment as well as therapeutic benefits associated with enhancing the spousal relationship and sense of couplehood. RESULTS: Findings suggest playing meaningful songs together is an enjoyable interactive activity that prompts musical reminiscence, involves joint agency, and supports relationship continuity within a relational, positive approach to dementia care. All couples mastered the task, none evaluated it as "very challenging," and positive couple interactions were evoked, commonly before and after playing the duets. CONCLUSIONS: The MMM Duet System is recommended for further research and development as an innovative way to support couples living with dementia with commercial implications, and as a new music technology suitable for use as a research tool.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 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".