Musical Connections: A Descriptive Study of Community-Based Choirs for Persons with Dementia and their Caregivers
Bibliographic record
Abstract
This descriptive qualitative study explores the key characteristics, benefits, and lessons learned from community-based choirs for persons with dementia (PwD) and their caregivers based on reports from choir administrators and directors. Although there is growing interest in choirs for PwD, there has been no synthesis of information on these choirs. Semi-structured interviews were conducted between December 2016 and February 2017 with six administrators and/or directors of community-based choirs for PwD and their caregivers. The interviews were audio-recorded and transcribed. Content analyses indicated that choirs had many similarities in membership (e.g., early to mid-stage dementia), establishing formal sections (e.g., soprano, alto, tenor, bass), administration (e.g., leadership, fees), and music programming (e.g., public performance, duration, and length of practice sessions). Benefits of the choir include enjoyment, sense of purpose, empowerment, caregiver support and respite, and increased awareness of dementia by others. In conclusion, this descriptive study suggests that community-based choirs are a cost effective and valuable program that improve quality of life for PwD and caregivers.
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 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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".