Music Moves: Intergenerational Silent Disco Parties in Long-Term Care
Bibliographic record
Abstract
OBJECTIVES: Older adults in long-term care (LTC) often experience loneliness and social isolation. This study explores the experiences of residents participating in silent disco headphones (SDH) parties alongside an intergenerational group of facilitators. The objective was to examine the acceptability of intergenerational SDH parties in fostering social engagement, inclusion, and emotional well-being. DESIGN: Qualitative study using video ethnography and thematic analysis. SETTING AND PARTICIPANTS: The study took place in an LTC home in British Columbia, Canada. Participants included 22 residents, 2 family caregivers, and 40 staff members who engaged in or observed the SDH parties. METHODS: Over 6 weeks, data were collected through video recordings, conversational interviews, observations, and focus groups with staff members. Thematic analysis was conducted to identify key themes in residents' experiences. The study was reported in accordance with the COREQ Checklist. RESULTS: Three themes emerged: (1) Dancing with students fosters intergenerational togetherness-residents valued the presence of younger facilitators who promoted social connection and emotional well-being. (2) Music connects and includes everyone-personalized music selections evoked memories, encouraged participation, and fostered nonverbal engagement. (3) Party twice a week builds social capacity-regular SDH sessions created anticipation, strengthened social bonds, and offered moments of shared joy. CONCLUSIONS AND IMPLICATIONS: Intergenerational SDH parties show promise in reducing social isolation and enhancing well-being in LTC residents. The customizable format promotes autonomy and meaningful engagement, particularly for individuals with cognitive or physical impairments. Findings support the integration of interactive, music-based interventions in LTC settings to foster social connection and improve residents' quality of life. Future research should explore the long-term effects of SDH parties on residents' emotional and social health, as well as best practices for sustaining these programs within LTC communities.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".