Dancing With Dementia: Exploring the Embodied Dimensions of Creativity and Social Engagement
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Dance is increasingly being implemented in residential long-term care to improve health and function. However, little research has explored the potential of dance to enhance social inclusion by supporting embodied self-expression, creativity, and social engagement of persons living with dementia and their families. RESEARCH DESIGN AND METHODS: This was a qualitative sequential multiphase study of Sharing Dance Seniors, a dance program that includes a suite of remotely streamed dance sessions that are delivered weekly to participants in long-term care and community settings. Our analysis focused on the participation of 67 persons living with dementia and 15 family carers in residential long-term care homes in Manitoba, Canada. Data included participant observation, video recordings, focus groups, and interviews; all data were analyzed thematically. RESULTS: We identified 2 themes: playfulness and sociability. Playfulness refers to the ways that the participants let go of what is "real" and became immersed in the narrative of a particular dance, often adding their own style. Sociability captures the ways in which the narrative approach of the Sharing Dance Seniors program encourages connectivity/intersubjectivity between participants and their community; participants co-constructed and collaboratively animated the narrative of the dances. DISCUSSION AND IMPLICATIONS: Our findings highlight the playful and imaginative nature of how persons living with dementia engage with dance and demonstrate how this has the potential to challenge the stigma associated with dementia and support social inclusion. This underscores the urgent need to make dance programs such as Sharing Dance Seniors more widely accessible to persons living with dementia everywhere.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".