Dance Wherever You Are: The Evolution of Multimodal Delivery for Social Inclusion of Rural Older Adults
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Older adult social inclusion involves meaningful participation that is increasingly mediated by information communication technology and in rural areas requires an understanding of older adults' experiences in the context of the digital divide. This article examines how the multimodal streaming (live, prerecorded, blended in-person) of the Sharing Dance Older Adults program developed by Canada's National Ballet School and Baycrest influenced social inclusion processes and outcomes in rural settings. RESEARCH DESIGN AND METHODS: Data were collected from on-site observations of dance sessions, research team reflections, focus groups, and interviews with older adult participants and their carers in pilot studies in the Peterborough region of Ontario and the Westman region of Manitoba, Canada (2017-2019). There were 289 participants including older adults, people living with dementia, family carers, long-term care staff, community facilitators, and volunteers. Analytic themes were framed in the context of rural older adult social exclusion. RESULTS: Remote delivery addressed barriers of physical distance by providing access to the arts-based program and enhancing opportunities for participation. Constraints were introduced by the use of technology in rural areas and mitigated by in-person facilitators and different streaming options. Meaningful engagement in dynamic interactions in the dance was achieved by involving local staff and volunteers in facilitation of and feedback on the program and its delivery. Different streaming technologies influenced social inclusion in different ways: live-stream enhanced connectedness, but constrained technical challenges; prerecorded was reliable, but less social; blended delivery provided options, but personalization was unsustainable. DISCUSSION AND IMPLICATIONS: Understanding different participants' experiences of different technologies will contribute to more effective remote delivery of arts-based programs with options to use technology in various contexts depending on individual and organizational capacities.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".