Transitioning relational arts for persons living with dementia to a virtual space
Bibliographic record
Abstract
Background While a growing body of literature explores the potentials and challenges of transitioning in-person to online arts for persons living with dementia (PLwD), little research has examined the translation of relational arts to virtual spaces. This paper explores the experiences and transition of a relational arts Academy to a virtual space for community artists with dementia.Methods Using participatory action research, we conducted 10 research conversations with artist facilitators, collaborators, and leaders involved in the transition process, and 13 observations of online arts sessions and team member huddles.Results Team members successfully translated relational arts virtually by intentionally embedding relational literacies, leveraging relational supports, and embracing creativity. Although initially reluctant, artist facilitators, collaborators, and leaders were opened to the possibilities of relational arts in virtual spaces.Conclusion This research demonstrates the feasibility of relational arts for PLwD in virtual spaces, and offers insights to inform other virtual art programs for PLwD and their care partners.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".