Making Space for Social Sharing: Insights from a Community-Based Social Group for People with Dementia
Bibliographic record
Abstract
People with dementia face major challenges in maintaining active social interaction. Designing digital tools for social sharing within families and care facilities has been well explored by HCI research, but comparatively less work has considered community settings. Situated in a community-based program for storytelling and socializing, our field observations and semi-structured interviews with people living with early-middle stage dementia, family caregivers, and program facilitators illustrate both positive and challenging aspects of social activities. We contribute a nuanced understanding of participants' social lives and identify four factors that aid in achieving positive outcomes: effective agencies for social interaction, normalized and friendly environments, collaboration and teamwork, and mediating social cues and communication. Finally, we examine our findings through the lens of past HCI work and offer insights for designing new social technologies to diversify the range of social spaces in community settings, through expanding peer collaboration, leveraging physical and virtual spaces, creating open-ended experiences, and developing flexible platforms.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| 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".