A Library-Based "Tech Club" for Older Adults Living with Dementia and Their Care Partners: A Codesigned Pilot Project
Bibliographic record
Abstract
Abstract: Addressing the digital divide is recognized as important, but less is known about how best to support older adults (fifty-five+) living with dementia and their care partners' digital learning needs. This paper reports on a partnership project between a university aging research center and a local Canadian public library. The aim was to support the social participation, connection, and inclusion of community-dwelling older adults (fifty-five+) living with dementia and their care partners through a codesigned "tech club" to address their self-identified digital learning needs. Data was collected between June 2024 and January 2025 via two codesign workshops, one-on-one pre-interviews, pre- and post-session mood questionnaires, ethnographic style field notes taken during each "tech club" session, and follow-up focus groups and interviews. Data analysis consisted of descriptive statistics and a thematic analysis. We report findings related to the participants' motivations for attending a dementia tech club, perceived social well-being benefits, potential challenges of a dementia tech club, and the importance of promoting tech-based opportunities to individuals living with dementia and their care partners. The findings demonstrate a mechanism (tech clubs) to address the digital divide for people living with dementia and promote social connection.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".