Exploring Technology Supporting Aging-in-Place Using an Equity Lens Through Focus Groups and World Café–Informed Research Agenda: Qualitative Study
Bibliographic record
Abstract
Background: Older adults prefer to age in their home or community of choice, which could include naturally occurring retirement communities (NORCs). As a place with a high density of older adults, NORCs could be sites where technology is leveraged to support independence and aging in the right place. However, there is limited research on how technology adoption and use occur in NORCs in ways that support older adults. Objective: This study aims to cocreate a research agenda on equity-informed technology considerations that help older adults live independently in NORCs. Methods: This is a 2-phase sequential qualitative descriptive study of 5 community-based focus groups and an in-person World Café event. We use the focus group method to acquire data about older adults' experiences with and perceptions of using technology to support aging-in-place in NORC settings. This data informs the design and facilitation of deliberate dialogues at the World Café event. Three questions helped to guide the small group discussions. The World Café is a creative, collaborative, and conversation-generating method that aims to generate exchanges between people with different views on a particular topic. Results: In total, 45 NORC residents participated in a focus group about their experience and use of technology. The data revealed 3 central categories that highlight the perception of the use of technology to support the independence of participants in their homes and communities, its challenges, and areas to consider when deploying technology for helping older adults age in place. The subsequent World Café event included 40 participants and a combination of NORC residents, service providers, researchers, technology innovators, and policy makers. Insights drawn from the focus groups and World Café informed a 10-question research agenda about equity-informed technology principles that span accessible support, accessible interfaces, affordable and equitable access, available digital literacy training, accessible data, and accessible partnerships. Conclusions: Our study explores NORCs as potential environments for offering a transformative opportunity to address equity considerations for technology supporting aging in place. Our findings and research agenda highlight critical areas for consideration, including leveraging partnerships, integrating public and private technology ecosystems, and designing technology with older users that evolves with the population's needs. Notably, by embedding principles of equity, inclusivity, and user-centered design, the collective of developers, researchers, and service providers can ensure that emerging technology serves diverse aging populations equitably and effectively.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".