Conversation for change: engaging older adults as partners in research on gerotechnology
Bibliographic record
Abstract
There is increasing research and public policy investment in the development of technologies to support healthy aging and age-friendly services in Canada. Yet adoption and use of technologies by older adults is limited and rates of abandonment remain high. In response to this, there is growing interest within the field of gerotechnology in fostering greater participation of older adults in research and design. The nature of participation ranges from passive information gathering to more active involvement in research activities, such as those informed by participatory design or participatory action research (PAR). However, participatory approaches are rare with identified barriers including ageism and ableism. This stigma contributes to the limited involvement of older adults in gerotechnology research and design, which in turn reinforces negative stereotypes, such as lack of ability and interest in technology. While the full involvement of older adults in gerotechnology remains rare, the Older Adults' Active Involvement in Ageing & Technology Research and Development (OA-INVOLVE) project aims to develop models of best practice for engaging older adults in these research projects. In this comment paper, we employ an unconventional, conversational-style format between academic researchers and older adult research contributors to provide new perspectives, understandings, and insights into: (i) motivations to engage in participatory research; (ii) understandings of roles and expectations as research contributors; (iii) challenges encountered in contributing to gerotechnology research; (iv) perceived benefits of participation; and (v) advice for academic researchers.
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 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 | 0.025 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.010 |
| 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; both teacher heads agree on what is shown here.
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".