Facilitating co-design among older adults in a digital setting: methodological challenges and opportunities
Bibliographic record
Abstract
Healthy ageing is a global priority due to a growing older population, which increases the need for preventive measures and tailored technology. In health technology development, co-design is emphasised as a valuable strategy to support a person-centred approach. Co-design, a value-driven and collaborative approach, involves end users in development processes to overcome barriers connected to capability, opportunity, and motivation. While a growing number of older adults are involved in design processes, there is a deficit of suitable methodologies for achieving active involvement. Additionally, the COVID-19 pandemic necessitated a shift to developing methodological skills and tools to facilitate co-design remotely in a digital setting. Here, we draw on experiences of conducting iterative co-design workshops with a Canadian and a Swedish cohort of older adults about technology development to support mobility, balance, and confidence in daily movement. We describe and discuss methodological and ethical challenges and opportunities to provide recommendations for conducting co-design research in a digital setting with older adults (+65 years). Our recommendations include the use of live mind mapping to facilitate participation involvement, and we address the issue of ‘homework’ in co-design and the importance of setting expectations.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".