A Participatory Design Methodology to Elicit Aging- in-Place Stakeholder Concerns with Ambient Assistive Living (AAL) Devices During COVID-19
Bibliographic record
Abstract
For varying reasons, our world is experiencing increasing life expectancies and decreasing birth rates, which has led to a generational shift in population distribution. The Government of Canada predicts that in the year 2030, over 9.5 million (23%) Canadians will be 65 years or older. For this growing demographic of older adults, intelligent home health technologies have been proposed as one beneficial avenue to support and maintain an individual’s health and wellness as they begin experiencing aging-related health effects. However, many ethical concerns have been raised regarding the design and deployment of intelligent home healthcare technologies in aging- in-place settings such as long-term care and nursing homes. This paper presents a revised participatory design methodology to identify aging-in-place stakeholders’ ethical concerns with two Ambient Assistive Living (AAL) devices. The main objective of this paper is to develop and test a participatory design research method that is well suited for older adults living in long-term care settings, which is currently lacking. Developed by an interdisciplinary team of engineers and social science researchers, this paper presents the participatory method that was designed and tested in a long-term care facility by collaborating with a mix of aging-in-place stakeholders, including older adults and healthcare professionals. By interweaving interactive activities, hand-written tasks, and discussions throughout the data collection process, the methodology successfully identified stakeholders’ ethical concerns with the devices.
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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 | 0.100 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| 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".