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.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".