Virtual Living Coach assistant for older adults living longer independently
Bibliographic record
Abstract
Technology is a tool that can bring benefits, but may increase disparities between people due to limited accessibility or relevance.Health innovations should be more accessible for marginalized and underserved communities, including people with limited digital literacy.Co-creation is a strategy to reduce inequities by involving end users, such as patients, clients, residents, professionals, in the health innovation development and implementation.CONTENT This symposium focuses on technology acceptance, usability, and adoption in marginalized groups and underserved communities.We use the GATE's 5P framework (WHO, 2022) to describe aspects of innovative technologies: people, policy, products, provision and personnel.First, Van Waterschoot and Gramberg will talk about innovative technologies to support professionals in the communication with older adults living independently at home.A virtual assistant has been developed to increase the awareness of seniors regarding their living conditions at home.Bults, Zuidhof, van den Berg, Liu, and den Ouden will discuss barriers and opportunities for wide-scale implementation of innovative technology in healthcare.Morita, Istrate, Zalc, Rumeau, Vigouroux, and Campo will focus on sustainable AAL technology for supporting seniors in independent living shared homes.Finally, Ríos Rincón, Miguel Cruz, Daum, and Liu will challenge assumptions about digital technology acceptance among older adults.STRUCTURE Presenters from the Netherlands, Canada, and France will give a brief presentation summarizing their papers.This will be followed by round table discussions based on the GATE's 5P Framework.CONCLUSION The symposium will provide participants with an opportunity to apply the GATE's 5P framework to their work, and exchange international perspectives.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
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".