AīCare: An Affordable, Reliable, and Intelligent Senior Care Ecosystem
Bibliographic record
Abstract
Global population aging has become an issue nowadays, which will eventually lead to a huge senior care market in the future. Currently, with the current senior care services and product, several critical issues exist which prevents seniors from getting care. The top reported problem includes the lack of professionals, overpriced services or products, insufficient attention to seniors’ mental needs, and insufficient government regulations. In this paper, a senior care ecosystem, named AīCare, was proposed to revolutionize the future senior care industry. The AīCare senior care ecosystem combined exponential technologies, such as IoT, robotics, and AI, to deliver affordable, reliable, and intelligent senior care services and products. The project was systematically planned and designed utilizing the Purpose Launchpad, coupled with business development tools provided by the Purpose Alliance. A low-fidelity prototype of this ecosystem was conceptualized as part of the innovation process. To substantiate the proposed hypotheses, two experimental cycles were implemented across China and Canada. The feedback acquired from these experiments continuously refines our ideas and directs the way for future development in this crucial sector. Our senior care ecosystem is not designed to entirely supplant the existing workforce of senior care professionals. Rather, our objective is to provide dependable and effective aids to complement their efforts, thereby enhancing their capability to cater to individuals requiring additional attention. This innovative approach intends to amplify the efficacy of current systems and contributes significantly towards the evolution of senior care, ultimately augmenting the quality-of-care provision.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".