Bibliographic record
Abstract
BACKGROUND: The world is experiencing a dramatic increase in the aging population, challenging the sustainability of traditional care models that have relied on in-person monitoring. This debate article discusses whether artificial intelligence health monitoring may be suitable enhancement or replacement for elder care. MAIN TEXT: Internationally, as life expectancy continues to rise, many countries are facing a severe shortage of direct care workers. The health workforce is aging, and replacement remains a challenge. Artificial intelligence health monitoring technologies may play a novel and significant role in filling the human resource gaps in caring for older adults by complementing current care provision, reducing the burden on family caregivers, and improving the quality of care. Nonetheless, opportunities brought on by these emerging technologies raise ethical questions that must be addressed to ensure that these automated systems can truly enhance care and health outcomes for older adults. This debate article explores some ethical dimensions of using automated health monitoring technologies. It argues that, in order for these health monitoring technologies to fulfill the wishes of older adults to age in place and also to empower them and improve their quality of life, we need deep knowledge of how stakeholders may balance their considerations of relational care, safety, and privacy. CONCLUSION: It is only when we design artificial intelligence health monitoring technologies with intersecting clinical and ethical factors in mind that the resulting systems will enhance productive relational care, facilitate independent living, promote older adults' health outcomes, and minimize waste.
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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 | 0.029 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".