Bibliographic record
Abstract
Background The aging of the population is a global phenomenon, with growing numbers of persons over the age of 65 years, greater diversity of aging societies, and fewer younger people available to provide care and support for older adults. At the same time, enabling technology offers new solutions for aging well, including self-management of chronic conditions, communication with family and the health care team, passive monitoring, and enriching the home and community environments. Objective This keynote address highlights the demand characteristics for healthy aging and identifies potential solutions and challenges with enabling technology. Methods This presentation is based on literature review and engagement with diverse scientific collaborators. Results Major societal trends include the following: the growth of the older population with associated increases in the prevalence of chronic conditions and functional and cognitive disability; increased demand for both health and social services; increased demands on family caregivers at a time when there are fewer caregivers available; explosion of health information and desire to self-manage chronic conditions while remaining at home; widespread workforce shortages; and escalating costs of care. The COVID-19 pandemic exposed the urgency of these demands and exacerbated health needs and workforce shortages while accelerating systematic change to address emergent challenges. New solutions are required to promote health, well-being, and health equity that entail both care model redesign and deployment of enabling technology. Optimal care for the future will place the older adult at the center; assure that information is available to all for good decision-making; and deploy human resources in the most effective way possible, providing the right person at the right time for the right task. Conclusions Technology has the potential to collect and make meaningful use of everyday data to inform plans for care; engage and optimize communication among the older adult, family, and care team; and enhance function and well-being. Actualizing this future requires appropriate policy, training, and leadership. Conflicts of Interest None declared.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".