mHealth as a Key Component of a New Model of Primary Care for Older Adults
Bibliographic record
Abstract
Unlabelled: With population aging, an increase in total life expectancy at birth (TLE) should ideally be accompanied by an equal increase in health span (HS), or by a trend in increasing HS/TLE ratio. Hong Kong has one of the longest life expectancies in the world; however, there is a trend of declining HS/TLE ratio, such that the absolute number of people with dependencies is increasing. To address this challenge, the World Health Organization proposed the model of integrated care for older people (ICOPE) that combines both health and social elements in community care, using the measurement of intrinsic capacity (IC) as a metric for monitoring the performance in different countries. The use of technology is essential in achieving a wide coverage of the population in assessing IC, followed by an individually tailored plan of action. This model can be adapted to different health and social care systems in different countries. Hong Kong has an extensive network of community centers, where the basic assessment may be based, followed by further assessments and personalized activities, and referral to medical professionals may only be needed in the presence of disease. Conversely, the medical sector may refer patients to the community for activities designed to optimize the various domains of IC. Such a model of care has the potential to address manpower shortage and mitigate inequalities in healthy aging, as well as enable the monitoring of physiological systems in community-dwelling adults using digital biomarkers as a metric of IC.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".