Towards Health Care Service Ecosystem Management for the Elderly
Bibliographic record
Abstract
As an increasing percentage of the global population joining the elderly age group, more financial strain is being placed on health care institutions and governments worldwide. To alleviate this problem, it is necessary to involve volunteers with various skills and backgrounds to help serve part of the elderly’s health care needs. Future health care service digital ecosystems have been envisioned to serve this purpose. However, there is a lack of management mechanisms for them that can holistically balance the goals of various stakeholders. In this paper, we provide a vision to face the challenge of health care service ecosystem from an interdisciplinary perspective. We propose a novel computational approach to simplify the problem of health care service ecosystem management and model it as a constraint optimization problem. By focusing on the stability and efficiency in usage health care information resources, the formulation articulates actionable objectives and constraints to make scalable and real-time solutions possible. Under such a vision, we discuss potential future research directions in the area of utility function formulation, game theoretic analysis and accommodating the special preferences of the elderly.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".