e-Health Technological Ecosystems: Advanced Solutions to Support Informal Caregivers and Vulnerable Populations During the COVID-19 Outbreak
Bibliographic record
Abstract
Introduction: This study highlights the importance of technological ecosystems in supporting informal caregivers and vulnerable populations in coping with the ongoing coronavirus disease 2019 (COVID-19) pandemic. Methods: This study integrates the available literature on internet of things (IoT) e-health ecosystem and informal care. Results: In the first part of this article, we describe the health consequences of quarantine and isolation and outline the potential role of informal care in containing the risk of spreading the infection and reducing the burden on the health care system. Then, we present an overview of the characteristics of emerging technological ecosystems in health care and how they can be adopted as a strategic option to achieve different goals: (1) support informal carers to help vulnerable populations during quarantine and isolation and facilitate the recovery process; (2) promote the adoption of e-health and telemedicine resources to reduce the well-documented burden experienced by caregivers; and (3) lessen the various forms of digital disadvantage among vulnerable individuals, who are at more risk to be digitally excluded. In the last part of this work, we introduce solutions to overcome potential challenges related to the development and adoption of advanced technological ecosystems and propose a reflection on the legacy of COVID-19 on telemedicine. Conclusions: Thanks to the disruptive potential of IoT for health and wellness promotion, technological ecosystems emerge as a valuable resource to support both informal carers and vulnerable populations. The main factors that represent a strategic advantage of a technological ecosystem are affordability, regulatory, and availability. A high degree of interconnection between all the stakeholders emerges as a key element for the provision of intergenerational care. The most important technical challenges of IoT e-health require to optimize privacy, security, and user-friendliness of IoT e-health.
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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 | 0.002 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".