A Systematic Review of Telemedicine for Older Adults With Dementia During COVID-19: An Alternative to In-person Health Services?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Introduction: Older adults with dementia have been significantly at more risk for not receiving the care needed and for developing further mental health problems during COVID-19. Although the rise in telemedicine adoption in the healthcare system has made it possible for patients to connect with their healthcare providers virtually, little is known about its use and effects among older adults with dementia and their mental health. Objective: This systematic review aimed to explore the use, accessibility, and feasibility of telemedicine in older adults with dementia, as well as examine the potential mental health impacts of these technologies, through reviewing evidence from studies conducted during COVID-19. Methods: PubMed, Scopus, and Web of Science databases were searched with the following keywords: (COVID * OR SARS-CoV-2 OR Coronavirus) AND (“mental health” OR Depression OR Stress) AND (Dementia OR Multi-Infarct Dementia OR Vascular Dementia OR Frontotemporal Dementia) AND (elder OR Aging OR Aging OR Aged) AND (Telemedicine OR “Remote Consultation” OR telehealth OR technology). Results: A total of 7 articles from Asia, Europe, and the United States were included in this review. Throughout the studies cognitive and mental health assessments (e.g., MoCA, FAST, etc.) were performed. Despite the barriers, telemedicine was noted as a feasible approach to assist individuals with dementia in connecting with their service providers and family while reducing complications related to travel (e.g., difficulty moving, traffic, distance). Conclusions: Due to the COVID-19 pandemic, finding alternative ways to provide services to older adults with dementia through technology may continue to become more necessary as time goes on.
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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) | 0.006 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 it