Personalized Telehealth: Redesigning Complex Care Delivery for the 65+ During the COVID Pandemic: a Survey of Patients, Caregivers, and Health-care Providers
Bibliographic record
Abstract
Background: In the context of the ongoing COVID-19 pandemic, rapid transitions have been made towards telehealth. Optimal use of telehealth in elderly patients remains poorly understood and adaptation challenges persist. Our study aimed at identifying perceptions, barriers, and possible facilitators to telehealth use amongst elderly patients with comorbidities, their caregivers, and health-care providers (HCPs). Methods: Health-care providers, patients 65 years and older with multiple comorbidities, and caregivers were recruited from outpatient clinics and invited to complete an electronic self-administered or telephone-administered survey on their perceptions of telehealth and of barriers to its implementation. Results: A total of 39 health-care providers, 40 patients, and 22 caregivers responded to the survey. Most patients (90%), caregivers (82%), and HCPs (97%) had experienced telephone visits, but few were conducted via videoconference platforms. Patients and caregivers showed interest in pursuing some future telehealth visits (68%, 86%, respectively), but felt they lacked access to technology and skills (n=8, 20%), and some felt that telehealth visits may be inferior to in-person visits (n=9, 23%). HCPs showed interest in incorporating telehealth visits into practice (n=32, 82%), but identified challenges in lack of administrative support (n=37), lack of HCP (n=28) and patient (n=37) technological skills, and limited infrastructure (n=37)/internet access (n=33). Conclusions: Older patients, caregivers, and HCPs show interest in pursuing future telehealth visits but elucidate similar barriers. Facilitating access to technology, as well as to administrative and technology support guides, could promote high quality and equal access to virtual care for the older adult.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 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".