Supporting Vulnerable Older Adults With Telehealth Through Wellness Calls and Tablet Distribution During COVID-19: Quality Improvement Project
Bibliographic record
Abstract
BACKGROUND: Loneliness, social isolation, and lack of technical literacy are associated with poorer health outcomes. To help improve social connection during the COVID-19 pandemic, Nova Southeastern University's South Florida Geriatric Workforce Enhancement Program partnered with a community-based organization to provide educational resources to promote telehealth services. OBJECTIVE: This study aimed to provide educational resources to older adults with limited resources and promote the use of telehealth services in this population. METHODS: Through this pilot project, we contacted 66 vulnerable older adults who expressed interest in telehealth support through wellness calls, with 44 participants moving on to participate in tablet usage. All tablets were preloaded with educational information on using the device, COVID-19 resources, and accessing telehealth services for patients, caregivers, and families. RESULTS: Feedback from wellness assessments suggested a significant need for telehealth support. Participants used the tablets mainly for telehealth (n=6, 15%), to connect with friends and family (n=10, 26%), and to connect with faith communities (n=3, 8%). CONCLUSIONS: The findings from the pilot project suggest that wellness calls and telehealth education are beneficial to support telehealth usage among older adults.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".