Technology and Social Isolation, Loneliness, and Health Inequities Among Older Adults
Bibliographic record
Abstract
Abstract Because of the COVID-19 pandemic, older adults have been advised to stay-at-home to reduce the risk of infection. Social distancing and quarantine measures increase their vulnerability to adverse health outcomes like depression and cardiovascular disease. Technology is an effective tool to promote social connectedness among older adults affected by the pandemic; however, its role in reducing loneliness and health inequities is not well understood. The goal of this project was to construct a model for how technologies may be deployed to mitigate the impact of a pandemic on social isolation, loneliness, and health inequities for older adults. PubMed, SCOPUS, and PsychINFO were searched for the following keywords: “social isolation,” “loneliness,” “social support,” “resilience,” “technology,” “pandemic,” and “health inequities.” Articles selected for full analysis attempted to understand or observe how technology alleviates social isolation and/or loneliness among older adults. Research evidence indicates that using technology reduces loneliness directly and indirectly (by reducing social isolation) and can strengthen social support, which in turn promotes resilience among older adults. Video-based technologies encourage care-seeking behaviors in this population. There is insufficient evidence to determine technology’s relationship to health inequities experienced by older adults. The model we have proposed should help advance research on the relationship between technology and health inequities among older adults that may be aggravated by pandemic-like situations. We hypothesize that technology interventions for social support and functional competence should be sequenced in order to have the best effects on reducing health disparities.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".