OLDER ADULTS’ PERSPECTIVES ON USING TECHNOLOGY TO ASSESS SOCIAL ISOLATION AT THE ONSET OF COVID-19
Bibliographic record
Abstract
Abstract Social isolation (SI) among older adults (OA) is a global health issue that was exacerbated by the pandemic. This study examines OA’s perspectives on technology use (i.e., home-based ambient sensor or wearable technology) to assess SI, with consideration of understanding these perspectives in relation to the pandemic. Two questions were examined: (i) How familiar are OA with technology that can be used to assess SI? and (ii) Are OA agreeable to using technology to assess SI? Online surveys (n=4379) and semi-structured interviews were completed by community-dwelling OA at the onset of COVID-19. Quantitative data were analyzed using descriptive statistics and qualitative data were analyzed using content analysis. From survey responses, 16.79% indicated they would be agreeable to using technology to assess SI, whereas 40.37% and 42.83% indicated no and maybe/not sure, respectively. Although most participants were familiar with some technologies that could be used as part of an SI assessment tool, interviews suggested they were unfamiliar with this application of technologies. Preliminary analysis of the interviews (n=31) revealed three broad categories addressing the research questions: (1) perceived barriers to sensor-based technology adoption, (2) user experience and acceptance, and (3) changing social norms. Our study is one of the first to examine the views of OA regarding familiarity and openness to technology use for SI assessment that emerged with COVID-19. Technology developers and healthcare providers need to address ethical concerns around sensor-based technology use and increase opportunities to improve OA’s digital literacy and accessibility to support technology acceptance for SI assessment.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".