The Factors Associated With Nonuse of Social Media or Video Communications to Connect With Friends and Family During the COVID-19 Pandemic in Older Adults: Web-Based Survey Study
Bibliographic record
Abstract
BACKGROUND: Digital technologies have enabled social connection during prolonged periods of physical distancing and travel restrictions throughout the COVID-19 pandemic. These solutions may exclude older adults, who are at higher risk for social isolation, loneliness, and severe outcomes if infected with SARS-CoV-2. OBJECTIVE: This study investigated factors associated with nonuse of social media or video communications to connect with friends and family among older adults during the pandemic's first wave. METHODS: A web-based, cross-sectional survey was administered to members of a national retired educators' organization based in Ontario, Canada, between May 6 and 19, 2020. Respondents (N=4879) were asked about their use of social networking websites or apps to communicate with friends and family, their internet connection and smartphone access, loneliness, and sociodemographic characteristics. Factors associated with nonuse were evaluated using multivariable logistic regression. A thematic analysis was performed on open-ended survey responses that described experiences with technology and virtual connection. RESULTS: Overall, 15.4% (751/4868) of respondents did not use social networking websites or apps. After adjustment, male gender (odds ratio [OR] 1.60, 95% CI 1.33-1.92), advanced age (OR 1.88, 95% CI 1.38-2.55), living alone (OR 1.68, 95% CI 1.39-2.02), poorer health (OR 1.33, 95% CI 1.04-1.71), and lower social support (OR 1.44, 95% CI 1.20-1.71) increased the odds of nonuse. The reliability of internet connection and access to a smartphone also predicted nonuse. Many respondents viewed these technologies as beneficial, especially for maintaining pre-COVID-19 social contacts and routines, despite preferences for in-person connection. CONCLUSIONS: Several factors including advanced age, living alone, and low social support increased the odds of nonuse of social media in older adults to communicate with friends and family during COVID-19's first wave. Our findings identified socially vulnerable subgroups who may benefit from intervention (eg, improved access, digital literacy, and telephone outreach) to improve social connection.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".