Older Adults’ Experiences With Using Technology for Socialization During the COVID-19 Pandemic: Cross-sectional Survey Study
Bibliographic record
Abstract
BACKGROUND: Technology use has become the most critical approach to maintaining social connectedness during the COVID-19 pandemic. Older adults (aged >65 years) are perceived as the most physiologically susceptible population to developing COVID-19 and are at risk of secondary mental health challenges related to the social isolation that has been imposed by virus containment strategies. To mitigate concerns regarding sampling bias, we analyzed a random sample of older adults to understand the uptake and acceptance of technologies that support socialization during the pandemic. OBJECTIVE: We aimed to conduct a population-based assessment of the barriers and facilitators to engaging in the use of technology for web-based socialization among older adults in the Canadian province of British Columbia during the COVID-19 pandemic. METHODS: We conducted a cross-sectional, population-based, regionally representative survey by using the random-digit dialing method to reach participants aged >65 years who live in British Columbia. Data were analyzed using SPSS (IBM Corporation), and open-text responses were analyzed via thematic analysis. RESULTS: Respondents included 400 older adults aged an average of 72 years, and 63.7% (n=255) of respondents were female. Most respondents (n=358, 89.5%) were aware of how to use technology to connect with others, and slightly more than half of the respondents (n=224, 56%) reported that, since the beginning of the pandemic, they used technology differently to connect with others during the pandemic. Additionally, 55.9% (n=223) of respondents reported that they adopted new technology since the beginning of the pandemic. Older adults reported the following key barriers to using technology: (1) a lack of access (including finance-, knowledge-, and age-related issues); (2) a lack of interest (including a preference for telephones and a general lack of interest in computers); and (3) physical barriers (resultant of cognitive impairments, stroke, and arthritis). Older adults also reported the following facilitators: (1) a knowledge of technologies (from self-teaching or external courses); (2) reliance on others (family, friends, and general internet searches); (3) technology accessibility (including appropriate environments, user-friendly technology, and clear instructions); and (4) social motivation (everyone else is doing it). CONCLUSIONS: Much data on older adults' use of technology are limited by sampling biases, but this study, which used a random sampling method, demonstrated that older adults used technology to mitigate social isolation during the pandemic. Web-based socialization is the most promising method for mitigating potential mental health effects that are related to virus containment strategies. Providing telephone training; creating task lists; and implementing the facilitators described by participants, such as facilitated socialization activities, are important strategies for addressing barriers, and these strategies can be implemented during and beyond the pandemic to bolster the mental health needs of 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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".