Comparison between two Canadian Provinces on technology use for social interaction by older adults: comparative cross-sectional survey study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic prompted most people to embrace different approaches to their daily activities. Due to various measures to slow transmission in many jurisdictions, older adults were particularly impacted by measures restricting interactions. Our previous cross-sectional study identified barriers and facilitators for technology use for social interaction among older adults in British Columbia (BC), Canada. To investigate whether regional differences exist, the same survey from the previous study was conducted in Saskatchewan (SK), Canada during the same time. We also explored whether education and income levels were associated with older adults' social technology usage. METHODS: The cross-sectional survey was conducted through random-digit dialing to older adults who were 65 or older in BC and SK. Data were analyzed through the Statistical Package for the Social Sciences (SPSS) software (IBM corporation) and Microsoft Excel. Thematic analysis was performed on the survey's responses to open-ended questions. RESULTS: There were 806 participants, 400 from BC and 406 from SK. Education levels were associated with awareness of using technology for social interaction for both BC and SK while only SK had an association between new technology use and education levels. Similarly, income levels were also associated with awareness of technology use for social interaction for both provinces while only BC had an association between income levels to uptake of new technology. From the previous and current study, the barriers identified for technology use for social interactions in BC and SK were lack of interest, access (including financial issues) and physical limitations. SK participants identified perceived low self-efficacy as an additional barrier. For facilitators, BC and SK participants identified current and previous technology knowledge, help from others and motivation to keep social connections. Access to technology was unique to BC while better technology was unique to SK. CONCLUSIONS: Our study suggests that when older adults have access to resources to support their technology use, they will use them more, possibly enhancing their capacity for technology use. Future studies with more diverse populations around Canada may identify varying factors for older adults' technology use and regional variations in how technology is used for social interaction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".