Intergenerational connections through technology: Insights from the Technology Use in Later Life multi-site study
Bibliographic record
Abstract
Abstract With enhanced challenges to maintain social connections especially during times of social distancing due to the COVID-19 pandemic, the need for technology solutions grow. Technologies have become interwoven into the daily lives for many older adults. The Technology Use in Later Life (TILL) study investigated how the perceptions and use of technology both can foster new and leverage existing intergenerational relationships. Through a mixed methods study engaging older adults aged 70 years of age and greater across rural and urban sites in Canada and the UK (N=37), participants described how the interconnection between technology and intergenerational relationships was an integral component to social connectedness with others. Through a qualitative descriptive approach, it was noted that older adults leveraged intergenerational relationships with family and friends to adjust to new technologies and to remain connected to adult children and grandchildren especially when there is high geographic separation between them. Especially during times of COVID-19, younger family members can play an important role to introduce and teach older adults how to use, technologies such as digital devices, computers, and social networking sites. Participants emphasized the benefits of intergenerational connections to adopt and use technology in later life noting flexibility and willingness to overcome barriers to technology adoption and remain connected across the generations. The adoption and uptake of technologies may continue as viable options during times of social distancing to support older persons to remain independent, age in place, in both age-friendly cities and across rural geographies during and post COVID-19.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".