Making sense of older adults’ everyday smartphone use for social connectedness
Bibliographic record
Abstract
One main purpose of smartphone use is to be socially connected, and in this sense, older people’s use of this device is no different from that of any other group. Smartphone use among this group is increasingly relevant given the growing number of older adults who lack meaningful social connections. Though the smartphone is permeating older adults’ everyday lives more and more, little is known about how use of this device shapes their sense of social connectedness in everyday life. To fill this gap, we analysed the relationship between smartphone use and perceived social connectedness in Canada, the Netherlands, Spain and Sweden, focusing on the following research question: What is the role of the smartphone in social connectedness in later life? We used a multi-method approach, tracking smartphone use and conducting an online survey with participants aged 55 to 79. We used the notion of social connectedness, involving three dimensions: Community Connections, Social Engagement and Personal Relationships. We found that reported smartphone use is a better predictor of social connectedness than tracked smartphone use. Also, using the smartphone for co-caring purposes intensified feelings of overall social connectedness, while sharing multimedia content enhanced the social connectedness dimensions of Community Connections and Social Engagement. We conclude with limitations and implications for future research. In sum, the obtained results help in overcoming stereotypical and ageist assumptions about older adults’ digital practices by providing rich, nuanced evidence on the meanings of smartphone-based communication in a sample aged 55 to 79.
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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 | 0.003 | 0.007 |
| 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 | 0.002 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".