Connected seniors: how older adults in East York exchange social support online and offline
Bibliographic record
Abstract
How do older adults mobilize social support, with and without digital media? To investigate this, we focus on older adults 65+ residing in the Toronto locality of East York, using 42 interviews lasting about 90 minutes done in 2013–2014. We find that digital media help in mobilizing social support as well as maintaining and strengthening existing relationships with geographically near and distant contacts. This is especially important for those individuals (and their network members) who have limited mobility. Once older adults start using digital media, they become routinely incorporated into their lives, used in conjunction with the telephone to maintain existing relationships but not to develop new ones. Contradicting fears that digital media are inadequate for meaningful relational contact, we found that these older adults considered social support exchanged via digital media to be real support that cannot be dismissed as token. Older adults especially used and valued digital media for companionship. They also used them for coordination, maintaining ties, and casual conversations. Email was used more with friends than relatives; some Skype was used with close family ties. Our research suggests that policy efforts need to emphasize the strengthening of existing networks rather than the establishment of interventions that are outside of older adults’ existing ties. Our findings also show that learning how to master technology is in itself a form of social support that provides opportunities to strengthen the networks 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 | 0.001 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".