Internet use, social isolation and loneliness in older adults
Bibliographic record
Abstract
Abstract The aim of this study was to explore associations between internet/email use in a large sample of older English adults with their social isolation and loneliness. Data from the English Longitudinal Study of Ageing Wave 8 were used, with complete data available for 4,492 men and women aged ⩾ 50 years (mean age = 64.3, standard deviation = 13.3; 51.7% males). Binomial logistic regression was used to analyse cross-sectional associations between internet/email use and social isolation and loneliness. The majority of older adults reported using the internet/email every day (69.3%), fewer participants reported once a week (8.5%), once a month (2.6%), once every three months (0.7%), less than every three months (1.5%) and never (17.4%). No significant associations were found between internet/email use and loneliness, however, non-linear associations were found for social isolation. Older adults using the internet/email either once a week (odds ratio (OR) = 0.60, 95% confidence interval (CI) = 0.49–0.72) or once a month (OR = 0.60, 95% CI = 0.45–0.80) were significantly less likely to be socially isolated than every day users; those using internet/email less than once every three months were significantly more likely to be socially isolated than every day users (OR = 2.87, 95% CI = 1.28–6.40). Once every three months and never users showed no difference in social isolation compared with every day users. Weak associations were found between different online activities and loneliness, and strong associations were found with social isolation. The study updated knowledge of older adults’ internet/email habits, devices used and activities engaged in online. Findings may be important for the design of digital behaviour change interventions in older adults, particularly in groups at risk of or interventions targeting loneliness and/or social isolation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".