THE GEOGRAPHIC LAYOUTS OF OLDER EUROPEANS’ SOCIAL NETWORKS AND LONELINESS IN THE COVID-19 PANDEMIC
Bibliographic record
Abstract
Abstract Many older Europeans sustain core personal networks that are geographically dispersed. The pandemic has brought concerns about their mental health, especially during lockdown periods when travel was not permissible and casual local contact was limited. This study examines whether and how older adults’ loneliness and depressed feelings vary by the prior geographic layouts of their core discussion networks. It uses a sample of community-dwelling respondents aged 50 and above (Wave 6, in 2015) from the Survey of Health, Aging, and Retirement in Europe (SHARE), with a follow-up at Wave 8 in 2020 during the height of the COVID pandemic. Latent Class Analysis and linear regression show that individuals whose networks were comprised mainly of families 5-25km and >25km away were not especially likely to feel lonely or distressed, despite typically lacking nearby confidants. We also uncover groups of people occupying more compositionally diverse networks. While these individuals were generally more likely to perceive loneliness, they tended not to attribute such feelings to the pandemic onset. The exception was individuals sustaining diverse networks comprised mainly of friends and families at intermediate distances (5-25km), who perceived heightened loneliness in the pandemic. Overall, even scattered at longer distances, family-oriented networks demonstrate good protectiveness against loneliness and depression. Meanwhile, diverse networks appeared to fall short in protectiveness under some conditions of geographic dispersion.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".