Contributors to age inequalities in loneliness among older adults: a decomposition analysis of 29 countries
Bibliographic record
Abstract
OBJECTIVES: Loneliness is highly prevalent and can have severe health consequences. While generally assumed to increase with age, some evidence suggests the relationship between age and loneliness may vary across country. In this study, we investigate the contribution of demographic and health factors to age-related inequalities in loneliness both within and across countries. METHOD: We used population-based cross-sectional data from 64,324 older adults (age range: 50-90 years) across 29 countries. Loneliness was measured with the 3 item UCLA loneliness scale. We quantified the magnitude of age inequalities in loneliness using concentration indices, and we estimated the contribution of demographic and health factors to age inequalities in loneliness using a decomposition approach. RESULTS: Loneliness was generally more concentrated among the oldest adults in the sample, although in the US and the Netherlands it was more concentrated among younger adults. Top contributors to age inequalities in loneliness were being unmarried and not working; however, the amount that factors contributed to inequalities differed markedly by country. CONCLUSION: Age inequalities in loneliness, and contributors to these inequalities, vary substantially across countries, suggesting that loneliness is not an inevitable consequence of age but may instead be shaped by environments within countries (e.g. social cohesion).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".