Loneliness Trajectories and Chronic Loneliness Around the World
Bibliographic record
Abstract
OBJECTIVES: We examine cross-national variation in (a) loneliness trajectories and (b) the association between common social risk factors and chronic loneliness in middle and older adulthood. METHODS: Using longitudinal data, we assess the country-level prevalence of loneliness trajectories (chronic, transitory, and no loneliness) and the extent of common social risk factors for loneliness (living alone, widowhood, divorce, no grandchildren, having chronic conditions, and never working) among adults 50 and older in 20 countries covering 47% of the global population in this age bracket. Additionally, we compare how the associations between social risk factors and chronic loneliness vary across countries. RESULTS: We find considerable variation in the prevalence of chronic loneliness cross-nationally, ranging between 4% (Denmark) and 15% (Greece) of adults 50 and older. Living arrangements have the most consistent association with the likelihood of chronic loneliness across countries, with those ever living alone having an 8% higher likelihood of chronic loneliness on average across countries, with a range of 2%-25%. Additionally, those who never report working and those with chronic conditions have a higher likelihood of chronic loneliness across more than a third of the countries. DISCUSSION: These results suggest that policies and interventions targeted to middle age and older adults living alone and with limited work histories or with chronic conditions are critical in reducing the public health challenges of chronic loneliness.
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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".