Life Events and Loneliness Transitions Among Middle-Aged and Older Adults Around the World
Bibliographic record
Abstract
Abstract Objectives Adult loneliness is a substantial social problem and a growing point of concern for policymakers around the world. We assess whether the predictors of loneliness onset among middle-aged and older adults vary from country to country in a large array of settings across world regions. Taking a life course perspective, we focus on common life events in our focal age range, including changes in partnership, coresidence, work, and health, and we test whether changes in them have comparable prospective associations with loneliness onset in different countries. Methods We draw on respondent-level data from a diversity of world regions surveyed in 7 harmonized cross-national studies in 20 countries, representing 47% of the global population over the age of 50. Our innovative longitudinal approach estimates prospective transition probability models that examine how each life event predicts the transition into loneliness. Results Despite substantial variation in the prevalence of loneliness and life events across the range of countries in our sample, our results highlight consistency in the predictors of loneliness transitions. Family and household changes like divorce, coresidence, and especially widowhood are paramount predictors of loneliness transition across settings, with changes in work and health playing more minor and less universal roles. Discussion The results demonstrate the importance that family and household connections play in determining loneliness at these ages. These findings suggest that addressing late-life loneliness may require a focus on key life events, especially those concerning changes in families and households.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".