Relationship Transitions and Loneliness in Midlife and Older Adulthood
Bibliographic record
Abstract
Abstract Prior research suggests that being married or living with a partner is linked to a lower likelihood of loneliness, with some studies indicating a stronger effect for men. Moreover, because social networks and their perception change across the lifespan, the impact of relationship status on loneliness may differ between middle-aged and older men and women. We investigated how three major relationship events—separation, moving in with a partner, and marriage—affect loneliness and whether these effects vary by gender and age. We also examined instances in which individuals both moved in and married within the same timeframe. Using data from 4,768 U.S. participants in the Health and Retirement Study (HRS), we applied propensity score matching to compare two-year changes in loneliness likelihood between individuals experiencing a relationship event and matched controls whose relationship status remained stable. Results showed that moving in with a partner was associated with a reduced likelihood of feeling lonely, particularly among older men who also married their partner within the same period. In contrast, marriage among already cohabiting individuals and separation were not linked to significant changes in loneliness. These findings highlight the protective role of relationship involvement against loneliness in midlife and older adulthood and underscore the importance of considering the co-occurrence of relationship events.
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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 | 0.001 |
| 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".