Lonely Me, Lonely You: Loneliness and the Longitudinal Course of Relationship Satisfaction
Bibliographic record
Abstract
Abstract Individuals feel lonely when they perceive a discrepancy between the amount of closeness and intimacy in social relationships they desire and what they actually experience. Across several studies, partner relationships have consistently been found to be the most powerful protective factor against loneliness. Previous research on this topic, however, has exclusively focused on loneliness as a concomitant or outcome of low relationship quality, but not as a predictor in its own right, which is surprising given the trait-like features of loneliness. In the present study, we investigated the role of loneliness in predicting later levels and the development of relationship satisfaction over a period of 8 years in a heterogeneous sample of 2337 stable couples drawn from the German Family Panel. By applying Actor–Partner Interdependence Models and dyadic response surface analyses, we found that loneliness evinced substantial negative actor and partner effects on relationship satisfaction and its development over 8 years. Furthermore, we found that women were most satisfied with their relationships when both partners scored low on loneliness, whereas men were most satisfied when their own loneliness was low, irrespective of their partners’ loneliness. Congruently low levels of loneliness between women and men as well as declines in loneliness of at least one partner were additionally associated with increases in relationship satisfaction over time.
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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 | 0.006 |
| 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 | 0.001 |
| 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".