Do Friends Help or Hurt? The Dyadic Effects of Friendships on Perceived Partner Relationship Maintenance and Relationship Satisfaction
Bibliographic record
Abstract
While romantic relationships are often examined in isolation, the emotional and tangible resources that friendships provide may influence couples. This study explores the impact that friendship networks have on romantic relationships by taking a dyadic approach to examine the association between the quality and size of couples’ respective friendship networks, perceived partner relationship maintenance, and relationship satisfaction. It was hypothesized that higher quality and quantity friendships would attenuate the effects of perceived partner relationship maintenance behaviors on relationship satisfaction, following a couple-oriented pattern. Samples of 153 heterosexual couples and 150 queer couples in the United States and Canada were recruited. Actor-partner interdependence models with mixed moderators were analyzed through multilevel modelling. Results revealed that friendship quality and quantity had moderating effects on the association between perceived partner maintenance behaviors and relationship satisfaction in most circumstances, although the patterns of these moderating effects varied. Friendship quality was not a significant moderator for heterosexual couples. However, friendship quality had significant moderating effects for queer couples, suggesting that when perceived maintenance is low, an individual’s higher quality friends can buffer and promote relationship satisfaction. Similarly, mixed interaction patterns between friendship quantity relationship maintenance were observed for both couple types. These findings suggest that couples may benefit from being mindful of how they use and interact with their friendships to maintain or augment relationship satisfaction.
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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 | 0.000 | 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 | 0.001 |
| 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".