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Given the powerful implications of relationship quality for health and well-being, a central mission of relationship science is explaining why some romantic relationships thrive more than others. This large-scale project used machine learning (i.e., Random Forests) to 1) quantify the extent to which relationship quality is predictable and 2) identify which constructs reliably predict relationship quality. Across 43 dyadic longitudinal datasets from 29 laboratories, the top relationship-specific predictors of relationship quality were perceived-partner commitment, appreciation, sexual satisfaction, perceived-partner satisfaction, and conflict. The top individual-difference predictors were life satisfaction, negative affect, depression, attachment avoidance, and attachment anxiety. Overall, relationship-specific variables predicted up to 45% of variance at baseline, and up to 18% of variance at the end of each study. Individual differences also performed well (21% and 12%, respectively). Actor-reported variables (i.e., own relationship-specific and individual-difference variables) predicted two to four times more variance than partner-reported variables (i.e., the partner's ratings on those variables). Importantly, individual differences and partner reports had no predictive effects beyond actor-reported relationship-specific variables alone. These findings imply that the sum of all individual differences and partner experiences exert their influence on relationship quality via a person's own relationship-specific experiences, and effects due to moderation by individual differences and moderation by partner-reports may be quite small. Finally, relationship-quality change (i.e., increases or decreases in relationship quality over the course of a study) was largely unpredictable from any combination of self-report variables. This collective effort should guide future models of relationships.

There is an increased risk for depression and anxiety across the perinatal period (i.e., spanning pregnancy and the first year postpartum); however, limited research has examined elevations in core negative affectivity underlying internalizing disorders more broadly. The current study sought to characterize trajectories of core internalizing problems among both mothers and fathers across the perinatal period and explored whether modifiable risk and protective factors buffered risk for elevated symptoms during this key developmental transition. A community sample of mixed‐sex couples (N = 159) completed assessments during pregnancy and at four postpartum timepoints. Using growth mixture modeling, we found that 21.2% of mothers demonstrated clinical elevations in core internalizing symptoms that persisted up to 2 years postpartum. In contrast, 7.8% of fathers demonstrated clinical elevations in core internalizing symptoms across this period, with an additional 29.0% of fathers demonstrating subthreshold symptom elevations. Concerns related to pregnancy and childbirth and paternal (partner) internalizing problems during pregnancy conferred risk for elevated symptoms in mothers, whereas psychological flexibility, emotional intimacy, and the quality of received support were identified as protective factors for fathers. Results highlight the importance of repeated screening for internalizing problems and suggest that promoting a strong interparental relationship is critical for emotional health and well‐being across the perinatal period. [ABSTRACT FROM AUTHOR]