122 research outputs found
When forgiving enhances psychological well-being: The role of interpersonal commitment
Contains fulltext :
63487.pdf (publisher's version ) (Closed access)The present research addresses the question of when and why forgiving might enhance psychological well-being. The authors predict that forgiving is associated with enhanced well-being but that this association should he more pronounced in relationships of strong rather than weak commitment. This hypothesis received good support in Studies 1-3. Studies 2 and 3 addressed the issue of why forgiving might be associated with psychological well-being, revealing that this association was reduced after controlling for psychological tension (i.e., a psychological state of discomfort due to conflicting cognitions and feelings). Study 4 revealed that in the context of marital relationships, tendencies toward forgiving one's spouse exhibited a more pronounced association with psychological well-being than did tendencies to forgive others in general.16 p
In situ redox reactions facilitate the assembly of a mixed-valence metal-organic nanocapsule
C-alkylpyrogallol[4]arenes (PgCs) have been studied for their ability to form metal-organic nanocapsules (MONCs) through coordination to appropriate metal ions. Here we present the synthesis and characterization of an MnII/MnIII-seamed MONC in addition to its electrochemical and magnetic behavior. This MONC assembles from 24 manganese ions and 6 PgCs, while an additional metal ion is located on the capsule interior, anchored through the introduction of bridging nitrite ions. The latter originate from an in situ redox reaction that occurs during the self-assembly process, thus representing a new route to otherwise unobtainable nanocapsules
Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies
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
Ru-Catalyzed (E)Specific ortho-CH Alkenylation of Arenecarboxylic Acids by Coupling with Alkenyl Bromides
- …