A new model is proposed to explain how automatic partner attitudes affect how couples cope with major life transitions. The automatic partner attitudes in transition (APAT) model assumes that people simultaneously possess contextualized automatic attitudes toward their partner that can differ substantively in valence pre- and posttransition. It further assumes that evaluatively pre- and posttransition automatic partner attitudes elicit heightened behavioral angst or uncertainty, self-protective behavior in response to risk, and relationship distress. A longitudinal study of the transition to first parenthood supported the model. People with evaluatively inconsistent automatic partner attitudes, whether more and , or more and , exhibited heightened evidence of cardiovascular threat discussing conflicts, increased self-protective behavior in response to parenting-related transgressions in daily interaction, and steeper declines in relationship well-being in the year following the transition to parenthood. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Download full-text PDF

Source
http://dx.doi.org/10.1037/pspi0000143DOI Listing

Publication Analysis

Top Keywords

automatic partner
20
partner attitudes
20
transition parenthood
12
attitudes transition
8
pre- posttransition
8
self-protective behavior
8
behavior response
8
automatic
6
partner
6
attitudes
6

Similar Publications

Post-translational modifications (PTMs) play pivotal roles in regulating cellular signaling, fine-tuning protein function, and orchestrating complex biological processes. Despite their importance, the lack of comprehensive tools for studying PTMs from a pathway-centric perspective has limited our ability to understand how PTMs modulate cellular pathways on a molecular level. Here, we present PTMNavigator, a tool integrated into the ProteomicsDB platform that offers an interactive interface for researchers to overlay experimental PTM data with pathway diagrams.

View Article and Find Full Text PDF

Second-language speakers are more likely to strategically reuse the words of their conversation partners (Zhang & Nicol, 2022). This study investigates if this is also the case for lower-proficiency bilinguals from a bilingual community, who use language more implicitly, and if there is more alignment with lower than with higher proficiency, provided the words to be aligned to are all highly familiar. In two experiments, Spanish-English bilinguals took turns with a confederate to name and match pictures in Spanish.

View Article and Find Full Text PDF

Completeness of repeated patient-reported outcome measures in adult rehabilitation: a randomized controlled trial in a diverse clinical population.

BMC Health Serv Res

December 2024

Health Services Research and Innovation Unit, and Center for Treatment of Rheumatic and Musculoskeletal Diseases (REMEDY), Diakonhjemmet Hospital, Oslo, Norway.

Background: Data collection through patient-reported outcome measures (PROMs) is essential for the purpose of rehabilitation research and registries. Existing problems with incomplete PROM data may relate to the patient burden and data set length. This study aimed to analyse response patterns and degree of data completeness in systematic outcome assessments conducted within a clinical study in a multidisciplinary rehabilitation setting, comparing completeness of a brief and a longer set of PROMs.

View Article and Find Full Text PDF

In contemporary healthcare, effective risk stratification in the general population is vital amidst rising chronic disease rates and an ageing demographic. Deceleration Capacity of the heart rate (DC), derived from 24-hour Holter electrocardiograms, holds promise in risk stratification for cardiac patients. However, the potential of short-term electrocardiograms of five minutes duration for population screening has not been fully explored.

View Article and Find Full Text PDF

The integration of machine learning into the domain of radiomics has revolutionized the approach to personalized medicine, particularly in oncology. Our research presents RadTA (RADiomics Trend Analysis), a novel framework developed to facilitate the automatic analysis of quantitative imaging biomarkers (QIBs) from time-series CT volumes. RadTA is designed to bridge a technical gap for medical experts and enable sophisticated radiomic analyses without deep learning expertise.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!