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Record W4383872265 · doi:10.1177/01461672231180450

In it Together: Relationship Transitions and Couple Concordance in Health and Well-Being

2023· article· en· W4383872265 on OpenAlexafffund
Theresa Pauly, Elisa Weber, Christiane A. Hoppmann, Denis Gerstorf, Urte Scholz

Bibliographic record

VenuePersonality and Social Psychology Bulletin · 2023
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsUniversity of British ColumbiaSimon Fraser University
FundersUniversität ZürichCanada Research Chairs
KeywordsConcordancePsychologyWell-beingMental healthTransition (genetics)Longitudinal studyGermanSocial psychologyMedicineStatisticsMathematicsChemistryGeographyPsychiatry

Abstract

fetched live from OpenAlex

Events that change the family system have the potential to impact couple dynamics such as concordance, that is, partner similarity in health and well-being. This project analyzes longitudinal data (≥ two decades) from both partners of up to 3,501 German and 1,842 Australian couples to investigate how couple concordance in life satisfaction, self-rated health, mental health, and physical health might change with transitioning to parenthood and an empty nest. Results revealed couple concordance in intercepts (averaged r = .52), linear trajectories (averaged r = .55), and wave-specific fluctuations around trajectories (averaged r = .21). Concordance in linear trajectories was stronger after transitions (averaged r = .81) than before transitions (averaged r = .43), whereas no systematic transition-related change in concordance of wave-specific fluctuations was found. Findings emphasize that shared transitions represent windows of change capable of sending couples onto mutual upward or downward trajectories in health and well-being.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.047
GPT teacher head0.418
Teacher spread0.371 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2023
Admission routes2
Has abstractyes

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Same venuePersonality and Social Psychology BulletinSame topicAttachment and Relationship DynamicsFrench-language works237,207