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Record W3049693410 · doi:10.1037/fam0000793

Subjective well-being across partnerships.

2020· article· en· W3049693410 on OpenAlexfundno aff
Matthew D. Johnson, Franz J. Neyer, Christine Finn

Bibliographic record

VenueJournal of Family Psychology · 2020
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaDeutsche Forschungsgemeinschaft
KeywordsPsychologySocial psychologyDevelopmental psychology

Abstract

fetched live from OpenAlex

Drawing on data gathered from 554 focal participants in the German Family Panel (pairfam) study surveyed at 4 time points spanning 2 intimate unions, this brief report investigated changes in 3 indicators of subjective well-being (life satisfaction, depressive symptoms, and self-esteem) across partnerships. Latent change score-modeling results showed no mean-level changes in life satisfaction or self-esteem from Time 1 in Partnership 1 to Time 2 of Partnership 2 and a slight increase in depressive symptoms across partnerships. This overall stability in subjective well-being was evident despite a series of changes in the interim period: Subjective well-being worsened as the end of Partnership 1 approached, improved after the initiation of Partnership 2, and leveled off as Partnership 2 progressed. Being female predicted worse initial subjective well-being at the outset of the study, a greater decrease in well-being as participants approached the end of Partnership 1, and an overall reduction in self-esteem and increase in depression symptoms across partnerships. Being older, married in Partnership 1, and having a longer duration first partnership predicted worse initial well-being, a steeper decrease in life satisfaction as Partnership 1 drew to a close, and older participants had lower life satisfaction across unions. These findings add to a growing literature documenting the remarkable stability of individual and relational functioning across time while also highlighting those most at risk of reduced subjective well-being across partnerships. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.132
Threshold uncertainty score0.829

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.108
GPT teacher head0.462
Teacher spread0.354 · 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 teacher head, 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

Citations9
Published2020
Admission routes1
Has abstractyes

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