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Record W4414260641 · doi:10.1177/19485506251371351

In What Domains Does Entering a Romantic Relationship Boost Well-Being? A Longitudinal Investigation

2025· article· en· W4414260641 on OpenAlexaff
Helena Yuchen Qin, Elaine Hoan, Samantha Joel, Geoff MacDonald

Bibliographic record

VenueSocial Psychological and Personality Science · 2025
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsUniversity of TorontoWestern University
Fundersnot available
KeywordsRomanceLife satisfactionGeneral partnershipLongitudinal dataSexual relationshipLongitudinal studyPartner effects

Abstract

fetched live from OpenAlex

Research shows higher well-being for partnered versus single individuals, but existing studies assess limited outcomes and do not use fully appropriate analytic methods. We assessed single people’s well-being at two timepoints 6 months apart ( N = 3,165) and assessed who did and did not remain single. There were small selection effects such that singles higher in life satisfaction and sexual satisfaction were more likely to partner. Stable partnership (as opposed to staying single or partnering then breaking up) was related to relatively small increases in life satisfaction, moderate decreases in loneliness, and larger increases in sexual satisfaction and satisfaction with relationship status. On some variables, effects were larger for men and for those with stronger initial desire for a partner. These data suggest that, for singles sufficiently motivated to partner, partnering may cause increases in well-being, especially for well-being domains like sexual satisfaction that are particularly tied to relationship status.

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)0.008
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 score0.003
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.430
Teacher spread0.383 · 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

Citations7
Published2025
Admission routes1
Has abstractyes

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