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Record W4391614271 · doi:10.1177/01461672231225571

Personality and Well-Being Across and Within Relationship Status

2024· article· en· W4391614271 on OpenAlexafffund
Elaine Hoan, Geoff MacDonald

Bibliographic record

VenuePersonality and Social Psychology Bulletin · 2024
Typearticle
Languageen
FieldPsychology
TopicPersonality Traits and Psychology
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyFacet (psychology)ConscientiousnessNeuroticismExtraversion and introversionPersonalityBig Five personality traitsHierarchical structure of the Big FiveTraitSocial psychologyBig Five personality traits and cultureDevelopmental psychologyPsychological well-beingClinical psychology

Abstract

fetched live from OpenAlex

Trends of increasing singlehood call for understanding of well-being correlates across and within relationship status. While personality is a major predictor of well-being, descriptive trait profiles of singles have not been developed. In the present research ( N = 1,811; 53% men; M age = 29), single and partnered individuals completed measures of personality and well-being, including life, relationship status, and sexual satisfaction. Results revealed effects whereby single individuals were lower in extraversion and conscientiousness but higher in neuroticism. Additional facet analyses showed that singles were lower across all extraversion facets, but specifically lower in productiveness (conscientiousness facet) and higher in depression (neuroticism facet). Largely, personality was associated with well-being similarly for single and partnered people. Furthermore, relationship status accounted for variance in well-being above and beyond personality traits. Our results suggest individual differences in personality could play an important role in understanding well-being’s link with 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 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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.508
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.054
GPT teacher head0.388
Teacher spread0.334 · 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.

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

Citations26
Published2024
Admission routes2
Has abstractyes

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