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Record W4382775942 · doi:10.1177/20551029231184034

Are social support, loneliness, and social connection differentially associated with happiness across levels of introversion-extraversion?

2023· article· en· W4382775942 on OpenAlexafffundabout
Kiffer G. Card, Shayna Skakoon‐Sparling

Bibliographic record

VenueHealth Psychology Open · 2023
Typearticle
Languageen
FieldPsychology
TopicPsychological Well-being and Life Satisfaction
Canadian institutionsToronto Metropolitan UniversitySimon Fraser University
FundersCanadian Institutes of Health ResearchMichael Smith Health Research BC
KeywordsLonelinessExtraversion and introversionPsychologyHappinessSocial connectednessSocial supportPsychological interventionAssociation (psychology)Social psychologyMultilevel modelClinical psychologyDevelopmental psychologyBig Five personality traitsPersonalityPsychiatry

Abstract

fetched live from OpenAlex

This study examines whether extraversion moderates the association between subjective happiness and measures of social connectedness using data from Canadian residents, aged 16+, recruited online during the third wave of the COVID-19 pandemic (21 April 2021–1 June 2021). To accomplish this aim we tested the moderating effect of extraversion scores on the association between Subjective Happiness scores and several social health measures: Perceived Social Support, Loneliness, social network size, and time with friends. Among 949 participants, results show that lower social loneliness ( p < .001) and higher social support from friends ( p = .001) and from family ( p = .007) was more strongly correlated with subjective happiness for people with low extraversion compared to those with high extroversion. Anti-loneliness interventions should consider the need to promote social connections among individuals across the introversion-extraversion continuum.

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 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 score0.779
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.129
GPT teacher head0.451
Teacher spread0.322 · 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

Citations17
Published2023
Admission routes3
Has abstractyes

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