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Record W2913468273 · doi:10.1080/17439760.2019.1579358

Positive emotions, hope, and life satisfaction in Chinese adults: a test of the broaden-and-build model in accounting for subjective well-being in Chinese college students

2019· article· en· W2913468273 on OpenAlexaff
Edward C. Chang, Olivia D. Chang, Mingqi Li, Zhen Xi, Yuwei Liu, Xitong Zhang, Xin Wang, Zimeng Li, Mingzhe Zhang, Xuan Zhang, Xinjie Chen

Bibliographic record

VenueThe Journal of Positive Psychology · 2019
Typearticle
Languageen
FieldPsychology
TopicOptimism, Hope, and Well-being
Canadian institutionsMcGill University
Fundersnot available
KeywordsPositive affectivityPsychologyMediationLife satisfactionNegative affectivityDispositionSocial psychologyAssociation (psychology)Test (biology)Subjective well-beingAgency (philosophy)Developmental psychologyPersonalityHappinessPsychotherapist

Abstract

fetched live from OpenAlex

The present study sought to determine if the positive association between positive emotions and life satisfaction can be understood as a function of hope in Chinese. Consistent with the broaden-and-build model of positive emotions, we tested the hypothesis that positive affectivity, the disposition to experience positive emotions, would be associated with broadening hope agency, building hope pathways, or both, in a sample of 212 Chinese college students. Results examining for bootstrapped mediation testing with multiple mediators indicated that positive affectivity was indirectly associated with life satisfaction through hope agency, but not through hope pathways. In support for partial mediation, however, the association between positive affectivity and life satisfaction remained significant even after including hope components in the model. Some implications of the present findings are discussed.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.005
GPT teacher head0.310
Teacher spread0.305 · 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

Citations78
Published2019
Admission routes1
Has abstractyes

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