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Record W4411238806 · doi:10.1037/bul0000480

Trust and subjective well-being across the lifespan: A multilevel meta-analysis of cross-sectional and longitudinal associations.

2025· review· en· W4411238806 on OpenAlexaff
Shanshan Bi, Marlies Maes, Gonneke W. J. M. Stevens, Coriena de Heer, Jian‐Bin Li, Catrin Finkenauer

Bibliographic record

VenuePsychological Bulletin · 2025
Typereview
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsEducation and Early Childhood Development
Fundersnot available
KeywordsMultilevel modelMeta-analysisPsychologyCross-sectional studyClinical psychologyDemographyGerontologyStatisticsMedicineMathematicsSociology

Abstract

fetched live from OpenAlex

= 49). Together, these results suggest that trust and well-being mutually reinforce each other in a cyclical fashion. Since trust needs to be built, these findings underscore the importance of being genuinely trustworthy, whether as an individual or as an institution. (PsycInfo Database Record (c) 2025 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 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)0.014
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.014
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.026
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.017
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
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 head0.233
GPT teacher head0.516
Teacher spread0.283 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations14
Published2025
Admission routes1
Has abstractyes

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