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Record W4416578117 · doi:10.1037/emo0001618

Reciprocal longitudinal associations of loneliness and perceived stress: A random intercept cross-lagged panel model.

2025· article· en· W4416578117 on OpenAlexafffund
Bronwen Grocott, Talia Morstead, Nancy L. Sin, Anita DeLongis, Joelle LeMoult

Bibliographic record

VenueEmotion · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of CanadaMichael Smith Health Research BC
KeywordsLonelinessAssociation (psychology)Stress (linguistics)ReciprocalMental healthLongitudinal studyLongitudinal data

Abstract

fetched live from OpenAlex

= 48.59, 84% women) completed measures of loneliness and perceived stress between June and September 2021. Data were analyzed using a random intercept cross-lagged panel model, which separately estimated between- and within-person effects over time. Supporting hypotheses, increases in loneliness and perceived stress, relative to expected scores, were each prospectively associated with respective increases in perceived stress and loneliness at the within-person level. In other words, loneliness and perceived stress were reciprocally related over time, even when accounting for their strong, traitlike association. Findings have key implications for methodological approaches to the study of loneliness and stress and inform mechanistic understandings of this association. (PsycInfo Database Record (c) 2026 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)0.005
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 score0.038
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0100.002

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 head0.080
GPT teacher head0.398
Teacher spread0.318 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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