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Record W4413449898 · doi:10.1111/jopy.70015

Heterogeneity of Effects in a Prosociality‐Based Intervention to Reduce Loneliness and Increase Social Contact

2025· article· en· W4413449898 on OpenAlexafffund
Yeeun Lee, Gu Li, Julia S. Nakamura, Yingchi Guo, Frances S. Chen

Bibliographic record

VenueJournal of Personality · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsTrinity Western UniversityUniversity of British ColumbiaWestern University
FundersCanadian Institutes of Health ResearchSocial Sciences and Humanities Research Council of CanadaUniversity of British ColumbiaUniversity of TorontoMichael Smith Health Research BC
KeywordsLonelinessPsychologySocial contactIntervention (counseling)Prosocial behaviorSocial psychologyClinical psychologyDevelopmental psychologyPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: This study evaluates the effects of an act of kindness intervention on increasing daily social contact and reducing loneliness among community adults experiencing loneliness. It also explores heterogeneity in effects and potential moderators, including individual differences in baseline social health and intervention implementation. METHOD: In a randomized controlled trial, 208 adults were randomly assigned to perform daily acts of kindness for others (Kindness condition) or take a short break for themselves (Control condition) for 2 weeks. Dairy assessments of loneliness and social contact were collected 3 days before and after the intervention. RESULTS: We did not find consistent average effects. Although loneliness decreased in the Kindness condition, the reduction was not significantly greater than in the Control condition. In contrast, a group difference was observed in social contact, which remained stable in the Kindness condition but declined in the Control condition. Notably, significant individual differences emerged: the intervention was more effective for participants with higher baseline social anxiety and loneliness, and when a greater proportion of prosocial acts targeted weak social ties and a smaller proportion targeted strangers. CONCLUSIONS: These findings highlight the importance of identifying individual differences-for whom and under what conditions prosociality-based interventions are most effective.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.030
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.035
GPT teacher head0.417
Teacher spread0.382 · 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.

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

Citations1
Published2025
Admission routes2
Has abstractyes

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