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Record W4393159221 · doi:10.1080/02699931.2024.2333920

Within- and between-person associations between social interactions and loneliness: students’ experiences during the COVID-19 pandemic

2024· article· en· W4393159221 on OpenAlexafffund
Alyssa K. Truong, Gizem Keskin, Jessica P. Lougheed

Bibliographic record

VenueCognition & Emotion · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersMichael Smith Health Research BCCanada Foundation for Innovation
KeywordsLonelinessPsychologyCoronavirus disease 2019 (COVID-19)PandemicSocial isolationSocial psychologySocial relationshipSocial relationDevelopmental psychologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

The COVID-19 pandemic introduced many restrictions to in-person interactions, and remote social interactions may be especially important for managing loneliness when such restrictions are in place. However, it is unclear how social interactions are related to loneliness when in-person interactions are limited. Data were collected between February 2021 and March 2022 from a sample of 581 university students. Participants reported their loneliness and participation in positive in-person or remote social interactions each day for 14 days. Results from dynamic structural equation models showed that participants felt less lonely than they usually felt on the days they engaged in positive remote interactions at the within-person level. Moreover, participants generally felt less lonely when engaging more frequently in remote interactions, but only when in-person interactions were restricted (between-person level). Some of these results varied by changing COVID-19 restrictions. Finally, for participants who felt lonelier in general, the effect of positive in-person and remote interactions on loneliness was less strong. These findings suggest that social interactions may buffer loneliness but are not as impactful for those who experience greater loneliness.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
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 head0.143
GPT teacher head0.434
Teacher spread0.291 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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