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Record W4410102242 · doi:10.1177/00938548251336784

Comparing Justice-Involved and Community Adolescents’ Emotional Well-Being and Feelings of Social Connection During COVID-19: A Daily Diary Study of Adolescents’ Social Contact

2025· article· en· W4410102242 on OpenAlexaff
April Gile Thomas, Adam K. Fetterman, Anna D. Ziencina, Natasha Chlebuch, Nicholas D. Evans, Caitlin Cavanagh

Bibliographic record

VenueCriminal Justice and Behavior · 2025
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsUniversity of Manitoba
FundersUniversity of Texas at El PasoAmerican Psychological FoundationNational Science Foundation
KeywordsFeelingCoronavirus disease 2019 (COVID-19)PsychologySocial contactSocial psychologyConnection (principal bundle)2019-20 coronavirus outbreakHuman factors and ergonomicsPoison controlSuicide preventionBody contactInjury preventionDevelopmental psychologyApplied psychologyMedicineMedical emergencyEngineering

Abstract

fetched live from OpenAlex

This study utilizes 6 weeks of electronic daily diary self-assessments across more than 1 year of the COVID-19 pandemic to examine associations between adolescents’ phone and video social contact with friends, family, and others over time. The sample includes justice-involved juveniles adjudicated to probation or incarceration and a comparison community sample of never-arrested adolescents. Findings reveal that community youth did not demonstrate positive emotional gains from social contact via phone or video during the pandemic and at times experienced more negative emotion on days with virtual social contact; however, such contact was especially beneficial for justice-involved youth, who had better self-conceptions and stronger feelings of social connection (although more loneliness as well) on days when they engaged in phone or video contact with friends or family. Thus, social contact via phone or video may serve to close the gap in emotional well-being between justice-involved and community adolescents.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science 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 score0.168
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.095
GPT teacher head0.407
Teacher spread0.312 · 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
Published2025
Admission routes1
Has abstractyes

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