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Record W4399516469 · doi:10.1111/jora.12990

Latinx adolescents' daily family assistance and emotional well‐being before and amid the <scp>COVID</scp>‐19 pandemic: A pilot measurement burst study

2024· article· en· W4399516469 on OpenAlexaff
Yishan Shen, Yao Zheng, Ari Rios Garza, Samantha Reisz

Bibliographic record

VenueJournal of Research on Adolescence · 2024
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsUniversity of Alberta
FundersSociety for Research in Child Development
KeywordsCoronavirus disease 2019 (COVID-19)PandemicPsychologyOutbreakAffect (linguistics)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Developmental psychologyGerontologyClinical psychologyMedicineVirologyCommunicationDisease

Abstract

fetched live from OpenAlex

Abstract This study examined Latinx adolescents' daily family assistance (assistance day, assistance time, language brokering) in relation to their daily affect and investigated whether the associations changed following the outbreak of the COVID‐19 pandemic. Two waves of 14‐day daily diary data collected from 13 18‐year‐old Latinx adolescents ( n days = 284; 77% Mexican American, 77% female) before and amid the pandemic were analyzed using multilevel modeling. Three main findings emerged: (1) assisting the family on a given day was associated with higher levels of same‐day positive affect both before and during COVID‐19, and with lower levels of negative affect during COVID‐19; (2) longer than usual family assistance time was associated with higher levels of same‐day positive affect and lower levels of negative affect only during COVID‐19; (3) language brokering on a given day was associated with higher levels of same‐day positive affect both before and during COVID‐19. These findings suggest a positive link between daily family assistance and Latinx youth's daily emotional well‐being, particularly during the COVID‐19 pandemic.

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)0.009
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 score0.016
Threshold uncertainty score0.892

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.213
GPT teacher head0.470
Teacher spread0.257 · 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

Citations7
Published2024
Admission routes1
Has abstractyes

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