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Record W4409218782 · doi:10.1002/casp.70080

Household Ecology and Recovery Among Young Adults: Digital Device Use as a Mixed Advantage

2025· article· en· W4409218782 on OpenAlexfundno aff
Xian Zhao, Shir Yi Toh

Bibliographic record

VenueJournal of Community & Applied Social Psychology · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Toronto MississaugaUniversity of Toronto
KeywordsEcologyGeographyBiology

Abstract

fetched live from OpenAlex

ABSTRACT People are increasingly spending time on digital devices, but contradictory evidence exists regarding the effect of screen time on well‐being. Instead of focusing on the focal effect of screen time, we propose that time spent on digital devices may interact with other factors in predicting resource recovery outcomes—the time on digital devices itself could be a distraction that separates people from their family and household roles and obligations and thus may attenuate or amplify the negative relationship between hostile household ecology and resource recovery. In one archival study based on the American Time Use Survey (ATUS) and two daily diary studies of young adults, we found a consistent pattern that the effect of hostile household ecology (e.g., large family size and high levels of home chaos) on recovery‐related outcomes was smaller when screen time was higher, depending on the digital device involved. This finding points to a new perspective on screen time for recovering from household demands and can shed light on the way people cope with a variety of stressors and working from home. Please refer to the Supporting Information section to find this article's .

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)0.002
metaresearch head score (Gemma)0.001
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 score0.244
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.002
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 head0.026
GPT teacher head0.331
Teacher spread0.305 · 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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