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Record W4417122221 · doi:10.1037/tmb0000180

Links between adolescent time-use sequences and well-being.

2025· article· en· W4417122221 on OpenAlexfundno aff
Elizabeth W. Chan, Natalie S. T. Cheung, Jessie Y. S. Choy, Felix Cheung

Bibliographic record

VenueTechnology Mind and Behavior · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsnot available
FundersOntario Ministry of Research and InnovationSocial Sciences and Humanities Research Council of CanadaCanada Research ChairsUniversity College LondonCanada Foundation for Innovation
KeywordsCohortExploratory analysisExploratory researchAssociation (psychology)Cohort studyPeriod (music)

Abstract

fetched live from OpenAlex

Studies on time use and adolescent well-being typically focus on total time but rarely examine how an activity is embedded within a sequence of various activities. However, the potential importance of time-use sequences is apparent in pediatric recommendations like avoiding technology before bed. Using 24-hr time-use diaries from a representative birth cohort of 2,198 U.K. adolescents, we examined whether time-use sequences are prospectively associated with subjective well-being and 23 developmental outcomes. Preregistered analyses identified two weekday and four weekend sequences linked to different demographic and well-being correlates. For example, adolescents with high screen time throughout the day and late bedtimes experienced poorer outcomes than those whose late-night screen time followed diverse activities or active leisure. Yet, in exploratory analyses accounting for total time, only a few significant associations remained. This illustrates the advantage of examining total time-use and time-use sequences together to gain a more comprehensive understanding of adolescent well-being.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.156
Threshold uncertainty score0.946

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.016
GPT teacher head0.314
Teacher spread0.299 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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