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Record W4412487172 · doi:10.1111/cch.70136

Screen Time Among and Youth Children With Disabilities: A Systematic Review and Meta‐Analysis

2025· review· en· W4412487172 on OpenAlexafffund
Leigh M. Vanderloo, Matthew Bourke, Leah G. Taylor, Sophie M. Phillips, Aidan Loh, Katerina Disimino, Rebecca Bassett‐Gunter, Tyler Koo, Molly Thompson‐Hill, Patricia Tucker

Bibliographic record

VenueChild Care Health and Development · 2025
Typereview
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsChildren’s Health Research InstituteOntario Centre of Excellence for Child and Youth Mental HealthLondon Health Sciences CentreYork UniversityWestern University
FundersSocial Sciences and Humanities Research Council of CanadaMitacsChildren's Health Research Institute
KeywordsScreen timeMeta-analysisPsychological interventionLimitingPopulationPsychologySystematic reviewMedicineMEDLINEClinical psychologyPsychiatryPhysical therapyEnvironmental healthPhysical activity

Abstract

fetched live from OpenAlex

OBJECTIVE: This paper aimed to review and synthesize screen time research among children and youth with disabilities. METHODS: Seven online databases were searched, and a machine learning-assisted systematic review model was used to identify relevant studies. English and French papers reporting on screen time among children and youth with a disability were eligible. Extracted data were synthesized by participant age, followed by type of screen time reporting. Meta-analyses were conducted to estimate daily screen time and adherence to screen time guidelines using random effects meta-analysis. RESULTS: Eighty-one studies were included. Screen time ranged from 0.5 to 7.27 h/day and varied widely based on disability type. Pooled average screen time was 3.70, 3.28 and 3.39 h/day for children and youth with ASD, ADHD and CP, respectively. CONCLUSION: Screen use is prominent among children and youth with disabilities. Limiting screen time in this group is critical in preventing numerous related consequences of excessive, prolonged use. POLICY IMPLICATIONS: Interventions targeting children and youth with disabilities are needed to decrease excessive screen time among this population and to inform future public health policy and setting-specific practice.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.564
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.039
GPT teacher head0.326
Teacher spread0.287 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations6
Published2025
Admission routes2
Has abstractyes

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