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Screen Use and Mental Health Symptoms in Canadian Children and Youth During the COVID-19 Pandemic

2021· article· en· W4200339142 on OpenAlexafffundabout
Xuedi Li, Leigh M. Vanderloo, Charles Keown‐Stoneman, Katherine Tombeau Cost, Alice Charach, Jonathon L. Maguire, Suneeta Monga, Jennifer Crosbie, Christie L. Burton, Evdokia Anagnostou, Stelios Georgiades, Rob Nicolson, Elizabeth Kelley, Muhammad Ayub, Daphne J. Korczak, Catherine S. Birken

Bibliographic record

VenueJAMA Network Open · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsQueen's UniversityMcMaster UniversityHolland Bloorview Kids Rehabilitation HospitalPublic Health OntarioHospital for Sick ChildrenSt. Michael's HospitalWestern UniversityUniversity of TorontoSickKids FoundationInstitute for Clinical Evaluative Sciences
FundersCanadian Institutes of Health ResearchHospital for Sick ChildrenUniversity of TorontoCentre for Addiction and Mental HealthOntario Brain Institute
KeywordsScreen timeIrritabilityMental healthAnxietyPsychological interventionDepression (economics)Video gameLongitudinal studyPsychologyClinical psychologyMedicinePsychiatryMultimediaPhysical therapy

Abstract

fetched live from OpenAlex

Importance: Longitudinal research on specific forms of electronic screen use and mental health symptoms in children and youth during COVID-19 is minimal. Understanding the association may help develop policies and interventions targeting specific screen activities to promote healthful screen use and mental health in children and youth. Objective: To determine whether specific forms of screen use (television [TV] or digital media, video games, electronic learning, and video-chatting time) were associated with symptoms of depression, anxiety, conduct problems, irritability, hyperactivity, and inattention in children and youth during COVID-19. Design, Setting, and Participants: A longitudinal cohort study with repeated measures of exposures and outcomes was conducted in children and youth aged 2 to 18 years in Ontario, Canada, between May 2020 and April 2021 across 4 cohorts of children or youth: 2 community cohorts and 2 clinically referred cohorts. Parents were asked to complete repeated questionnaires about their children's health behaviors and mental health symptoms during COVID-19. Main Outcomes and Measures: The exposure variables were children's daily TV or digital media time, video game time, electronic-learning time, and video-chatting time. The mental health outcomes were parent-reported symptoms of child depression, anxiety, conduct problems and irritability, and hyperactivity/inattention using validated standardized tools. Results: This study included 2026 children with 6648 observations. In younger children (mean [SD] age, 5.9 [2.5] years; 275 male participants [51.7%]), higher TV or digital media time was associated with higher levels of conduct problems (age 2-4 years: β, 0.22 [95% CI, 0.10-0.35]; P < .001; age ≥4 years: β, 0.07 [95% CI, 0.02-0.11]; P = .007) and hyperactivity/inattention (β, 0.07 [95% CI, 0.006-0.14]; P = .04). In older children and youth (mean [SD] age, 11.3 [3.3] years; 844 male participants [56.5%]), higher levels of TV or digital media time were associated with higher levels of depression, anxiety, and inattention; higher levels of video game time were associated with higher levels of depression, irritability, inattention, and hyperactivity. Higher levels of electronic learning time were associated with higher levels of depression and anxiety. Conclusions and Relevance: In this cohort study, higher levels of screen use were associated poor mental health of children and youth during the COVID-19 pandemic. These findings suggest that policy intervention as well as evidence-informed social supports are needed to promote healthful screen use and mental health in children and youth during the pandemic and beyond.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.031
Threshold uncertainty score0.226

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.036
GPT teacher head0.302
Teacher spread0.266 · 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 source (direct Gemma or distilled Codex), 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

Citations135
Published2021
Admission routes3
Has abstractyes

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