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Record W4223568847 · doi:10.47326/ocsat.2022.03.59.1.0

Increased Screen Time for Children and Youth During the COVID-19 Pandemic

2022· report· en· W4223568847 on OpenAlexaboutno aff
Elaine Toombs, Christopher J. Mushquash, Linda Mah, Kathy G. Short, Nancy L. Young, Chiachen Cheng, Lynn Zhu, Gillian Strudwick, Catherine S. Birken, Jessica Hopkins, Daphne J. Korczak, Anna Perkhun, Karen Born

Bibliographic record

Venuenot available
Typereport
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsnot available
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)PandemicScreen time2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)VirologyPsychologyMedicineOutbreakInternal medicineInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Screen time has substantially increased for children and youth in Ontario and globally during the COVID-19 pandemic. Emergency measures introduced during the pandemic such as closures of schools and recreation contributed to increased screen time. There is a growing body of evidence associating increased screen time with harms to physical (e.g., decreased physical activity, eye strain and headaches), cognitive (e.g., attentiveness) and mental (e.g., reported symptoms of depression and anxiety) health in children and youth. There are evidence-based strategies to promote healthy screen habits for children and their families which offer an approach to encourage healthier screen use in the home setting and mitigate potential harms. However, the burden to reduce screen time cannot fall to parents and families alone. Policies are needed to avoid closures of school and recreation, and ensure alternatives to screen time for children and youth of all ages that promote socialization and physical activity. In addition, there are key equity considerations when it comes to accessibility of alternatives to screen time such as child care and community recreation.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.137
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.047
GPT teacher head0.344
Teacher spread0.297 · 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

Citations39
Published2022
Admission routes1
Has abstractyes

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Same topicImpact of Technology on AdolescentsFrench-language works237,207