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Record W4376645325 · doi:10.1093/pch/pxac125

Screen time and preschool children: Promoting health and development in a digital world

2023· review· en· W4376645325 on OpenAlexafffund
Michelle Ponti

Bibliographic record

VenuePaediatrics & Child Health · 2023
Typereview
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsCanadian Paediatric Society
FundersMemorial University of Newfoundland
KeywordsScreen timeEarly childhoodPsychosocialPsychologyChild developmentBest practiceDevelopmentally Appropriate PracticeHealth carePandemicMedicineHealth professionalsCoronavirus disease 2019 (COVID-19)Developmental psychologyMedical educationNursingPhysical activityEarly childhood educationPsychiatryPolitical science

Abstract

fetched live from OpenAlex

COVID-19 transformed the family media environment and spurred research on the effects of screen media exposure and use on young children. This update of a 2017 CPS statement re-examines the potential benefits and risks of screen media in children younger than 5 years, with focus on developmental, psychosocial, and physical health. Four evidence-based principles-minimizing, mitigating, mindfully using, and modelling healthy use of screens-continue to guide children's early experience with a rapidly changing media landscape. Knowing how young children learn and develop informs best practice for health care providers and early years professionals (e.g., early childhood educators, child care providers). Anticipatory guidance should now include child and family screen use in (and beyond) pandemic conditions.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.038
GPT teacher head0.329
Teacher spread0.291 · 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 designNot applicable
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

Citations139
Published2023
Admission routes2
Has abstractyes

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