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Record W4220932930 · doi:10.1002/icd.2313

Describing correlates of early childhood screen time and outdoor time in Soweto, South Africa

2022· article· en· W4220932930 on OpenAlexfundno aff
Alessandra Prioreschi, Shane A. Norris

Bibliographic record

VenueInfant and Child Development · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchSouth African Medical Research Council
KeywordsScreen timePsychological interventionSocioeconomic statusPsychologyOutdoor activityPopulationDemographyMultilevel modelEnvironmental healthDevelopmental psychologyGeographyMedicinePhysical activitySociologyStatistics

Abstract

fetched live from OpenAlex

Abstract Background Contextual factors are likely to influence whether young children are able to adhere to recommended health behaviours. This study aimed to: (1) describe the social and environmental characteristics of children under five living in Soweto, South Africa; and (2) determine factors associated with screen time and outdoor play in this population. Methods Household surveys were conducted in Soweto to collect data on children's screen time and access to outdoor space, as well as information about the household. A multilevel regression analysis was conducted for each outcome. Results Data on 2309 children aged five or under were included in this analysis. Children used screens for an average of one and a half hours per day during the week, and nearly 2 hours per day on weekends. Almost all (92%) children had a safe space to play inside, while just over a third (34%) had a safe space to play outside. A higher socioeconomic status was associated with less time spent playing outside and more screen time. Conclusion Interventions promoting outdoor play and restricting screen time are essential for improving health trajectories, but need to address structural barriers that exist, in order to protect the safety of children while promoting health behaviours.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.450
Threshold uncertainty score0.673

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.015
GPT teacher head0.209
Teacher spread0.194 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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