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Record W2155643491 · doi:10.21149/spm.v55i5.7248

Screen time in Mexican children: findings from the 2012 National Health and Nutrition Survey (ENSANUT 2012)

2013· article· en· W2155643491 on OpenAlexaff
Ian Janssen, Catalina Medina, Andrea Pedroza-Tobías, Sı́món Barquera

Bibliographic record

VenueSalud Pública de México · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsQueen's University
Fundersnot available
KeywordsScreen timeSocioeconomic statusMedicineGuidelineDemographyEnvironmental healthPediatricsPhysical activityPopulationPhysical therapy

Abstract

fetched live from OpenAlex

OBJECTIVE: To provide descriptive information on the screen time levels of Mexican children. MATERIALS AND METHODS: 5 660 children aged 10-18 years from the 2012 National Health and Nutrition Survey (ENSANUT 2012) were studied. Screen time (watching television, movies, playing video games and using a computer) was self-reported. RESULTS: On average, children engaged in 3 hours/day of screen time, irrespective of gender and age. Screen time was higher in obese children, children from the northern and Federal District regions of the country, children living in urban areas, and children in the highest socioeconomic status and education categories. Approximately 33% of 10-14 year olds and 36% of 15-18 year olds met the screen time guideline of ≤ 2 hours/day. CONCLUSIONS: 10-18 year old Mexican children accumulate an average of 3 hours/day of screen time. Two thirds of Mexican children exceed the recommended maximal level of time for this activity.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.062
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.023
GPT teacher head0.283
Teacher spread0.259 · 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

Citations24
Published2013
Admission routes1
Has abstractyes

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