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Record W7079586510 · doi:10.26108/8c7q-0p08

Screen-based media use, physical activity level and body mass index of children and youth in Nova Scotia

2006· article· en· W7079586510 on OpenAlexaboutno aff

Bibliographic record

VenueAcadiaU-DEV · 2006
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsnot available
Fundersnot available
KeywordsOverweightNova scotiaPhysical activityBody mass indexObesityLeisure timePopulationMass media

Abstract

fetched live from OpenAlex

There is an increasing trend towards overweight and obesity in children and youth in North America. This trend has typically been associated with poor nutritional practices and reduced physical activity. Certain leisure time activities such as the number of hours of television viewing, playing video games and internet/computer use have also been implicated. Objective: The goal of this project was to assess the relationships between objectively measured physical activity, BMI, and screen-based media use. Participants: We studied a large, random sample of male and female grade 3, 7 and 11 (n=1643) students in the province of Nova Scotia. Design/Setting: A child/youth questionnaire was administered to gather information on media use of participants. Height and weight were recorded for each participant and an accelerometer was worn for 7 consecutive days. Results: Physical activity to achieve health benefits (defined as 60 minutes or more of moderate to vigorous physical activity 5 or more days per week) in this population decreased significantly between grades 3 and 7 and females were particularly at risk. The use of media increased with age but did not always relate significantly to physical activity levels or BMI in this population. Conclusion: Leisure time pursuits including television viewing and screen-based media use in general, do not seem to have the direct relationships with physical inactivity and increasing BMI that have previously been reported.

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)0.000
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 score0.017
Threshold uncertainty score0.601

Codex and Gemma teacher scores by category

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

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 head0.026
GPT teacher head0.230
Teacher spread0.204 · 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

Citations0
Published2006
Admission routes1
Has abstractyes

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