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Record W1982687468 · doi:10.1155/2012/342386

Adolescent Weight Status and Related Behavioural Factors: Web Survey of Physical Activity and Nutrition

2011· article· en· W1982687468 on OpenAlexafffundabout
Kate Storey, Laura E Forbes, Shawn N. Fraser, John C. Spence, Ronald C. Plotnikoff, Kim D. Raine, Linda J. McCargar

Bibliographic record

VenueJournal of Obesity · 2011
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsAthabasca UniversityUniversity of Alberta
FundersCanadian Institutes of Health ResearchInstituto DanoneAlberta Heritage Foundation for Medical ResearchDanoneFondation pour la Recherche MédicaleAlberta Innovates - Health SolutionsDanone Institute of CanadaAlberta InnovatesHeart and Stroke Foundation of Canada
KeywordsMedicinePhysical activityGerontologyWeb applicationWeb surveyWorld Wide WebPhysical therapy

Abstract

fetched live from OpenAlex

Purpose. To identify whether non-overweight students were different from their overweight or obese peers with respect to diet, suboptimal meal behaviours, and physical activity using a self-administered web-based survey. Methods. 4097 adolescents living in Alberta, Canada completed Web-SPAN (Web Survey of Physical Activity and Nutrition). Students were classified as overweight or obese, and differences were described in terms of nutrient intakes, physical activity, and meal behaviours. Results. Non-overweight students consumed significantly more carbohydrate and fibre, and significantly less fat and high calorie beverages, and had a higher frequency of consuming breakfast and snacks compared to overweight or obese students. Both non-overweight and overweight students were significantly more active than obese students. Conclusions. This research supports the need to target suboptimal behaviours such as high calorie beverage consumption, fat intake, breakfast skipping, and physical inactivity. School nutrition policies and mandatory physical education for all students may help to improve weight status in adolescents.

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 score0.022
Threshold uncertainty score0.541

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.044
GPT teacher head0.280
Teacher spread0.236 · 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

Citations17
Published2011
Admission routes3
Has abstractyes

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