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Record W2052721701 · doi:10.1186/1471-2458-13-569

A cross-sectional examination of modifiable risk factors for chronic disease among a nationally representative sample of youth: are Canadian students graduating high school with a failing grade for health?

2013· article· en· W2052721701 on OpenAlexafffundabout
Scott T. Leatherdale, Vicki Rynard

Bibliographic record

VenueBMC Public Health · 2013
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsImpactUniversity of Waterloo
FundersHealth Canada
KeywordsMedicineBiostatisticsPublic healthOverweightEnvironmental healthYouth Risk Behavior SurveyObesityEpidemiologyCross-sectional studyBinge drinkingGuidelineGerontologyHealth promotionSuicide preventionPoison control

Abstract

fetched live from OpenAlex

BACKGROUND: Substance use and weight gain among youth increase the risk for future disease. As such, the purpose of this study is to examine how many Canadian youth are currently failing to meet substance use and weight gain related public health guidelines. METHODS: Data from the 2010-11 Youth Smoking Survey were used to examine grade 9 to 12 students meeting seven different guidelines by sex and by grade. RESULTS: Among Canadian youth, 8.8% were current smokers, 18.8% were current marijuana users, 25.5% were current binge drinkers, 22.5% were considered overweight or obese, 31.2% did not meet physical activity guidelines, 89.4% exceeded sedentary behaviour guidelines, and 93.6% reported inadequate fruit and vegetable intake. The mean number of risk factors per student was 2.9 (±1.2); only 0.5% of youth reported having none of the risk factors. CONCLUSION: Students rarely met all seven public health guideline examined, and the vast majority of actually reported having two or more modifiable risk factors for disease.

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)0.002
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 score0.039
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.004
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.077
GPT teacher head0.368
Teacher spread0.292 · 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

Citations70
Published2013
Admission routes3
Has abstractyes

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