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Record W1986223537 · doi:10.1080/17477160601124613

Obesity in a provincial population of Canadian preschool children: Differences between 1984 and 1997 birth cohorts

2007· article· en· W1986223537 on OpenAlexaffabout
Patricia Canning, Mary L. Courage, Lynn M. Frizzell, Tim Seifert

Bibliographic record

VenueInternational Journal of Pediatric Obesity · 2007
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsMemorial University of Newfoundland
FundersPublic Health Agency
KeywordsOverweightMedicineObesityBody mass indexDemographyPopulationChildhood obesityPsychological interventionPediatricsDisease controlEnvironmental healthGerontology

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine whether trends of increasing overweight and obesity reported for older children and adults are evident in Canadian preschoolers. METHODS: A sample of 3857 preschool-aged children (51.1% boys) in the Province of Newfoundland and Labrador, born in 1984 and measured in 1987-1989, was selected from government archival records. The sample of 4161 children (50.1% boys), born in 1997 and measured in 2000-2002, was obtained from regional health authority records. Body mass index (BMI) was calculated using heights and weights measured by nurses. Overweight and obesity prevalence was estimated according to the International Obesity Task Force (IOTF) and the Centres for Disease Control (CDC) methods. RESULTS: Combined rates of overweight and obesity were significantly higher in preschoolers born in 1997 (25.6% IOTF and 36.0% CDC) than in 1984 (16.9% IOTF and 25.1% CDC), when levels were already high. There were some differences between sexes and classification systems. CONCLUSION: The relatively rapid rise in overweight and obesity in children as young as 3.5 years, in little more than a decade, underscores the immediate need for monitoring, and implementation of effective interventions. Overweight and obesity in preschool children is not new, but has become increasingly prevalent, and requires population-based strategies.

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 score0.009
Threshold uncertainty score0.969

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.011
GPT teacher head0.258
Teacher spread0.248 · 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

Citations39
Published2007
Admission routes2
Has abstractyes

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