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Record W3125010608

Factors Influencing the Body Mass Index of Adults in Canada

2007· preprint· en· W3125010608 on OpenAlexaboutno aff
John Cranfield

Bibliographic record

VenueRePEc: Research Papers in Economics · 2007
Typepreprint
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsnot available
Fundersnot available
KeywordsBody mass indexDemographyAlcohol consumptionMedicineIndex (typography)GerontologyAlcoholEndocrinologySociologyBiology
DOInot available

Abstract

fetched live from OpenAlex

This paper explores socio-demographic, economic and behaviour factors influencing body mass index (BMI) amongst 20 to 64 year old adults in Canada. BMI scores in Canada have increased, with gains stemming from disproportionate increases in female BMI. Econometric results indicate higher BMI scores for males, those born in Canada, those in food insecure homes and whites. Age-gender interactions suggest different patterns of BMI adjustment over the life of males and females; a pronounced inverse quadratic relationship between with age and male BMI is noted, while female BMI increases with age. Education, used as a gauge of inequality, is inversely related to BMI, while income has a muted effect. BMI is inversely related to level of physical activity, an effect which is more pronounced for females in Canada. BMI has an inverse quadratic relationship with smoking behaviour, with higher BMI amongst former smokers than daily, occasional and non-smokers. BMI appears to be inversely related to intensity of alcohol consumption.

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)0.002
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 score0.012
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.027
GPT teacher head0.306
Teacher spread0.279 · 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

Citations1
Published2007
Admission routes1
Has abstractyes

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Same venueRePEc: Research Papers in Economics→Same topicObesity, Physical Activity, Diet→French-language works237,207→