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Record W2028498736 · doi:10.1139/h04-008

The Economic Costs Associated With Physical Inactivity and Obesity in Canada: An Update

2004· article· en· W2028498736 on OpenAlexafffundabout
Peter T. Katzmarzyk, Ian Janssen

Bibliographic record

VenueCanadian Journal of Applied Physiology · 2004
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsQueen's University
FundersNational Cancer InstituteCanadian Institutes of Health ResearchHealth CanadaObesity CanadaNational Institutes of Health
KeywordsIndirect costsObesityEconomic costEnvironmental healthPublic healthHealth careMedicineCost–benefit analysisBusinessEconomicsEconomic growthPolitical science

Abstract

fetched live from OpenAlex

The purpose of this analytical review was to estimate the direct and indirect economic costs of physical inactivity and obesity in Canada in 2001. The relative risks of diseases associated with physical inactivity and obesity were determined from a meta-analysis of existing prospective studies and applied to the health care costs of these diseases in Canada. Estimates were derived for both the direct health care expenditures and the indirect costs, which included the value of economic output lost because of illness, injury-related work disability, or premature death. The economic burden of physical inactivity was $5.3 billion ($1.6 billion in direct costs and $3.7 billion in indirect costs) while the cost associated with obesity was $4.3 billion ($1.6 billion of direct costs and $2.7 billion of indirect costs). The total economic costs of physical inactivity and obesity represented 2.6% and 2.2%, respectively, of the total health care costs in Canada. The results underscore the importance of public health efforts aimed at combating the current epidemics of physical inactivity and obesity in Canada.

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)0.003
metaresearch head score (Gemma)0.010
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.041
Threshold uncertainty score0.281

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0090.021
Science and technology studies0.0010.001
Scholarly communication0.0040.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.006
GPT teacher head0.207
Teacher spread0.201 · 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

Citations650
Published2004
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Applied PhysiologySame topicObesity, Physical Activity, DietFrench-language works237,207