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Record W2339179101 · doi:10.22374/cjgim.v8i3.67

Restricting Marketing of Unhealthy Foods: Should General Internists Engage?

2013· article· en· W2339179101 on OpenAlexaffvenueabout
Norm R.C. Campbell

Bibliographic record

VenueCanadian Journal of General Internal Medicine · 2013
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsLibin Cardiovascular Institute of AlbertaUniversity of Calgary
Fundersnot available
KeywordsMedicineGovernment (linguistics)Environmental healthConsumption (sociology)ObesityUnhealthy foodPopulationFood marketingMarketingBusiness

Abstract

fetched live from OpenAlex

Unhealthy diet is the leading risk for death, years of life lost, and disability, causing an estimated 65,722 deaths and 864,032 life years lost in Canada in 2010. 1 Although the causes of unhealthy diet are complex, unhealthy eating habits start early in life, and unhealthy food and beverage marketing to children is consistently associated with unhealthy dietary behaviours and childhood obesity. Although there have been recommendations from the World Health Organization (WHO) and the United Nations urging countries to restrict such marketing to children as a population strategy to improve diet, the food industry continues to direct millions of marketing dollars to increase the sales and consumption of the very foods that contribute to disease burden. While many countries have heeded the WHO recommendations, in Canada, outside of Quebec, the food industry largely self-regulates its marketing of unhealthy food to children, with no government monitoring or oversight. The result is that Canadian children are extensively exposed to marketing of unhealthy food products that would not be allowed in several other countries.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.190
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 integrity0.0000.001
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 head0.046
GPT teacher head0.313
Teacher spread0.267 · 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.

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

Citations2
Published2013
Admission routes3
Has abstractyes

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