The retail food environment and the cardiometabolic health of Canadians
Bibliographic record
Abstract
Diet is among the most important modifiable risk factors for morbidity and mortality worldwide, primarily through the development of cardiometabolic disease.Most Canadians have a suboptimal diet, which poses a considerable health burden on the population, given the importance of dietary quality to disease prevention and management.Poor diet at the populationlevel likely reflects responses to the food environment that individuals are exposed to.The retail food environment is a component of the built environment that can be modified to contribute to improvements in the health of Canadians at the population level.In Canada, neighbourhoods with easy access to less healthy food retailers or limited access to healthy food retailers have been associated with poorer diets, higher body mass index (BMI) and higher risks of markers of cardiometabolic disease.There is no "gold standard" dataset of businesses that researchers have identified to calculate food environment access measures.Measurement error in food environments and either diet or health outcomes have generated inconsistent findings in the body of scientific work to date.l'échantillon complet (OR = 1,17, IC 95 % 1,05 à 1,31) et dans les modèles spécifiques au sexe (femmes : OR = 1,14, IC 95 % 1,01 à 1,29 ; et hommes : OR = 1,21, IC 95 % 1,03 à 1,43) compte tenu d'une gamme de covariables.Les caractéristiques des environnements de vente au détail d'aliments variaient considérablement dans cinq quartiers de Montréal.Toutefois, aucune tendance claire n'a été observée dans les caractéristiques de l'environnement alimentaire en fonction de la marginalisation du quartier.J'ai également montré qu'il y avait généralement peu de changements dans le nombre et la proportion de points de vente d'aliments dans chaque quartier au cours des trois années, malgré les restrictions et les changements dans la mobilité individuelle dus à la pandémie de COVID-19.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".