The local restaurant environment in relation to eating out and sugary drink intake among Canadian children and youth
Bibliographic record
Abstract
Background: Accessibility of food retail in communities may play a role in shaping the food choices of local residents. However, previous studies have shown mixed results. This study examined associations between the local restaurant environment and the frequency of eating food from restaurants and intake of sugary drinks among Canadian children and youth. Data and methods: The study cohort consisted of 23,776 participants (aged 1 to 17 years) in the 2019 Canadian Health Survey on Children and Youth who resided in large urban population centres across the Canadian provinces. Measures of geographic access to various restaurant types within walking distance of participants' residential areas came from the 2018 Canadian Food Environment Dataset. Poisson regression models with robust standard errors assessed associations between measures of absolute densities (number per km²) of full-service, fast-food and other restaurants, and the relative density of fast-food restaurants (as a percentage of total restaurants) with the frequency of eating food from fast-food or full-service restaurants and sugary drink intake in the previous seven days. Results: After adjustment for a range of sociodemographic covariates, there were no consistent associations between absolute and relative measures of restaurant access and the frequency of eating food from restaurants or intake of sugary drinks. Interpretation: Results reveal no consistent relationships between local restaurant exposures and the frequency of eating food from restaurants or sugary drink intake among Canadian children and youth. Efforts to create environments that foster healthy food choices among young people will remain important but will likely need to target multiple activity spaces beyond the local neighbourhood.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".