The relationship between health eating and overweight/obesity in Canada: cross‐sectional study using the CCHS
Bibliographic record
Abstract
Objective: The relationship between Canada's Food Guide (CFG) adherence and overweight/obesity at the population level is unknown. Our objective was to explore the association between overweight/obesity and CFG adherence in Canada. Methods: Using 24-h dietary recall data from the Canadian Community Health Survey (CCHS), we conducted a cross-sectional analysis of Canadians' consumption of four predefined food types from CFG (grain products, vegetables and fruit, milk and alternatives, meat and alternatives). Respondents aged 18 to 65 years with measured BMI were included. The total number of servings in each food group was compared with the number of recommended servings in CFG to determine adherence. Linear regression was used to explore the association between overweight/obesity and CFG adherence. Results: Participants who met the minimum servings in vegetables and fruit had a lower measured BMI. Also, participants who met the minimum servings in meat and alternatives had a higher measured BMI. These associations were observed for the sample as a whole and for those with overweight/obesity, and, for meat and alternatives, among women. Conclusion: There is evidence that following the CFG recommendation is associated with measured BMI, for some food groups. This relationship needs to be validated using longitudinal data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".