Are the 2019 Canada’s Food Guide Recommendations on Healthy Food Choices Consistent with the EAT-Lancet Reference Diet from Sustainable Food Systems?
Bibliographic record
Abstract
BACKGROUND: The diet proposed by the EAT-Lancet Commission, which supports both health and environmental sustainability, provides an opportunity to assess the sustainability of food-based dietary guidelines. OBJECTIVES: The primary objective was to assess the alignment of the 2019 Canada's Food Guide (CFG) with the EAT-Lancet diet. To do so, an index assessing adherence to the EAT-Lancet diet was developed and evaluated. METHODS: Data from 1147 adults were used from the cross-sectional PRÉDicteurs Individuals, Sociaux et Environnementaux (PREDISE) study conducted between 2015 and 2017 in the province of Québec. The EAT-Lancet Dietary Index (EAT-I) was developed to evaluate adherence to the EAT-Lancet diet. Adherence to the 2019 CFG was assessed using the Healthy Eating Food Index (HEFI)-2019. Associations between the HEFI-2019 score and component scores and the EAT-I score were examined using linear regression models. RESULTS: The mean EAT-I score (/80) in this population was 33.4 points [95% confidence interval (CI): 32.2, 34.6]. EAT-I scores were consistent with expected differences in diet quality between females and males (+6.9 points, 95% CI: 4.8, 9.0) and between adults aged 50-65 y and 18-34 y (+4.3 points, 95% CI: 1.6, 7.0). The mean HEFI-2019 (/80) score was 44.9 points (95% CI: 44.1, 45.7). The HEFI-2019 was strongly associated with the EAT-I (ß = 0.76, 95% CI: 0.72, 0.80). Among the 10 components of the HEFI-2019, components such as the whole-grain foods (ß =4.01, 95% CI: 3.49, 4.52), grain foods ratio (ß =3.65, 95% CI: 3.24, 4.07), plant-based protein foods (ß =2.41, 95% CI: 2.03, 2.78), and fatty acids ratio (ß =3.12, 95% CI: 2.72, 3.51) showed the strongest associations with the EAT-I. CONCLUSIONS: These results suggest that recommendations in the 2019 CFG are largely coherent with the EAT-Lancet diet underscoring the complementarity and compatibility of the 2019 CFG for sustainability and health promotion purposes.
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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 | 0.012 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".