Development of the Healthy Eating Food Index (HEFI)-2019 measuring adherence to Canada’s Food Guide 2019 recommendations on healthy food choices
Bibliographic record
Abstract
The release of Canada’s Food Guide (CFG) in 2019 by Health Canada prompted the development of indices to measure adherence to these updated dietary recommendations for Canadians. This study describes the development and scoring standards of the Healthy Eating Food Index (HEFI-2019), which is intended to measure alignment of eating patterns with CFG-2019 recommendations on food choices among Canadians aged 2 years and older. Alignment with the intent of each key recommendation in the CFG-2019 was the primary principle guiding the development of the HEFI-2019. Additional considerations included previously published indices, data on Canadians’ dietary intakes from the 2015 Canadian Community Health Survey-Nutrition, and expert judgement. The HEFI-2019 includes 10 components: Vegetables and fruits (20 points), Whole-grain foods (5 points), Grain foods ratio (5 points), Protein foods (5 points), Plant-based protein foods (5 points), Beverages (10 points), Fatty acids ratio (5 points), Saturated fats (5 points), Free sugars (10 points), and Sodium (10 points). All components are expressed as ratios (e.g., proportions of total foods, total beverages, or total energy). The HEFI-2019 score has a maximum of 80 points. Potential uses of the HEFI-2019 include research as well as monitoring and surveillance of food choices in population-based surveys. Novelty: The Healthy Eating Food Index-2019 was developed to measure adherence to the 2019 Canada's Food Guide recommendations on healthy food choices. The HEFI-2019 includes 10 components, of which 5 are based on foods, 1 on beverages and 4 on nutrients, for a total of 80 points.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".