Development of a Healthy Eating Pattern for the Revision of Canada's Food Guide
Bibliographic record
Abstract
Health Canada develops evidence‐based guidance on healthy eating. Canada's Food Guide is currently under revision and Health Canada is exploring an expanded food pattern modelling methodology to develop a revised healthy eating pattern. The objective of this presentation is to share Health Canada's draft modelling methodology. Food pattern modelling is the process of adjusting daily amounts of foods from different categories to meet specific criteria, such as meeting nutrient intake goals, limiting nutrients of concern or ensuring inclusion of foods associated with positive health outcomes. Food pattern modelling was used to develop the healthy eating pattern in the previous (2007) version of Canada's Food Guide and will be used again during this revision. To help inform the draft methodology, a literature review was conducted to describe and compare statistical modelling methods used internationally in the development of dietary patterns, primarily since the 2007 Canadian food guide was released. Results of the review indicate that while different countries used various approaches, many similarities were found in terms of the overall steps taken to develop recommended dietary patterns. Differences included how energy levels were considered, whether or not to include guidance on discretionary calories, and modelling of additional patterns to highlight certain food groups that reduce chronic disease risks. The review identified techniques and ideas on how Canada's modeling approach could be expanded and improved. This information was integrated in the proposed modelling methodology that Health Canada is putting forward. This presentation will highlight the key steps and important considerations while developing the revised healthy eating pattern. This session will give attendees the opportunity to ask questions and provide feed‐back on the proposed modeling approach.
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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 | 0.001 | 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 | 0.002 | 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".