Developing an Evidence Review Cycle Model for Canadian Dietary Guidance
Bibliographic record
Abstract
Formulating dietary guidance involves navigating a large volume of substantive, conflicting evidence. Canada's guidance is determined after periodic evidence reviews. Health Canada identified the need for a more formal and systematic process to gather, assess, and analyze evidence. This led to the development of the Evidence Review Cycle model for Canada's dietary guidance. The Evidence Review Cycle consists of 5 steps that form a dynamic, iterative process to promote evidence-based, transparent, and proactive decision making. Resulting actions may include enhancing the implementation of guidance, revising guidance, or developing new guidance. Here, the development of this model is described, including considerations for implementation.
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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 | 0.137 | 0.139 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".