Comparison of methodological quality between the 2007 and 2019 Canadian dietary guidelines
Bibliographic record
Abstract
OBJECTIVE: With significant shifts in the dietary recommendations between the 2007 and 2019 Canadian dietary guidelines, such as promoting plant-based food intake, reducing highly processed food intake and advocating the practice of food skills, we compared their differences in guideline development methods. DESIGN: Two reviewers used twenty-five guided criteria to appraise the methods used to develop the most recent dietary guidelines against those outlined in the 2014 WHO Handbook for Guideline Development. SETTING: Canada. PARTICIPANTS: 2007 and 2019 dietary guidelines. RESULTS: We found that the 2019 guidelines were more evidence-based and met 80 % (20/25) of the WHO criteria. For example, systematic reviews and health organisation authoritative reports, but not industry reports, constituted the evidence base for the dietary recommendations. However, recommendations on food sustainability and food skill practice were driven primarily by stakeholders' interests. By contrast, less information was recorded about the process used to develop the 2007 guidelines, resulting in 24 % (6/25) consistency with the WHO standards. CONCLUSIONS: Our analysis suggests that a more transparent and evidence-based approach is used to develop the 2019 Canadian dietary guidelines and that method criteria should support further incorporation of nutrition priorities (food sustainability and food skills) in future dietary guideline development.
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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 | 0.404 | 0.737 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.013 |
| Bibliometrics | 0.046 | 0.052 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.009 | 0.008 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".