Development of the Canadian Eating Practices Screener to assess eating practices based on 2019 Canada's Food Guide recommendations
Bibliographic record
Abstract
In 2019, Health Canada released a new iteration of Canada's Food Guide (2019-CFG), which, for the first time, highlighted recommendations regarding eating practices, i.e., guidance on where, when, why, and how to eat. The objective of this study was to develop a brief self-administered screener to assess eating practices recommended in the 2019-CFG among adults aged 18–65 years. Development of the screener items was informed by a review of existing tools and mapping of items onto 2019-CFG recommendations. Face and content validity were assessed with experts in public health nutrition and/or dietary assessment ( n = 16) and individuals from Government of Canada ( n = 14). Cognitive interviews were conducted with English-speaking ( n = 16) and French-speaking ( n = 16) adults living in Canada to assess face validity and understanding of the screener items. While some modifications were identified to improve relevance or clarity, overall, the screener items were found to be relevant, well-constructed, and clearly worded. This comprehensive process resulted in the Canadian Eating Practices Screener/Questionnaire court canadien sur les pratiques alimentaires, which includes 21 items that assess eating practices recommended in the 2019-CFG. This screener can facilitate monitoring and surveillance efforts of the 2019-CFG eating practices as well as research exploring how these practices are associated with various health outcomes.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".