Development and evaluation of a food choices assessment score (FCAS) measuring the healthfulness of dietary choices according to 2019 Canada's Food Guide/Canada's Dietary Guidelines, using the Canadian Health Measures Survey food frequency questionnaire
Bibliographic record
Abstract
The objective of this study was to develop and evaluate a food choices assessment score (FCAS) measuring alignment with 2019 Canada's Food Guide (CFG) and Canada's Dietary Guidelines (CDG) using a non-quantitative food frequency questionnaire (FFQ) data. Cross-sectional data from the Canadian Health Measures Survey (2016–2019), including 6459 participants (≥19 years) and a non-quantitative FFQ (∼100 food items) were used. Content and construct validity and assessing reliability were used to evaluate the FCAS, including a comparison of mean FCAS among Canadian subgroups, calculating the FCAS for high quality diet menus, investigating the consistency of the FCAS with the Dietary Approaches to Stop Hypertension (DASH), as a healthy diet linked with lower cardiometabolic risks, and estimating Cronbach's alpha for reliability. The FCAS consisted of nine components for a total of 80 points. The FCAS captured the key recommendations of the 2019 CFG/CDG. Mean (SE) FCAS of the adult Canadian population was 29.3 (0.4) (/80) and was higher in females 32.2 (0.4) and non-smokers 30.3 (0.3) compared to males 26.7 (0.4) and smokers 23.6 (0.9), respectively ( p < 0.0001). FCAS yielded high scores for healthy menu samples of CDG (80/80) and DASH (70/80) diets. FCAS was correlated with DASH diet score ( r = 0.83). Cronbach's alpha was found to be moderate (0.5), as expected, which confirmed the multidimensionality of the FCAS components in reflecting different characteristics of diet quality. These analyses suggest adequate validity with multidimensional consistency of the 2019 CFG/CDG FCAS as a new tool for use with non-quantitative FFQ data.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".