Addition of food group equivalents to the Canadian Diet History Questionnaire II for the estimation of the Canadian Healthy Eating Index-2005
Bibliographic record
Abstract
INTRODUCTION: Poor diet quality has been shown to increase the risk of common chronic diseases that can negatively impact quality of life and burden the healthcare system. Canada's Food Guide evidence-based recommendations provide dietary guidance aimed at increasing diet quality. Compliance with Canada's Food Guide can be assessed with the Canadian Healthy Eating Index (C-HEI), a diet quality score. The recently designed Canadian Diet History Questionnaire II (C-DHQ II), a comprehensive food frequency questionnaire could be used to estimate the C-HEI in Canadian populations with the addition of food group equivalents (representing Canada's Food Guide servings) to the C-DHQ II nutrient database. We describe methods developed to augment the C-DHQ II nutrient database to estimate the C-HEI. METHODS: Food group equivalents were created using food and nutrient data from existing published food and nutrient databases (e.g. the Canadian Community Health Survey - Cycle 2.2 Nutrition [2004]). The variables were then added to the C-DHQ II companion nutrient database. C-HEI scores were determined and descriptive analyses conducted for participants who completed the C-DHQ II in a cross-sectional Canadian study. RESULTS: The mean (standard deviation) C-HEI score in this sample of 446 adults aged 20 to 83 was 64.4 (10.8). Women, non-smokers, and those with more than high school education had statistically significant higher C-HEI scores than men, smokers and those with high school diplomas or less. CONCLUSION: The ability to assess C-HEI using the C-DHQ II facilitates the study of diet quality and health outcomes in Canada.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".