Development and implications of a revised Canadian Healthy Eating Index (HEIC-2009)
Bibliographic record
Abstract
OBJECTIVE: The purpose was to update the Healthy Eating Index-C (HEI-C) with Canada's new food guide recommendations (HEIC-2009) and compare scores and ratings among a small sample of grade 6 students. DESIGN: Updates to the HEI-C were completed with Canada's new food guide recommendations for daily number of servings. HEI-C and HEIC-2009 scores were computed for a small sample (n 405) of grade 6 students utilizing nutrition data that were collected using the Food Behaviour Questionnaire, a validated web-based dietary assessment tool (including a 24 h dietary recall, FFQ, and food and physical activity behavioural questions). SETTING: Data were collected in fifteen schools in the Region of Waterloo District School Board, Ontario, Canada. SUBJECTS: A total of 405 students (48 % males and 52 % females) from grade 6 classrooms completed the web-based survey. RESULTS: The index scores revealed that participants scored higher (74.5 v. 69.6, P < 0.001) using the HEIC-2009 compared with the HEI-C, even though both index scores are rated in the 'needs improvement' category (HEIC-2009, 75 %; HEI-C, 71 %). A small group of participants (n 14), who were previously rated (using the HEI-C) in the 'poor' category, were rated in the 'needs improvement' category using the HEIC-2009 (chi2 = 589.647, df = 4, P < 0.001). CONCLUSIONS: The HEIC-2009 has the potential to be used as a population-level diet quality index 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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 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".