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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".