Comparing three summary indices to assess diet quality of Canadian children: a call for consensus
Bibliographic record
Abstract
Purpose: The Healthy Eating Index-Canada 2015 (HEI-C 2015), Diet Quality Index-International (DQI-I), and Healthy Eating Food Index 2019 (HEFI-2019) are commonly used to summarize the quality of Canadian diets. This paper sought to compare these three diet quality indices with respect to their ability to capture diets of different quality in Canadian children and to discriminate between population subgroups. Methods: = 909). Diet quality was assessed using HEI-C 2015, DQI-I and HEFI-2019. Agreement between the three indices was assessed using weighted Cohen's kappa. Univariate and multivariable linear regression models assessed diet quality according to student's sex, grade level, school material/social deprivation, and geographic region. Results: HEFI-2019 scores had the widest range, while DQI-I had the smallest. Agreement was 0.55 between HEI-C 2015 and DQI-I, 0.38 between HEI-C 2015 and HEFI-2019, and 0.29 between DQI-I and HEFI-2019. Boys and students from materially deprived areas reported diets of lower quality, irrespective of the index. There were no differences in diet quality across grade levels and geographic region. Energy consumption was associated positively with DQI-I and negatively HEFI-2019 scores. Conclusion: The three indices demonstrated fair to moderate agreement and varying ability to discriminate diet quality between different population subgroups of Canadian children. This study shows that the choice of a diet quality index affects the interpretation of results and practical considerations, yielding different conclusions with respect to the determinants of children's diet quality. Seeking consensus on which diet quality index to use for research, policy and/or practice would help support dietary research and policy development, and promote dietary guidelines implementation.
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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 | 0.150 | 0.211 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.014 | 0.006 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".