Evaluation of the Healthy Eating Food Index (HEFI)-2019 measuring adherence to Canada’s Food Guide 2019 recommendations on healthy food choices
Bibliographic record
Abstract
The objective of this study was to evaluate the construct validity and reliability of the Healthy Eating Food Index-2019 (HEFI-2019), which was developed to measure adherence to Canada’s Food Guide 2019 (CFG-2019) recommendations on healthy food choices. Dietary intake data from 24-hour dietary recalls in the 2015 Canadian Community Health Survey-Nutrition were used for that purpose. Multidimensionality was examined using principal component analysis. Mean scores were compared among subgroups of the population. The association between scores and energy intake was assessed using Pearson correlations. Cronbach’s alpha was calculated to assess reliability. The estimated mean HEFI-2019 score (/80) was 43.1 (95% CI, 42.7 to 43.6) among Canadians aged 2 years and older. The first and 99th percentiles were 22.1 and 62.9 points. The mean HEFI-2019 score for smokers was 7.2 points lower than for non-smokers (95% CI, –8.5 to –5.9). The HEFI-2019 was weakly correlated with energy intake (r = –0.13; 95% CI, –0.20 to –0.06). The principal components analysis revealed at least 4 dimensions. Cronbach’s alpha was 0.66 (95% CI, 0.63 to 0.69). Evidence of construct validity and internal consistency support the use of the HEFI-2019 to assess adherence to CFG-2019’s recommendations on healthy food choices. Novelty: Examination of the HEFI-2019’s psychometric properties is needed prior to implementation. Analyses support the construct validity and internal consistency of the HEFI-2019. Interpretation of the total HEFI-2019 score must be accompanied by its components’ scores, considering it assesses multiple dimensions of food choices.
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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 | 0.001 | 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".