Are French Canadians able to accurately self-rate the quality of their diet? Insights from the PREDISE study
Bibliographic record
Abstract
The main objective of this study was to compare self-rated diet quality with a more comprehensive score of diet quality and to assess the ability of self-rated diet quality to predict adherence to healthy eating guidelines. This study also aimed to evaluate the influence of individual characteristics on the association between self-rated diet quality and the overall diet quality score. As part of the PRédicteurs Individuels, Sociaux et Environnementaux (PREDISE) study, 1045 participants (51% women) from the Province of Québec, Canada, self-rated their diet quality ("In general, would you say that your dietary habits are excellent, very good, good, fair, or poor?"). Three Web-based 24-h food recalls were completed, generating data for the calculation of the Canadian Healthy Eating Index (C-HEI) score, an overall diet quality indicator. Participants rated their diet quality as excellent (2.4%), very good (22.7%), good (49.5%), fair (20.3%), or poor (5.1%). C-HEI scores differed significantly between diet ratings, in the expected direction (p < 0.0001). Self-rated diet quality predicted adherence to healthy eating guidelines (i.e., C-HEI > 68) with a sensitivity of 44.5% and a specificity of 81.5% (C-statistic = 0.63). Sex significantly modified the association between self-rated diet quality and C-HEI score (p interaction = 0.0131); women had higher C-HEI scores than did men in the "good" and "fair" ratings. Self-rated diet quality can be useful in obtaining an overview of the diet quality of a population, but the results of this study suggest that such data should be used with caution given their poor ability to predict adherence to healthy eating guidelines. Individual characteristics may influence one's ability to appropriately self-evaluate diet quality.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".