Associations Between Nutrition Knowledge and Overall Diet Quality: The Moderating Role of Sociodemographic Characteristics—Results From the PREDISE Study
Bibliographic record
Abstract
Purpose: To assess how nutrition knowledge is associated with global diet quality and to investigate whether sociodemographic characteristics (ie, sex, age, education, income, marital status, and living with children or not) moderate this association. Design: Cross-sectional web-based study. Participants: The PREDISE study aims at identifying correlates of adherence to healthy eating guidelines in French-speaking adults from the Province of Quebec, Canada. Subjects: A probability sample of 1092 participants (50% female). Measures: The Nutrition Knowledge Questionnaire and 24-hour food recalls from which the Canadian Healthy Eating Index (C-HEI) was calculated. Analysis: Multiple linear regressions performed to assess how nutrition knowledge is associated with the C-HEI. Interaction terms tested to evaluate whether sociodemographic characteristics moderate the association between nutrition knowledge and the C-HEI. Results: Nutrition knowledge (B = 0.141 [95% CI: 0.075-0.208], P < .0001) was identified as a significant correlate of the C-HEI. Education significantly moderated the association between nutrition knowledge and the C-HEI ( P interaction = .0038), with a significative association among participants with a lower education level (B = 0.295 [95% CI: 0.170-0.421], P < .0001) but not among participants with a higher education level (B = 0.077 [95% CI: −0.004 to 0.157], P = .06). Whether participants lived with or without children also significantly moderated the association ( P interaction = 0.0043); nutrition knowledge was associated with the C-HEI only in participants who were not living with children (B = 0.261 [95% CI: 0.167 to 0.355], P < .0001). Conclusion: This study suggests that the association between nutrition knowledge and adherence to healthy eating guidelines is not the same in different subgroups of the population. Interventions aiming at increasing nutrition knowledge may be a promising approach to improve diet quality, especially among individuals with a lower education.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".