Associations among eating behaviour traits, diet quality and food labelling: a mediation model
Bibliographic record
Abstract
OBJECTIVE: To assess the associations among eating behaviour traits, food label use and diet quality and to evaluate if the association between eating behaviour traits and diet quality is mediated by food label use. DESIGN: Eating behaviour traits were assessed using the Three-Factor Eating Questionnaire (TFEQ), the Restraint Scale and the Intuitive Eating Scale, whereas food label use was measured with the Label Reading Survey. Diet quality (Canadian Healthy Eating Index) was assessed with an FFQ. SETTING: Cross-sectional study. PARTICIPANTS: Adults (n 385; mean (sd): BMI = 26·0 (4·9) kg/m2, age = 41·1 (15·0) years) involved in two previous experimental studies. RESULTS: When controlling for potential covariates, general food label use (β = 1·18 (se 0·26), P < 0·0001) was the main determinant of diet quality, explaining 6·7 % of its variance. General food label use partly mediated the association between TFEQ-cognitive restraint and diet quality; the indirect effect (βindirect (se); 95 % CI) was stronger in men (0·32 (0·10); 0·15, 0·55) than women (0·16 (0·05); 0·08, 0·27). General food label use also partly mediated the negative association between unconditional permission to eat and diet quality; the indirect effect (βindirect (se); 95 % CI) was also stronger in men (-1·88 (0·55); -3·11, -0·96) than women (-1·03 (0·33); -1·81, -0·49). CONCLUSIONS: General food label use was the main determinant of diet quality and partly mediated the association between eating behaviour traits and diet quality. The stronger mediating effect observed in men suggests they rely more on food labelling when attempting to restrained themselves, which translates into better 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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".