Exploring the Intersection Between Diet and Self-Identity: A Cross-Sectional Study With Australian Adults
Bibliographic record
Abstract
Objective Diet-related self-identity, which includes components such as individuals' overall dietary pattern and food choice motivations, is a strong predictor of health behaviors. This study sought to assess the variation in dietary patterns reported by a sample of Australian adults and their associations with diet quality. Design Cross-sectional survey. Participants Australian adults (n = 2,010) Variables measured The main outcome measure was diet quality relative to the Australian Dietary Guidelines, measured by the Healthy Diet Score survey. Other outcomes captured included dietary patterns (eg, unrestricted, vegetarian, flexitarian, or ketogenic diets), diet-related self-identity constructs (centrality, prosocial motivation, personal motivation, and strictness), and sociodemographic characteristics (eg, age, sex, and education level). Analysis Data were analyzed descriptively, and ordinary least squares regression was performed to identify significant predictors of diet quality. Results Eighteen unique dietary patterns were reported. These were classified into 3 categories on the basis of the degree of restriction of core food groups. Diets based on restriction of animal protein were associated with the highest diet quality, including the highest consumption of fruits, vegetables, and whole grains, whereas restriction of other foods was associated with the poorest diet quality. Unrestricted diets reported the highest consumption of discretionary food (high in saturated fat, salt, or added sugar). Finally, the regression analysis found that diet quality was significantly predicted by dietary pattern and diet-related self-identity constructs ( F [8, 1974] = 54.952; P < 0.0001; adjusted R 2 = 0.179). Conclusions and Implications Dietary pattern and diet-related self-identity constructs are key determinants of diet quality. This has implications for future interventions, including that programs and messages could be tailored to ensure they align with the target population's self-identity and overall dietary patterns.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".