Bibliographic record
Abstract
Plant-based dietary patterns (vegan and vegetarian) are often considered ‘healthy’ and have been associated with broad health benefits, including decreased risk of obesity and ill health (cardiovascular disease, blood glucose and type II diabetes). However, the association between plant-based diets and mood disorders such as depression remains largely equivocal. This cross-sectional study of 219 adults aged 18–44 (M=31.22, SD=7.40) explored the associations between an estimate of overall plant-based diet quality and depression in vegans (n=165) and vegetarians (n=54). Overall plant-based diet quality was associated with depressive symptoms in vegans and vegetarians F(1, 215)=13.71, p<0.001 accounting for 6% of the variation in depressive symptoms. For those without depression, higher diet quality was protective against depressive symptoms F(1, 125)=6.49, p=0.012. Conversely, for those with depression no association with diet quality was found F(1, 89)=0.01, p=0.963. These findings suggest that a high-quality plant-based diet may be protective against depressive symptoms in vegans and vegetarians. In line with emerging research between food and mental health, higher-quality dietary patterns are associated with a reduced risk of depressive symptoms. Given the rapidly increasing rate of vegan and vegetarian food products within Australia, understanding the potential mechanisms of effects through which a plant-based diet may influence depressive symptoms is required.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.006 |
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".